<jupyter_start><jupyter_text>ModelThis is the model file for the PyTorch implementation<jupyter_code>import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn import init
import math
from net import MLP, StateTransition, GINTransition, GINPreTransition


class GNN(nn.Module):

    def __init__(self, config, state_net=None, out_net=None):
        super(GNN, self).__init__()

        self.config = config
        # hyperparameters and general properties
        self.convergence_threshold = config.convergence_threshold
        self.max_iterations = config.max_iterations
        self.n_nodes = config.n_nodes
        self.state_dim = config.state_dim
        self.label_dim = config.label_dim
        self.output_dim = config.output_dim
        self.state_transition_hidden_dims = config.state_transition_hidden_dims
        self.output_function_hidden_dims = config.output_function_hidden_dims

        # node state initialization
        self.node_state = torch.zeros(*[self.n_nodes, self.state_dim]).to(self.config.device)  # (n,d_n)
        self.converged_states = torch.zeros(*[self.n_nodes, self.state_dim]).to(self.config.device)
        # state and output transition functions
        if state_net is None:
            # self.state_transition_function = StateTransition(self.state_dim, self.label_dim,
            #                                                  mlp_hidden_dim=self.state_transition_hidden_dims,
            #                                                  activation_function=config.activation)
            self.state_transition_function = GINPreTransition(self.state_dim, self.label_dim,
                                                             mlp_hidden_dim=self.state_transition_hidden_dims,
                                                             activation_function=config.activation)


        else:
            self.state_transition_function = state_net
        if out_net is None:
            self.output_function = MLP(self.state_dim, self.output_function_hidden_dims, self.output_dim)
        else:
            self.output_function = out_net

        self.graph_based = self.config.graph_based

    def reset_parameters(self):

        self.state_transition_function.mlp.init()
        self.output_function.init()

    def forward(self,
                edges,
                agg_matrix,
                node_labels,
                node_states=None,
                graph_agg=None
                ):
        n_iterations = 0
        # convergence loop
        # state initialization
        node_states = self.node_state if node_states is None else node_states


        # while n_iterations < self.max_iterations:
        #     with torch.no_grad():  # without memory consumption
        #         new_state = self.state_transition_function(node_states, node_labels, edges, agg_matrix)
        #     n_iterations += 1
        #     # convergence condition
        #
        #     # if torch.dist(node_states, new_state) < self.convergence_threshold:  # maybe uses broadcst?
        #     #     break
        #     # with torch.no_grad():
        #         # distance = torch.sqrt(torch.sum((new_state - node_states) ** 2, 1) + 1e-20)
        #     distance = torch.norm(input=new_state - node_states,
        #                           dim=1)  # checked, they are the same (in cuda, some bug)
        #     #
        #     # diff =torch.norm(input=new_state - node_states, dim=1) -  torch.sqrt(torch.sum((new_state - node_states) ** 2, 1) )
        #
        #     check_min = distance < self.convergence_threshold
        #     node_states = new_state
        #
        #     if check_min.all():
        #         break
        # node_states = self.state_transition_function(node_states, node_labels, edges, agg_matrix) # one more to propagate gradient only on last

        while n_iterations < self.max_iterations:
            new_state = self.state_transition_function(node_states, node_labels, edges, agg_matrix)
            n_iterations += 1
            # convergence condition
            with torch.no_grad():
                # distance = torch.sqrt(torch.sum((new_state - node_states) ** 2, 1) + 1e-20)
                distance = torch.norm(input=new_state - node_states,
                                      dim=1)  # checked, they are the same (in cuda, some bug)

                check_min = distance < self.convergence_threshold
            node_states = new_state

            if check_min.all():
                break

        states = node_states
        self.converged_states = states
        if self.graph_based:
            states = torch.matmul(graph_agg, node_states)

        output = self.output_function(states)

        return output, n_iterations<jupyter_output><empty_output>
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