115 lines
3.5 KiB
Python
Executable File
115 lines
3.5 KiB
Python
Executable File
import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from model.STGNCDE import controldiffeq
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from model.STGNCDE.vector_fields import *
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class NeuralGCDE(nn.Module):
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def __init__(
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self,
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args,
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func_f,
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func_g,
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input_channels,
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hidden_channels,
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output_channels,
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initial,
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device,
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atol,
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rtol,
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solver,
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):
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super(NeuralGCDE, self).__init__()
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self.num_node = args["num_nodes"]
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self.input_dim = input_channels
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self.hidden_dim = hidden_channels
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self.output_dim = output_channels
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self.horizon = args["horizon"]
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self.num_layers = args["num_layers"]
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self.default_graph = args["default_graph"]
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self.node_embeddings = nn.Parameter(
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torch.randn(self.num_node, args["embed_dim"]), requires_grad=True
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)
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self.func_f = func_f
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self.func_g = func_g
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self.solver = solver
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self.atol = atol
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self.rtol = rtol
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# predictor
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self.end_conv = nn.Conv2d(
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1,
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args["horizon"] * self.output_dim,
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kernel_size=(1, self.hidden_dim),
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bias=True,
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)
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self.init_type = "fc"
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if self.init_type == "fc":
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self.initial_h = torch.nn.Linear(self.input_dim, self.hidden_dim)
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self.initial_z = torch.nn.Linear(self.input_dim, self.hidden_dim)
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elif self.init_type == "conv":
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self.start_conv_h = nn.Conv2d(
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in_channels=input_channels,
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out_channels=hidden_channels,
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kernel_size=(1, 1),
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)
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self.start_conv_z = nn.Conv2d(
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in_channels=input_channels,
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out_channels=hidden_channels,
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kernel_size=(1, 1),
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)
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def forward(self, times, coeffs):
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# source: B, T_1, N, D
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# target: B, T_2, N, D
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spline = controldiffeq.NaturalCubicSpline(times, coeffs)
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if self.init_type == "fc":
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h0 = self.initial_h(spline.evaluate(times[0]))
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z0 = self.initial_z(spline.evaluate(times[0]))
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elif self.init_type == "conv":
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h0 = (
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self.start_conv_h(
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spline.evaluate(times[0]).transpose(1, 2).unsqueeze(-1)
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)
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.transpose(1, 2)
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.squeeze()
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)
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z0 = (
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self.start_conv_z(
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spline.evaluate(times[0]).transpose(1, 2).unsqueeze(-1)
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)
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.transpose(1, 2)
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.squeeze()
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)
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z_t = controldiffeq.cdeint_gde_dev(
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dX_dt=spline.derivative, # dh_dt
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h0=h0,
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z0=z0,
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func_f=self.func_f,
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func_g=self.func_g,
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t=times,
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method=self.solver,
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atol=self.atol,
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rtol=self.rtol,
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)
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# init_state = self.encoder.init_hidden(source.shape[0])
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# output, _ = self.encoder(source, init_state, self.node_embeddings) #B, T, N, hidden
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# output = output[:, -1:, :, :] #B, 1, N, hidden
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z_T = z_t[-1:, ...].transpose(0, 1)
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# CNN based predictor
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output = self.end_conv(z_T) # B, T*C, N, 1
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output = output.squeeze(-1).reshape(
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-1, self.horizon, self.output_dim, self.num_node
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)
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output = output.permute(0, 1, 3, 2) # B, T, N, C
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return output
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