+ 
+
+
+ 
+
+
EzGATr (Easy Geometric Algebra Transformer) intends to be a simple-to-use and lightweight Python library for building 3D Geometric Algebra Transformers (GATr). It is a collection of operators, modules, utilities, etc. build on top of PyTorch. In addition, EzGATr also seeks to bridge the gap between the mathematical formulations and corresponding implementations through extensive documentation and explanations to facilitate learning and potential future optimizations.
+ +EzGATr is currently in development and not yet available on PyPI. To install it, you can clone the repository and install it using pip. Use the -e flag to install it in editable mode for quick changes.
$ git clone https://github.com/Guest400123064/ezgatr.git
+$ cd ezgatr
+$ pip install -e .
+The quick start example shown below demonstrates how to use EzGATr to build a equivariant network with one input/output channel (e.g., a point cloud) and four hidden channels. The mock input data contains a batch of eight samples, each with 256 3D objects embedded as multi-vectors.
+ +import torch
+import torch.nn as nn
+
+from ezgatr.nn import EquiLinear
+from ezgatr.nn.functional import scaler_gated_gelu
+
+
+class SimpleNet(nn.Module):
+ def __init__(self):
+ super(SimpleNet, self).__init__()
+ self.fc1 = EquiLinear(1, 4)
+ self.fc2 = EquiLinear(4, 1)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = scaler_gated_gelu(x)
+ x = self.fc2(x)
+ return x
+
+
+net = SimpleNet()
+in_ = torch.randn(8, 256, 1, 16)
+out = net(in_)
+One can refer to this example for how to build a full-fledged GATr model with EzGATr, involving equivariant geometric attention and geometric MLP.
+ +The complete API references for EzGATr can be found here. TL;DR, the package is organized as follows:
+ +ezgatr.nn: Contains the core modules and layers for building GATr models. It is organized similarly to PyTorch's torch.nn package, where the functional submodule contains lower-level operators and transformations.ezgatr.interfaces: Contains the utility functions that help encode and decode 3D objects to and from multi-vectors.ezgatr.nets: Contains off-of-the-shelf networks built with EzGATr building blocks. It can also be used as references for building custom networks with EzGATr.EzGATr is distributed under the terms of the MIT license.
+
diff --git a/docs/ezgatr/interfaces/plane.html b/docs/ezgatr/interfaces/plane.html
index 8054b51..8cb01c4 100644
--- a/docs/ezgatr/interfaces/plane.html
+++ b/docs/ezgatr/interfaces/plane.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/point.html b/docs/ezgatr/interfaces/point.html
index efc430e..d0499c0 100644
--- a/docs/ezgatr/interfaces/point.html
+++ b/docs/ezgatr/interfaces/point.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/pseudoscalar.html b/docs/ezgatr/interfaces/pseudoscalar.html
index 6aa1cfe..d3da9bf 100644
--- a/docs/ezgatr/interfaces/pseudoscalar.html
+++ b/docs/ezgatr/interfaces/pseudoscalar.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/reflection.html b/docs/ezgatr/interfaces/reflection.html
index 0053d01..37277c1 100644
--- a/docs/ezgatr/interfaces/reflection.html
+++ b/docs/ezgatr/interfaces/reflection.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/rotation.html b/docs/ezgatr/interfaces/rotation.html
index 2f66427..33092f3 100644
--- a/docs/ezgatr/interfaces/rotation.html
+++ b/docs/ezgatr/interfaces/rotation.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/scalar.html b/docs/ezgatr/interfaces/scalar.html
index 4eb16bc..bdf2e60 100644
--- a/docs/ezgatr/interfaces/scalar.html
+++ b/docs/ezgatr/interfaces/scalar.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/interfaces/translation.html b/docs/ezgatr/interfaces/translation.html
index 24b49a9..622345e 100644
--- a/docs/ezgatr/interfaces/translation.html
+++ b/docs/ezgatr/interfaces/translation.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/nets.html b/docs/ezgatr/nets.html
index 6ed10c1..75300bf 100644
--- a/docs/ezgatr/nets.html
+++ b/docs/ezgatr/nets.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/nets/mv_only_gatr.html b/docs/ezgatr/nets/mv_only_gatr.html
index 77e7079..24d3452 100644
--- a/docs/ezgatr/nets/mv_only_gatr.html
