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| import torch | |
| import torch.nn as nn | |
| #from equivariant_attention.modules import get_basis_and_r, GSE3Res, GNormBias | |
| #from equivariant_attention.modules import GConvSE3, GNormSE3 | |
| #from equivariant_attention.fibers import Fiber | |
| from util_module import init_lecun_normal_param | |
| from se3_transformer.model import SE3Transformer | |
| from se3_transformer.model.fiber import Fiber | |
| class SE3TransformerWrapper(nn.Module): | |
| """SE(3) equivariant GCN with attention""" | |
| def __init__(self, num_layers=2, num_channels=32, num_degrees=3, n_heads=4, div=4, | |
| l0_in_features=32, l0_out_features=32, | |
| l1_in_features=3, l1_out_features=2, | |
| num_edge_features=32): | |
| super().__init__() | |
| # Build the network | |
| self.l1_in = l1_in_features | |
| # | |
| fiber_edge = Fiber({0: num_edge_features}) | |
| if l1_out_features > 0: | |
| if l1_in_features > 0: | |
| fiber_in = Fiber({0: l0_in_features, 1: l1_in_features}) | |
| fiber_hidden = Fiber.create(num_degrees, num_channels) | |
| fiber_out = Fiber({0: l0_out_features, 1: l1_out_features}) | |
| else: | |
| fiber_in = Fiber({0: l0_in_features}) | |
| fiber_hidden = Fiber.create(num_degrees, num_channels) | |
| fiber_out = Fiber({0: l0_out_features, 1: l1_out_features}) | |
| else: | |
| if l1_in_features > 0: | |
| fiber_in = Fiber({0: l0_in_features, 1: l1_in_features}) | |
| fiber_hidden = Fiber.create(num_degrees, num_channels) | |
| fiber_out = Fiber({0: l0_out_features}) | |
| else: | |
| fiber_in = Fiber({0: l0_in_features}) | |
| fiber_hidden = Fiber.create(num_degrees, num_channels) | |
| fiber_out = Fiber({0: l0_out_features}) | |
| self.se3 = SE3Transformer(num_layers=num_layers, | |
| fiber_in=fiber_in, | |
| fiber_hidden=fiber_hidden, | |
| fiber_out = fiber_out, | |
| num_heads=n_heads, | |
| channels_div=div, | |
| fiber_edge=fiber_edge, | |
| use_layer_norm=True) | |
| #use_layer_norm=False) | |
| self.reset_parameter() | |
| def reset_parameter(self): | |
| # make sure linear layer before ReLu are initialized with kaiming_normal_ | |
| for n, p in self.se3.named_parameters(): | |
| if "bias" in n: | |
| nn.init.zeros_(p) | |
| elif len(p.shape) == 1: | |
| continue | |
| else: | |
| if "radial_func" not in n: | |
| p = init_lecun_normal_param(p) | |
| else: | |
| if "net.6" in n: | |
| nn.init.zeros_(p) | |
| else: | |
| nn.init.kaiming_normal_(p, nonlinearity='relu') | |
| # make last layers to be zero-initialized | |
| #self.se3.graph_modules[-1].to_kernel_self['0'] = init_lecun_normal_param(self.se3.graph_modules[-1].to_kernel_self['0']) | |
| #self.se3.graph_modules[-1].to_kernel_self['1'] = init_lecun_normal_param(self.se3.graph_modules[-1].to_kernel_self['1']) | |
| nn.init.zeros_(self.se3.graph_modules[-1].to_kernel_self['0']) | |
| nn.init.zeros_(self.se3.graph_modules[-1].to_kernel_self['1']) | |
| def forward(self, G, type_0_features, type_1_features=None, edge_features=None): | |
| if self.l1_in > 0: | |
| node_features = {'0': type_0_features, '1': type_1_features} | |
| else: | |
| node_features = {'0': type_0_features} | |
| edge_features = {'0': edge_features} | |
| return self.se3(G, node_features, edge_features) | |