163 lines
6.8 KiB
Python
Executable File
163 lines
6.8 KiB
Python
Executable File
import numpy as np
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import torch
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from model.DCRNN import utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class LayerParams:
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def __init__(self, rnn_network: torch.nn.Module, layer_type: str):
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self._rnn_network = rnn_network
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self._params_dict = {}
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self._biases_dict = {}
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self._type = layer_type
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def get_weights(self, shape):
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if shape not in self._params_dict:
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nn_param = torch.nn.Parameter(torch.empty(*shape, device=device))
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torch.nn.init.xavier_normal_(nn_param)
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self._params_dict[shape] = nn_param
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self._rnn_network.register_parameter('{}_weight_{}'.format(self._type, str(shape)),
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nn_param)
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return self._params_dict[shape]
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def get_biases(self, length, bias_start=0.0):
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if length not in self._biases_dict:
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biases = torch.nn.Parameter(torch.empty(length, device=device))
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torch.nn.init.constant_(biases, bias_start)
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self._biases_dict[length] = biases
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self._rnn_network.register_parameter('{}_biases_{}'.format(self._type, str(length)),
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biases)
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return self._biases_dict[length]
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class DCGRUCell(torch.nn.Module):
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def __init__(self, num_units, adj_mx, max_diffusion_step, num_nodes, nonlinearity='tanh',
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filter_type="laplacian", use_gc_for_ru=True):
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"""
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:param num_units:
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:param adj_mx:
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:param max_diffusion_step:
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:param num_nodes:
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:param nonlinearity:
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:param filter_type: "laplacian", "random_walk", "dual_random_walk".
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:param use_gc_for_ru: whether to use Graph convolution to calculate the reset and update gates.
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"""
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super().__init__()
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self._activation = torch.tanh if nonlinearity == 'tanh' else torch.relu
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# support other nonlinearities up here?
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self._num_nodes = num_nodes
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self._num_units = num_units
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self._max_diffusion_step = max_diffusion_step
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self._supports = []
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self._use_gc_for_ru = use_gc_for_ru
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supports = []
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if filter_type == "laplacian":
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supports.append(utils.calculate_scaled_laplacian(adj_mx, lambda_max=None))
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elif filter_type == "random_walk":
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supports.append(utils.calculate_random_walk_matrix(adj_mx).T)
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elif filter_type == "dual_random_walk":
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supports.append(utils.calculate_random_walk_matrix(adj_mx).T)
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supports.append(utils.calculate_random_walk_matrix(adj_mx.T).T)
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else:
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supports.append(utils.calculate_scaled_laplacian(adj_mx))
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for support in supports:
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self._supports.append(self._build_sparse_matrix(support))
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self._fc_params = LayerParams(self, 'fc')
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self._gconv_params = LayerParams(self, 'gconv')
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@staticmethod
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def _build_sparse_matrix(L):
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L = L.tocoo()
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indices = np.column_stack((L.row, L.col))
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# this is to ensure row-major ordering to equal torch.sparse.sparse_reorder(L)
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indices = indices[np.lexsort((indices[:, 0], indices[:, 1]))]
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L = torch.sparse_coo_tensor(indices.T, L.data, L.shape, device=device)
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return L
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def forward(self, inputs, hx):
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"""Gated recurrent unit (GRU) with Graph Convolution.
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:param inputs: (B, num_nodes * input_dim)
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:param hx: (B, num_nodes * rnn_units)
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:return
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- Output: A `2-D` tensor with shape `(B, num_nodes * rnn_units)`.
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"""
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output_size = 2 * self._num_units
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if self._use_gc_for_ru:
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fn = self._gconv
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else:
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fn = self._fc
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value = torch.sigmoid(fn(inputs, hx, output_size, bias_start=1.0))
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value = torch.reshape(value, (-1, self._num_nodes, output_size))
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r, u = torch.split(tensor=value, split_size_or_sections=self._num_units, dim=-1)
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r = torch.reshape(r, (-1, self._num_nodes * self._num_units))
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u = torch.reshape(u, (-1, self._num_nodes * self._num_units))
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c = self._gconv(inputs, r * hx, self._num_units)
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if self._activation is not None:
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c = self._activation(c)
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new_state = u * hx + (1.0 - u) * c
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return new_state
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@staticmethod
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def _concat(x, x_):
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x_ = x_.unsqueeze(0)
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return torch.cat([x, x_], dim=0)
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def _fc(self, inputs, state, output_size, bias_start=0.0):
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batch_size = inputs.shape[0]
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inputs = torch.reshape(inputs, (batch_size * self._num_nodes, -1))
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state = torch.reshape(state, (batch_size * self._num_nodes, -1))
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inputs_and_state = torch.cat([inputs, state], dim=-1)
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input_size = inputs_and_state.shape[-1]
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weights = self._fc_params.get_weights((input_size, output_size))
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value = torch.sigmoid(torch.matmul(inputs_and_state, weights))
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biases = self._fc_params.get_biases(output_size, bias_start)
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value += biases
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return value
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def _gconv(self, inputs, state, output_size, bias_start=0.0):
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# Reshape input and state to (batch_size, num_nodes, input_dim/state_dim)
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batch_size = inputs.shape[0]
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inputs = torch.reshape(inputs, (batch_size, self._num_nodes, -1))
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state = torch.reshape(state, (batch_size, self._num_nodes, -1))
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inputs_and_state = torch.cat([inputs, state], dim=2)
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input_size = inputs_and_state.size(2)
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x = inputs_and_state
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x0 = x.permute(1, 2, 0) # (num_nodes, total_arg_size, batch_size)
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x0 = torch.reshape(x0, shape=[self._num_nodes, input_size * batch_size])
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x = torch.unsqueeze(x0, 0)
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if self._max_diffusion_step == 0:
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pass
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else:
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for support in self._supports:
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x1 = torch.sparse.mm(support, x0)
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x = self._concat(x, x1)
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for k in range(2, self._max_diffusion_step + 1):
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x2 = 2 * torch.sparse.mm(support, x1) - x0
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x = self._concat(x, x2)
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x1, x0 = x2, x1
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num_matrices = len(self._supports) * self._max_diffusion_step + 1 # Adds for x itself.
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x = torch.reshape(x, shape=[num_matrices, self._num_nodes, input_size, batch_size])
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x = x.permute(3, 1, 2, 0) # (batch_size, num_nodes, input_size, order)
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x = torch.reshape(x, shape=[batch_size * self._num_nodes, input_size * num_matrices])
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weights = self._gconv_params.get_weights((input_size * num_matrices, output_size))
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x = torch.matmul(x, weights) # (batch_size * self._num_nodes, output_size)
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biases = self._gconv_params.get_biases(output_size, bias_start)
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x += biases
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# Reshape res back to 2D: (batch_size, num_node, state_dim) -> (batch_size, num_node * state_dim)
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return torch.reshape(x, [batch_size, self._num_nodes * output_size])
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