+++ b/docs/ezgatr/nets/mv_only_gatr.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/nn.html b/docs/ezgatr/nn.html
index 3e7230f..f1a7967 100644
--- a/docs/ezgatr/nn.html
+++ b/docs/ezgatr/nn.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/nn/functional.html b/docs/ezgatr/nn/functional.html
index ae43fb5..46dac5c 100644
--- a/docs/ezgatr/nn/functional.html
+++ b/docs/ezgatr/nn/functional.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/nn/modules.html b/docs/ezgatr/nn/modules.html
index c5a77b6..b1eccfc 100644
--- a/docs/ezgatr/nn/modules.html
+++ b/docs/ezgatr/nn/modules.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/utils.html b/docs/ezgatr/utils.html
index a54ae21..f727613 100644
--- a/docs/ezgatr/utils.html
+++ b/docs/ezgatr/utils.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/utils/debug.html b/docs/ezgatr/utils/debug.html
index 210e311..a259d75 100644
--- a/docs/ezgatr/utils/debug.html
+++ b/docs/ezgatr/utils/debug.html
@@ -22,7 +22,6 @@
diff --git a/docs/ezgatr/utils/logger.html b/docs/ezgatr/utils/logger.html
index e298255..c2c689f 100644
--- a/docs/ezgatr/utils/logger.html
+++ b/docs/ezgatr/utils/logger.html
@@ -22,7 +22,6 @@
diff --git a/docs/search.js b/docs/search.js
index ef0793a..2792f88 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
window.pdocSearch = (function(){
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\n\nExtract normal and translation vectors from multi-vectors with PGA.
\n\nNote that the translation vectors associated with each plane is not unique.\nIn this case, we use the PGA convention that e_0 coefficients are the\ndistances from the origin to the plane along the normal direction to determine\nthe translation vectors.
Encode 3D points into multi-vectors with PGA.
\n\nxyz coordinates; tensor of shape (..., 3).Extract 3D points from multi-vectors with PGA.
\n\nOne needs to divide the coordinates by the homogeneous coordinate to obtain\nthe 3D points. In this case, to prevent numerical instability, we set a threshold\nto the homogeneous coordinate, so that the division will only be performed when\nthe absolute value of the homogeneous coordinate is above the threshold.
\n\nxyz coordinates; tensor of shape (..., 3).Encode scalars into the pseudoscalar dimension of multi-vectors with PGA.
\n\nThis function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.
\n\nExtract the pseudoscalar values from multi-vectors with PGA.
\n\nThis function do not automatically squeeze the last dimension when returning\nthe scalar tensor.
\n\nEncode reflection into multi-vectors with PGA.
\n\nPlane serves as both the geometric object and the reflection operation in\nPGA. Therefore, we encode reflection over a plane as a plane itself.
\n\nExtract normal and position vectors of the reflection plane with PGA.
\n\nEncode 3D rotation (as quaternions) into multi-vectors with PGA.
\n\nijkw order and Hamilton convention (ijk = -1)Extract quaternions from multi-vectors with PGA.
\n\nijkw order and Hamilton convention (ijk = -1)Encode scalars into multi-vectors with PGA.
\n\nThis function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.
\n\nExtract scalars from multi-vectors with PGA.
\n\nThis function do not automatically squeeze the last dimension when returning\nthe scalar tensor.
\n\nEncode translations into multi-vectors with PGA.
\n\nWe use the convention that translations are represented by the xyz coordinates\nof the translation vectors in 3D Euclidean space, i.e., end positions.
Configuration class for the MVOnlyGATr model.
True.Embedding layer to project input number of channels to hidden channels.
\n\nThis layer corresponds to the very first equivariant linear layer of the\noriginal design mentioned in the GATr paper.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.embedding", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Implements the geometric bilinear sub-layer of the geometric MLP.
\n\nGeometric bilinear operation consists of geometric product and equivariant\njoin operations. The results of two operations are concatenated along the\nhidden channel axis and passed through a final equivariant linear projection\nbefore being passed to the next layer, block, or module.
