176 lines
6.6 KiB
Python
176 lines
6.6 KiB
Python
import torch
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from torch import nn
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import numpy as np
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import tqdm
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from torch.utils.data import Dataset, DataLoader
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from labirinth_ai.Models.BaseModel import device
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class NodeGene:
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valid_types = ['sensor', 'hidden', 'output']
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def __init__(self, node_id, node_type, bias=None):
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assert node_type in self.valid_types, 'Unknown node type!'
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self.node_id = node_id
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self.node_type = node_type
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if node_type == 'hidden':
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assert bias is not None, 'Expected a bias for hidden node types!'
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self.bias = bias
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else:
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self.bias = None
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class ConnectionGene:
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def __init__(self, start, end, enabled, innovation_num, weight=None, recurrent=False):
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self.start = start
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self.end = end
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self.enabled = enabled
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self.innvovation_num = innovation_num
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self.recurrent = recurrent
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if weight is None:
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self.weight = np.random.random(1)[0] * 2 - 1.0
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else:
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self.weight = weight
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class EvolutionModel(nn.Module):
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evolutionary = True
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def __init__(self, view_dimension, action_num, channels, genes=None):
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super(EvolutionModel, self).__init__()
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self.flatten = nn.Flatten()
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self.action_num = action_num
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self.viewD = view_dimension
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self.channels = channels
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if genes is None:
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self.num_input_nodes = channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2
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self.genes = {'nodes': {}, 'connections': []}
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node_id = 0
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for _ in range(self.num_input_nodes):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'sensor')
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node_id += 1
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first_action = node_id
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for _ in range(action_num * 2):
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self.genes['nodes'][node_id] = NodeGene(node_id, 'output')
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node_id += 1
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for index in range(self.num_input_nodes):
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for action in range(action_num * 2):
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self.genes['connections'].append(
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ConnectionGene(index, first_action + action, True, index*(action_num * 2) + action)
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)
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self.incoming_connections = {}
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for connection in self.genes['connections']:
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if connection.end not in self.incoming_connections.keys():
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self.incoming_connections[connection.end] = []
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self.incoming_connections[connection.end].append(connection)
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self.layers = {}
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self.indices = {}
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self.has_recurrent = False
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non_recurrent_indices = {}
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with torch.no_grad():
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for key, value in self.incoming_connections.items():
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value.sort(key=lambda element: element.start)
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lin = nn.Linear(len(value), 1, bias=self.genes['nodes'][key].bias is not None)
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for index, connection in enumerate(value):
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lin.weight[0, index] = value[index].weight
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if self.genes['nodes'][key].bias is not None:
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lin.bias[0] = self.genes['nodes'][key].bias
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non_lin = nn.ELU()
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sequence = nn.Sequential(
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lin,
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non_lin
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)
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self.add_module('layer_' + str(key), sequence)
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self.layers[key] = sequence
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self.indices[key] = list(map(lambda element: element.start, value))
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non_recurrent_indices[key] = list(filter(lambda element: not element.recurrent, value))
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if not self.has_recurrent and len(non_recurrent_indices[key]) != len(self.indices[key]):
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self.has_recurrent = True
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non_recurrent_indices[key] = list(map(lambda element: element.start, non_recurrent_indices[key]))
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rank_of_node = {}
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for i in range(self.num_input_nodes):
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rank_of_node[i] = 0
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layers_to_add = list(non_recurrent_indices.items())
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while len(layers_to_add) > 0:
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for index, (key, incoming_nodes) in enumerate(list(layers_to_add)):
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max_rank = -1
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all_ranks_found = True
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for incoming_node in incoming_nodes:
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if incoming_node in rank_of_node.keys():
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max_rank = max(max_rank, rank_of_node[incoming_node])
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else:
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all_ranks_found = False
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if all_ranks_found:
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rank_of_node[key] = max_rank + 1
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layers_to_add = list(filter(lambda element: element[0] not in rank_of_node.keys(), layers_to_add))
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ranked_layers = list(rank_of_node.items())
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ranked_layers.sort(key=lambda element: element[1])
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ranked_layers = list(filter(lambda element: element[1] > 0, ranked_layers))
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self.layer_order = list(map(lambda element: element[0], ranked_layers))
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self.memory = torch.Tensor((max(map(lambda element: element[1].node_id, self.genes['nodes'].items())) + 1))
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def forward(self, x, memory=None):
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x_flat = self.flatten(x)
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if memory is None:
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memory = torch.Tensor(self.memory)
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outs = []
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for batch_element in x_flat:
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memory[0:self.num_input_nodes] = batch_element
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for layer_index in self.layer_order:
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memory[layer_index] = self.layers[layer_index](memory[self.indices[layer_index]])
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outs.append(memory[self.num_input_nodes: self.num_input_nodes + self.action_num * 2])
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outs = torch.stack(outs)
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self.memory = torch.Tensor(memory)
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return torch.reshape(outs, (x.shape[0], 4, 2))
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else:
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memory[:, 0:self.num_input_nodes] = x
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for layer_index in self.layer_order:
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memory[:, layer_index] = self.layers[layer_index](memory[:, self.indices[layer_index]])
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return torch.reshape(
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memory[:, self.num_input_nodes: self.num_input_nodes + self.action_num * 2],
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(x.shape[0], 4, 2))
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if __name__ == '__main__':
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sample = np.random.random((1, 486))
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model = EvolutionModel(5, 4, 4).to(device)
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print(model)
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print(model.has_recurrent)
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test = model(torch.tensor(sample, dtype=torch.float32))
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# test = test.cpu().detach().numpy()
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print(test)
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state = np.random.random((1, 486))
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target = np.random.random((4, 2))
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states = [
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[state],
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[state],
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[state],
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[state],
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]
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targets = [
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[target],
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[target],
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[target],
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[target],
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]
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optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
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from labirinth_ai.Models.BaseModel import train
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train(states, targets, model, optimizer)
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