159 lines
5.3 KiB
Python
159 lines
5.3 KiB
Python
from typing import Callable
|
|
|
|
import jax, jax.numpy as jnp
|
|
from utils import unflatten_conns, flatten_conns, topological_sort, I_INF
|
|
|
|
from . import BaseGenome
|
|
from ..gene import BaseNodeGene, BaseConnGene, DefaultNodeGene, DefaultConnGene
|
|
from ..ga import BaseMutation, BaseCrossover, DefaultMutation, DefaultCrossover
|
|
|
|
|
|
class DefaultGenome(BaseGenome):
|
|
"""Default genome class, with the same behavior as the NEAT-Python"""
|
|
|
|
network_type = "feedforward"
|
|
|
|
def __init__(
|
|
self,
|
|
num_inputs: int,
|
|
num_outputs: int,
|
|
max_nodes=5,
|
|
max_conns=4,
|
|
node_gene: BaseNodeGene = DefaultNodeGene(),
|
|
conn_gene: BaseConnGene = DefaultConnGene(),
|
|
mutation: BaseMutation = DefaultMutation(),
|
|
crossover: BaseCrossover = DefaultCrossover(),
|
|
output_transform: Callable = None,
|
|
):
|
|
super().__init__(
|
|
num_inputs,
|
|
num_outputs,
|
|
max_nodes,
|
|
max_conns,
|
|
node_gene,
|
|
conn_gene,
|
|
mutation,
|
|
crossover,
|
|
)
|
|
|
|
if output_transform is not None:
|
|
try:
|
|
_ = output_transform(jnp.zeros(num_outputs))
|
|
except Exception as e:
|
|
raise ValueError(f"Output transform function failed: {e}")
|
|
self.output_transform = output_transform
|
|
|
|
def transform(self, state, nodes, conns):
|
|
u_conns = unflatten_conns(nodes, conns)
|
|
conn_exist = ~jnp.isnan(u_conns[0])
|
|
|
|
seqs = topological_sort(nodes, conn_exist)
|
|
|
|
return seqs, nodes, u_conns
|
|
|
|
def restore(self, state, transformed):
|
|
seqs, nodes, u_conns = transformed
|
|
conns = flatten_conns(nodes, u_conns, C=self.max_conns)
|
|
return nodes, conns
|
|
|
|
def forward(self, state, inputs, transformed):
|
|
cal_seqs, nodes, u_conns = transformed
|
|
|
|
ini_vals = jnp.full((self.max_nodes,), jnp.nan)
|
|
ini_vals = ini_vals.at[self.input_idx].set(inputs)
|
|
nodes_attrs = nodes[:, 1:]
|
|
|
|
def cond_fun(carry):
|
|
values, idx = carry
|
|
return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
|
|
|
|
def body_func(carry):
|
|
values, idx = carry
|
|
i = cal_seqs[idx]
|
|
|
|
def hit():
|
|
ins = jax.vmap(self.conn_gene.forward, in_axes=(None, 1, 0))(
|
|
state, u_conns[:, :, i], values
|
|
)
|
|
|
|
z = self.node_gene.forward(
|
|
state,
|
|
nodes_attrs[i],
|
|
ins,
|
|
is_output_node=jnp.isin(i, self.output_idx),
|
|
)
|
|
|
|
new_values = values.at[i].set(z)
|
|
return new_values
|
|
|
|
# the val of input nodes is obtained by the task, not by calculation
|
|
values = jax.lax.cond(jnp.isin(i, self.input_idx), lambda: values, hit)
|
|
|
|
return values, idx + 1
|
|
|
|
vals, _ = jax.lax.while_loop(cond_fun, body_func, (ini_vals, 0))
|
|
|
|
if self.output_transform is None:
|
|
return vals[self.output_idx]
|
|
else:
|
|
return self.output_transform(vals[self.output_idx])
|
|
|
|
def update_by_batch(self, state, batch_input, transformed):
|
|
cal_seqs, nodes, u_conns = transformed
|
|
|
|
batch_size = batch_input.shape[0]
|
|
batch_ini_vals = jnp.full((batch_size, self.max_nodes), jnp.nan)
|
|
batch_ini_vals = batch_ini_vals.at[:, self.input_idx].set(batch_input)
|
|
nodes_attrs = nodes[:, 1:]
|
|
|
|
def cond_fun(carry):
|
|
batch_values, nodes_attrs_, u_conns_, idx = carry
|
|
return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
|
|
|
|
def body_func(carry):
|
|
batch_values, nodes_attrs_, u_conns_, idx = carry
|
|
i = cal_seqs[idx]
|
|
|
|
def hit():
|
|
batch_ins, new_conn_attrs = jax.vmap(
|
|
self.conn_gene.update_by_batch,
|
|
in_axes=(None, 1, 1),
|
|
out_axes=(1, 1),
|
|
)(state, u_conns_[:, :, i], batch_values)
|
|
batch_z, new_node_attrs = self.node_gene.update_by_batch(
|
|
state,
|
|
nodes_attrs[i],
|
|
batch_ins,
|
|
is_output_node=jnp.isin(i, self.output_idx),
|
|
)
|
|
new_batch_values = batch_values.at[:, i].set(batch_z)
|
|
return (
|
|
new_batch_values,
|
|
nodes_attrs_.at[i].set(new_node_attrs),
|
|
u_conns_.at[:, :, i].set(new_conn_attrs),
|
|
)
|
|
|
|
# the val of input nodes is obtained by the task, not by calculation
|
|
(batch_values, nodes_attrs_, u_conns_) = jax.lax.cond(
|
|
jnp.isin(i, self.input_idx),
|
|
lambda: (batch_values, nodes_attrs_, u_conns_),
|
|
hit,
|
|
)
|
|
|
|
return batch_values, nodes_attrs_, u_conns_, idx + 1
|
|
|
|
batch_vals, nodes_attrs, u_conns, _ = jax.lax.while_loop(
|
|
cond_fun, body_func, (batch_ini_vals, nodes_attrs, u_conns, 0)
|
|
)
|
|
|
|
nodes = nodes.at[:, 1:].set(nodes_attrs)
|
|
new_transformed = (cal_seqs, nodes, u_conns)
|
|
|
|
if self.output_transform is None:
|
|
return batch_vals[:, self.output_idx], new_transformed
|
|
else:
|
|
return (
|
|
jax.vmap(self.output_transform)(batch_vals[:, self.output_idx]),
|
|
new_transformed,
|
|
)
|