\n\nIn both geometric product and equivariant join operations, the input\nmulti-vectors are first projected to a hidden space with the same number of\nchannels, i.e., left and right. Then, the results of each operation are\nderived from the interaction of left and right hidden representations, each\nwith half number of size_channels_intermediate.
MVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_bil", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_out", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.forward", "kind": "function", "doc": "Forward pass of the geometric bilinear sub-layer.
\n\nGeometric MLP block without scaler channels.
\n\nHere we fix the structure of the MLP block to be a single equivariant linear\nprojection followed by a gated GELU activation function. In addition, the\nequivariant normalization layer can be configured to be learnable, so the\nnormalization layer needs to be included in the block instead of being shared\nacross the network.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.layer_norm", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.equi_bil", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.proj_out", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.forward", "kind": "function", "doc": "Forward pass of the geometric MLP block.
\n\nGeometric attention block without scaler channels.
\n\nThe GATr attention calculation is slightly different from the original\ntransformers implementation in that each head has the sample number of\nchannels as the input tensor, instead of dividing into smaller chunks.\nIn this case, the final output linear transformation maps from\nsize_channels_hidden * attn_num_heads to size_channels_hidden.
One additional note here is that the attn_mix parameter is a dictionary\nof learnable weighting parameter LOGITS for each attention kind.\nThey will be exponentiated before being used in the attention calculation.
MVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.layer_norm", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.attn_mix", "kind": "variable", "doc": "\n", "annotation": ": dict[str, torch.Tensor]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_qkv", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_out", "kind": "variable", "doc": "\n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.forward", "kind": "function", "doc": "Forward pass of the geometric attention block.
\n\nGATr block without scaler channels.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig, layer_id: int)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.layer_id", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.mlp", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.attn", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Multi-Vector only GATr model.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.embedding", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.blocks", "kind": "variable", "doc": "\n", "annotation": ": torch.nn.modules.container.ModuleList"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.head", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Compute the query and key tensors for the distance-aware attention.
\n\nCompute the query and key tensors for the inner product attention.
\n\nCompute the dual of the input multi-vector.
\n\nEquivariant geometric attention.
\n\nNone to denote no additional parameters supplied. Available options:\nCompute the equivariant join of two multi-vectors given the reference.
\n\nPerform Pin-equivariant linear map defined by weight on input x.
\n\nOne way to think of the equivariant linear map is a channel-wise\n\"map-reduce\", where the same weight (of one neuron) are applied to\nall channels of the input multi-vector and the results are summed up\nalong the basis/blade axis. In other words, the map is a channel-mixing\noperation. Using a parallel analogy with a regular nn.Linear layer,\neach channel of a input multi-vector corresponds to a \"feature value\"\nof a simple hidden representation, and the number of output channels\nis the number of neurons in the hidden layer.
Within each channel, similar to the geometric product implementation,\nthe linear operation starts with a matrix multiplication between the\n(weighted, if normalized) 16-by-16 computation graph, i.e., the basis,\nand the input multi-vector to take into account the effect that blades\ncontaining e_0 will be \"downgraded\" to a lower grade after the map.\nAgain, a map from \"source-to-destination\" style. Note that we may want\nto optimize this operation as the basis is pretty sparse.
Compute PGA inner-induced RMS norm of multi-vectors.
\n\nAlthough the original GATr paper 1 named the normalization operation\nas E(3)-equivariant LayerNorm, we find the actual implementation more\nsimilar to RMSNorm 2 by substituting the scalar squaring with the inner\nproduct of multi-vectors, i.e., PGA inner-induced RMSNorm.
\n\nWe find the adaptation more intuitive by viewing each channel of the input\nmulti-vector as an \"imaginary number\", so that the inner product of the\nmulti-vector with itself is the squared \"modulus\". And, in turn, instead of\nthinking the input tensor as channels-of-multi-vectors, we can think of it\nas feature-vector-of-imaginary-numbers. And the normalization operation is\nthe same as the RMSNorm operation on the scalar-valued feature vectors.
\n\nNOTE: THIS DESIGN IS PROBABLY QUESTIONABLE. The inner product do not\nconsider coefficients associated with blades containing e_0 while\nthe scaling is also applied to these blades.
weight are initialized\noutside of the function.Geometric product between two batches of multi-vectors.
