models/three_wl.py
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27
E501: line too long (86 > 79 characters)
(and 19 similar)
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Line too long (86 > 79 characters):
10 CODE adapted from https://github.com/hadarser/ProvablyPowerfulGraphNetworks_torch/ -
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15 def __init__(self, gnn_type, num_tasks, three_wl_in_dim, n_layers=3, depth_of_mlp=2, hidden_dim=128, -
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23 original_features_num = self.in_dim_node + 1 # Number of features of the input -
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29 mlp_block = RegularBlock(depth_of_mlp, last_layer_features, next_layer_features, self.residual) -
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35 # each block's output will be pooled (thus have 2*output_features), and pass through a fully connected -
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36 fc = FullyConnected(2 * output_features, num_tasks, activation_fn=None) -
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48 # def __init__(self, gnn_type, num_tasks, feature_dim, n_layers=3, depth_of_mlp=2, hidden_dim=128, -
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57 # block_features = [hidden_dim] * n_layers # L here is the block number -
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58 # original_features_num = self.in_dim_node + self.num_bond_type + 1 # Number of features of the input -
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64 # mlp_block = RegularBlock(depth_of_mlp, last_layer_features, next_layer_features, self.residual) -
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70 # # each block's output will be pooled (thus have 2*output_features), and pass through a fully connected -
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71 # fc = FullyConnected(2 * output_features, num_tasks, activation_fn=None) -
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88 list_FC_layers = [nn.Linear(input_dim // 2 ** l, input_dim // 2 ** (l + 1), bias=True) for l in range(L)] -
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89 list_FC_layers.append(nn.Linear(input_dim // 2 ** L, output_dim, bias=True)) -
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105 Take the input through 2 parallel MLP routes, multiply the result, and add a skip-connection at the end. -
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109 def __init__(self, depth_of_mlp, in_features, out_features, residual=False): -
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132 inputs, out = inputs.permute(3, 2, 1, 0).squeeze(), out.permute(3, 2, 1, 0).squeeze() -
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148 def __init__(self, in_features, out_features, depth_of_mlp, activation_fn=nn.functional.relu): -
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153 self.convs.append(nn.Conv2d(in_features, out_features, kernel_size=1, padding=0, bias=True)) -
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177 self.conv = nn.Conv2d(in_features, out_features, kernel_size=1, padding=0, bias=True) -
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189 def __init__(self, in_features, out_features, activation_fn=nn.functional.relu): -
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208 max_diag = torch.max(torch.diagonal(input, dim1=-2, dim2=-1), dim=2)[0] # BxS -
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215 min_mat = torch.mul(val, torch.eye(N, device=input.device)).view(1, 1, N, N) -
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217 max_offdiag = torch.max(torch.max(input - min_mat, dim=3)[0], dim=2)[0] # BxS -
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237 self.a = nn.Parameter(torch.ones(d).unsqueeze(0).unsqueeze(0)) # shape is 1 x 1 x d -
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238 self.b = nn.Parameter(torch.zeros(d).unsqueeze(0).unsqueeze(0)) # shape is 1 x 1 x d -
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244 x = self.a * (x - mean) / torch.sqrt(var + 1e-6) + self.b # shape is n x n x d
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- 2 E741: ambiguous variable name 'l'