\n\nThe input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels.\nWhen channel dimensions are present, the geometric product is calculated\nchannel-wise (and batch-wise). For instance, the first channel of x[0]\nis multiplied with the first channel of y[0], and so on. No channel-mixing\nhere.
Compute the PGA inner product between two multi-vectors.
\n\nSimilarly, the PGA inner product is calculated channel-wise (and batch-wise).\nNo channel-mixing here.
\n\nOuter product between two batches of multi-vectors.
\n\nThe input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels. When\nchannel dimensions are present, the outer product is calculated channel-wise\n(and batch-wise). For instance, the first channel of x[0] is multiplied\nwith the first channel of y[0], and so on. No channel-mixing here.
Compute scaler-gated GeLU activation function.
\n\nPin(3, 0, 1)-equivariant linear map.
\n\nInitialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(\tin_channels: int,\tout_channels: int,\tbias: bool = True,\tnormalize_basis: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiLinear.in_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.in_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.out_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.out_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.out_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.normalize_basis": {"fullname": "ezgatr.nn.modules.EquiLinear.normalize_basis", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.normalize_basis", "kind": "variable", "doc": "\n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiLinear.weight": {"fullname": "ezgatr.nn.modules.EquiLinear.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.weight", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor"}, "ezgatr.nn.modules.EquiLinear.bias": {"fullname": "ezgatr.nn.modules.EquiLinear.bias", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.bias", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiLinear.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiLinear.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.reset_parameters", "kind": "function", "doc": "\n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.forward": {"fullname": "ezgatr.nn.modules.EquiLinear.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Set the extra representation of the module.
\n\nTo print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm": {"fullname": "ezgatr.nn.modules.EquiRMSNorm", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm", "kind": "class", "doc": "Applies PGA inner-induced RMS norm to a batch of multi-vectors.
\n\nInitialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(\tin_channels: int,\teps: float | None = None,\tchannelwise_rescale: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiRMSNorm.in_channels": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.in_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiRMSNorm.eps": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.eps", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.eps", "kind": "variable", "doc": "\n", "annotation": ": float | None"}, "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.channelwise_rescale", "kind": "variable", "doc": "\n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiRMSNorm.weight": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.weight", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiRMSNorm.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.reset_parameters", "kind": "function", "doc": "\n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.forward": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Set the extra representation of the module.
\n\nTo print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.utils": {"fullname": "ezgatr.utils", "modulename": "ezgatr.utils", "kind": "module", "doc": "\n"}, "ezgatr.utils.debug": {"fullname": "ezgatr.utils.debug", "modulename": "ezgatr.utils.debug", "kind": "module", "doc": "\n"}, "ezgatr.utils.debug.time_cuda_exec": {"fullname": "ezgatr.utils.debug.time_cuda_exec", "modulename": "ezgatr.utils.debug", "qualname": "time_cuda_exec", "kind": "function", "doc": "Time the execution of a function involving CUDA.
\n", "signature": "(n_exec: int = 1000, report_avg: bool = True):", "funcdef": "def"}, "ezgatr.utils.logger": {"fullname": "ezgatr.utils.logger", "modulename": "ezgatr.utils.logger", "kind": "module", "doc": "\n"}}, "docInfo": {"ezgatr": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 75}, "ezgatr.interfaces.plane.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 114}, "ezgatr.interfaces.point": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.point.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 61}, "ezgatr.interfaces.point.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 155}, "ezgatr.interfaces.pseudoscalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.pseudoscalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 84}, "ezgatr.interfaces.pseudoscalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 72}, "ezgatr.interfaces.reflection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.reflection.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 103}, "ezgatr.interfaces.reflection.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 68}, "ezgatr.interfaces.rotation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.rotation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 66}, "ezgatr.interfaces.rotation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 80}, "ezgatr.interfaces.scalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.scalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 78}, "ezgatr.interfaces.scalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 70}, "ezgatr.interfaces.translation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.translation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 87}, "ezgatr.interfaces.translation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 30, "bases": 0, "doc": 3}, "ezgatr.nets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 281}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 381, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 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EzGATr (Easy Geometric Algebra Transformer) intends to be a simple-to-use and lightweight Python library for building 3D Geometric Algebra Transformers (GATr). It is a collection of operators, modules, utilities, etc. build on top of PyTorch. In addition, EzGATr also seeks to bridge the gap between the mathematical formulations and corresponding implementations through extensive documentation and explanations to facilitate learning and potential future optimizations.
\n\nEzGATr is currently in development and not yet available on PyPI. To install it, you can clone the repository and install it using pip. Use the -e flag to install it in editable mode for quick changes.
![\"Website\"](\"https://img.shields.io/website?label=documentation&up_message=online&url=https%3A%2F%2Fguest400123064.github.io/ezgatr\")
$ git clone https://github.com/Guest400123064/ezgatr.git\n$ cd ezgatr\n$ pip install -e .\nThe quick start example shown below demonstrates how to use EzGATr to build a equivariant network with one input/output channel (e.g., a point cloud) and four hidden channels. The mock input data contains a batch of eight samples, each with 256 3D objects embedded as multi-vectors.
\n\nimport torch\nimport torch.nn as nn\n\nfrom ezgatr.nn import EquiLinear\nfrom ezgatr.nn.functional import scaler_gated_gelu\n\n\nclass SimpleNet(nn.Module):\n def __init__(self):\n super(SimpleNet, self).__init__()\n self.fc1 = EquiLinear(1, 4)\n self.fc2 = EquiLinear(4, 1)\n\n def forward(self, x):\n x = self.fc1(x)\n x = scaler_gated_gelu(x)\n x = self.fc2(x)\n return x\n\n\nnet = SimpleNet()\nin_ = torch.randn(8, 256, 1, 16)\nout = net(in_)\nOne can refer to this example for how to build a full-fledged GATr model with EzGATr, involving equivariant geometric attention and geometric MLP.
\n\nThe complete API references for EzGATr can be found here. TL;DR, the package is organized as follows:
\n\nezgatr.nn: Contains the core modules and layers for building GATr models. It is organized similarly to PyTorch's torch.nn package, where the functional submodule contains lower-level operators and transformations.ezgatr.interfaces: Contains the utility functions that help encode and decode 3D objects to and from multi-vectors.ezgatr.nets: Contains off-of-the-shelf networks built with EzGATr building blocks. It can also be used as references for building custom networks with EzGATr.EzGATr is distributed under the terms of the MIT license.
\n"}, "ezgatr.interfaces": {"fullname": "ezgatr.interfaces", "modulename": "ezgatr.interfaces", "kind": "module", "doc": "\n"}, "ezgatr.interfaces.plane": {"fullname": "ezgatr.interfaces.plane", "modulename": "ezgatr.interfaces.plane", "kind": "module", "doc": "\n"}, "ezgatr.interfaces.plane.encode_pga": {"fullname": "ezgatr.interfaces.plane.encode_pga", "modulename": "ezgatr.interfaces.plane", "qualname": "encode_pga", "kind": "function", "doc": "Encode planes into multi-vectors with PGA.
\n\nExtract normal and translation vectors from multi-vectors with PGA.
\n\nNote that the translation vectors associated with each plane is not unique.\nIn this case, we use the PGA convention that e_0 coefficients are the\ndistances from the origin to the plane along the normal direction to determine\nthe translation vectors.
Encode 3D points into multi-vectors with PGA.
\n\nxyz coordinates; tensor of shape (..., 3).Extract 3D points from multi-vectors with PGA.
\n\nOne needs to divide the coordinates by the homogeneous coordinate to obtain\nthe 3D points. In this case, to prevent numerical instability, we set a threshold\nto the homogeneous coordinate, so that the division will only be performed when\nthe absolute value of the homogeneous coordinate is above the threshold.
\n\nxyz coordinates; tensor of shape (..., 3).Encode scalars into the pseudoscalar dimension of multi-vectors with PGA.
\n\nThis function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.
\n\nExtract the pseudoscalar values from multi-vectors with PGA.
\n\nThis function do not automatically squeeze the last dimension when returning\nthe scalar tensor.
\n\nEncode reflection into multi-vectors with PGA.
\n\nPlane serves as both the geometric object and the reflection operation in\nPGA. Therefore, we encode reflection over a plane as a plane itself.
\n\nExtract normal and position vectors of the reflection plane with PGA.
\n\nEncode 3D rotation (as quaternions) into multi-vectors with PGA.
\n\nijkw order and Hamilton convention (ijk = -1)Extract quaternions from multi-vectors with PGA.
\n\nijkw order and Hamilton convention (ijk = -1)Encode scalars into multi-vectors with PGA.
\n\nThis function assumes that the scalar tensor has shape (..., 1)\nand will pad the remaining 15 components with zeros.
\n\nExtract scalars from multi-vectors with PGA.
\n\nThis function do not automatically squeeze the last dimension when returning\nthe scalar tensor.
\n\nEncode translations into multi-vectors with PGA.
\n\nWe use the convention that translations are represented by the xyz coordinates\nof the translation vectors in 3D Euclidean space, i.e., end positions.
Configuration class for the MVOnlyGATr model.
True.Embedding layer to project input number of channels to hidden channels.
\n\nThis layer corresponds to the very first equivariant linear layer of the\noriginal design mentioned in the GATr paper.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.embedding", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrEmbedding.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Implements the geometric bilinear sub-layer of the geometric MLP.
\n\nGeometric bilinear operation consists of geometric product and equivariant\njoin operations. The results of two operations are concatenated along the\nhidden channel axis and passed through a final equivariant linear projection\nbefore being passed to the next layer, block, or module.
\n\nIn both geometric product and equivariant join operations, the input\nmulti-vectors are first projected to a hidden space with the same number of\nchannels, i.e., left and right. Then, the results of each operation are\nderived from the interaction of left and right hidden representations, each\nwith half number of size_channels_intermediate.
MVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_bil", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.proj_out", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBilinear.forward", "kind": "function", "doc": "Forward pass of the geometric bilinear sub-layer.
\n\nGeometric MLP block without scaler channels.
\n\nHere we fix the structure of the MLP block to be a single equivariant linear\nprojection followed by a gated GELU activation function. In addition, the\nequivariant normalization layer can be configured to be learnable, so the\nnormalization layer needs to be included in the block instead of being shared\nacross the network.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.layer_norm", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.equi_bil", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.equi_bil", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrBilinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.proj_out", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrMLP.forward", "kind": "function", "doc": "Forward pass of the geometric MLP block.
\n\nGeometric attention block without scaler channels.
\n\nThe GATr attention calculation is slightly different from the original\ntransformers implementation in that each head has the sample number of\nchannels as the input tensor, instead of dividing into smaller chunks.\nIn this case, the final output linear transformation maps from\nsize_channels_hidden * attn_num_heads to size_channels_hidden.
One additional note here is that the attn_mix parameter is a dictionary\nof learnable weighting parameter LOGITS for each attention kind.\nThey will be exponentiated before being used in the attention calculation.
MVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.layer_norm", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.layer_norm", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.norm.EquiRMSNorm"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.attn_mix", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.attn_mix", "kind": "variable", "doc": "\n", "annotation": ": dict[str, torch.Tensor]"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_qkv", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_qkv", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.proj_out", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.proj_out", "kind": "variable", "doc": "\n"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrAttention.forward", "kind": "function", "doc": "Forward pass of the geometric attention block.
\n\nGATr block without scaler channels.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig, layer_id: int)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.layer_id", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.layer_id", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.mlp", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.mlp", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrMLP"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.attn", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.attn", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrAttention"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrBlock.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrBlock.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Multi-Vector only GATr model.
\n\nMVOnlyGATrConfig for more details.Initialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(config: ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig)"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.config", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.config", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.embedding", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.embedding", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nets.mv_only_gatr.MVOnlyGATrEmbedding"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.blocks", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.blocks", "kind": "variable", "doc": "\n", "annotation": ": torch.nn.modules.container.ModuleList"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.head", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.head", "kind": "variable", "doc": "\n", "annotation": ": ezgatr.nn.modules.linear.EquiLinear"}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward": {"fullname": "ezgatr.nets.mv_only_gatr.MVOnlyGATrModel.forward", "modulename": "ezgatr.nets.mv_only_gatr", "qualname": "MVOnlyGATrModel.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Compute the query and key tensors for the distance-aware attention.
\n\nCompute the query and key tensors for the inner product attention.
\n\nCompute the dual of the input multi-vector.
\n\nEquivariant geometric attention.
\n\nNone to denote no additional parameters supplied. Available options:\nCompute the equivariant join of two multi-vectors given the reference.
\n\nPerform Pin-equivariant linear map defined by weight on input x.
\n\nOne way to think of the equivariant linear map is a channel-wise\n\"map-reduce\", where the same weight (of one neuron) are applied to\nall channels of the input multi-vector and the results are summed up\nalong the basis/blade axis. In other words, the map is a channel-mixing\noperation. Using a parallel analogy with a regular nn.Linear layer,\neach channel of a input multi-vector corresponds to a \"feature value\"\nof a simple hidden representation, and the number of output channels\nis the number of neurons in the hidden layer.
Within each channel, similar to the geometric product implementation,\nthe linear operation starts with a matrix multiplication between the\n(weighted, if normalized) 16-by-16 computation graph, i.e., the basis,\nand the input multi-vector to take into account the effect that blades\ncontaining e_0 will be \"downgraded\" to a lower grade after the map.\nAgain, a map from \"source-to-destination\" style. Note that we may want\nto optimize this operation as the basis is pretty sparse.
Compute PGA inner-induced RMS norm of multi-vectors.
\n\nAlthough the original GATr paper 1 named the normalization operation\nas E(3)-equivariant LayerNorm, we find the actual implementation more\nsimilar to RMSNorm 2 by substituting the scalar squaring with the inner\nproduct of multi-vectors, i.e., PGA inner-induced RMSNorm.
\n\nWe find the adaptation more intuitive by viewing each channel of the input\nmulti-vector as an \"imaginary number\", so that the inner product of the\nmulti-vector with itself is the squared \"modulus\". And, in turn, instead of\nthinking the input tensor as channels-of-multi-vectors, we can think of it\nas feature-vector-of-imaginary-numbers. And the normalization operation is\nthe same as the RMSNorm operation on the scalar-valued feature vectors.
\n\nNOTE: THIS DESIGN IS PROBABLY QUESTIONABLE. The inner product do not\nconsider coefficients associated with blades containing e_0 while\nthe scaling is also applied to these blades.
weight are initialized\noutside of the function.Geometric product between two batches of multi-vectors.
\n\nThe input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels.\nWhen channel dimensions are present, the geometric product is calculated\nchannel-wise (and batch-wise). For instance, the first channel of x[0]\nis multiplied with the first channel of y[0], and so on. No channel-mixing\nhere.
Compute the PGA inner product between two multi-vectors.
\n\nSimilarly, the PGA inner product is calculated channel-wise (and batch-wise).\nNo channel-mixing here.
\n\nOuter product between two batches of multi-vectors.
\n\nThe input tensors x and y are multi-vectors with shape (..., 16).\nwhere ... dimensions can denote batches or batches plus channels. When\nchannel dimensions are present, the outer product is calculated channel-wise\n(and batch-wise). For instance, the first channel of x[0] is multiplied\nwith the first channel of y[0], and so on. No channel-mixing here.
Compute scaler-gated GeLU activation function.
\n\nPin(3, 0, 1)-equivariant linear map.
\n\nInitialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(\tin_channels: int,\tout_channels: int,\tbias: bool = True,\tnormalize_basis: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiLinear.in_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.in_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.out_channels": {"fullname": "ezgatr.nn.modules.EquiLinear.out_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.out_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiLinear.normalize_basis": {"fullname": "ezgatr.nn.modules.EquiLinear.normalize_basis", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.normalize_basis", "kind": "variable", "doc": "\n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiLinear.weight": {"fullname": "ezgatr.nn.modules.EquiLinear.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.weight", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor"}, "ezgatr.nn.modules.EquiLinear.bias": {"fullname": "ezgatr.nn.modules.EquiLinear.bias", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.bias", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiLinear.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiLinear.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.reset_parameters", "kind": "function", "doc": "\n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiLinear.forward": {"fullname": "ezgatr.nn.modules.EquiLinear.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiLinear.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Set the extra representation of the module.
\n\nTo print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm": {"fullname": "ezgatr.nn.modules.EquiRMSNorm", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm", "kind": "class", "doc": "Applies PGA inner-induced RMS norm to a batch of multi-vectors.
\n\nInitialize internal Module state, shared by both nn.Module and ScriptModule.
\n", "signature": "(\tin_channels: int,\teps: float | None = None,\tchannelwise_rescale: bool = True,\tdevice: torch.device | None = None,\tdtype: torch.dtype | None = None)"}, "ezgatr.nn.modules.EquiRMSNorm.in_channels": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.in_channels", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.in_channels", "kind": "variable", "doc": "\n", "annotation": ": int"}, "ezgatr.nn.modules.EquiRMSNorm.eps": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.eps", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.eps", "kind": "variable", "doc": "\n", "annotation": ": float | None"}, "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.channelwise_rescale", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.channelwise_rescale", "kind": "variable", "doc": "\n", "annotation": ": bool"}, "ezgatr.nn.modules.EquiRMSNorm.weight": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.weight", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.weight", "kind": "variable", "doc": "\n", "annotation": ": torch.Tensor | None"}, "ezgatr.nn.modules.EquiRMSNorm.reset_parameters": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.reset_parameters", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.reset_parameters", "kind": "function", "doc": "\n", "signature": "(self) -> None:", "funcdef": "def"}, "ezgatr.nn.modules.EquiRMSNorm.forward": {"fullname": "ezgatr.nn.modules.EquiRMSNorm.forward", "modulename": "ezgatr.nn.modules", "qualname": "EquiRMSNorm.forward", "kind": "function", "doc": "Define the computation performed at every call.
\n\nShould be overridden by all subclasses.
\n\nAlthough the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.
Set the extra representation of the module.
\n\nTo print customized extra information, you should re-implement\nthis method in your own modules. Both single-line and multi-line\nstrings are acceptable.
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ezgatr.utils": {"fullname": "ezgatr.utils", "modulename": "ezgatr.utils", "kind": "module", "doc": "\n"}, "ezgatr.utils.debug": {"fullname": "ezgatr.utils.debug", "modulename": "ezgatr.utils.debug", "kind": "module", "doc": "\n"}, "ezgatr.utils.debug.time_cuda_exec": {"fullname": "ezgatr.utils.debug.time_cuda_exec", "modulename": "ezgatr.utils.debug", "qualname": "time_cuda_exec", "kind": "function", "doc": "Time the execution of a function involving CUDA.
\n", "signature": "(n_exec: int = 1000, report_avg: bool = True):", "funcdef": "def"}, "ezgatr.utils.logger": {"fullname": "ezgatr.utils.logger", "modulename": "ezgatr.utils.logger", "kind": "module", "doc": "\n"}}, "docInfo": {"ezgatr": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 778}, "ezgatr.interfaces": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.plane.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 75}, "ezgatr.interfaces.plane.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 114}, "ezgatr.interfaces.point": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.point.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 61}, "ezgatr.interfaces.point.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 155}, "ezgatr.interfaces.pseudoscalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.pseudoscalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 84}, "ezgatr.interfaces.pseudoscalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 72}, "ezgatr.interfaces.reflection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.reflection.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 44, "bases": 0, "doc": 103}, "ezgatr.interfaces.reflection.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 68}, "ezgatr.interfaces.rotation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.rotation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 66}, "ezgatr.interfaces.rotation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 46, "bases": 0, "doc": 80}, "ezgatr.interfaces.scalar": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.scalar.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 78}, "ezgatr.interfaces.scalar.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 70}, "ezgatr.interfaces.translation": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.interfaces.translation.encode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 87}, "ezgatr.interfaces.translation.decode_pga": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 30, "bases": 0, "doc": 3}, "ezgatr.nets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr": {"qualname": 0, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig": {"qualname": 1, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 281}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.__init__": {"qualname": 3, "fullname": 8, "annotation": 0, "default_value": 0, "signature": 381, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.num_layers": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_context": {"qualname": 3, "fullname": 8, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_in": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_out": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "ezgatr.nets.mv_only_gatr.MVOnlyGATrConfig.size_channels_hidden": {"qualname": 4, "fullname": 9, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 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a/src/ezgatr/__init__.py b/src/ezgatr/__init__.py index e69de29..02665fe 100644 --- a/src/ezgatr/__init__.py +++ b/src/ezgatr/__init__.py @@ -0,0 +1,3 @@ +r""" +.. include:: ../../README.md +"""