uber-research / pytorch-neat Goto Github PK

  File "g:/Emulators/ML AI open AI/env2.py", line 130, in <module>
    run()  # pylint: disable=no-value-for-parameter
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\click\core.py", line 764, in __call__
    return self.main(*args, **kwargs)
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\click\core.py", line 717, in main
    rv = self.invoke(ctx)
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\click\core.py", line 956, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\click\core.py", line 555, in invoke
    return callback(*args, **kwargs)
  File "g:/Emulators/ML AI open AI/env2.py", line 120, in run
    winner = pop.run(eval_genomes, n_generations)
  File "g:\emulators\ml ai open ai\src\neat-python\neat\population.py", line 88, in run
    fitness_function(list(self.population.items()), self.config)
  File "g:/Emulators/ML AI open AI/env2.py", line 110, in eval_genomes
    raise e
  File "g:/Emulators/ML AI open AI/env2.py", line 106, in eval_genomes
    genome, config, debug=DEBUG and i % 100 == 0
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\tf_neat\multi_env_eval.py", line 30, in eval_genome
    net = self.make_net(genome, config, self.batch_size)
  File "g:/Emulators/ML AI open AI/env2.py", line 52, in make_net
    device="gpu",
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\tf_neat\adaptive_linear_net.py", line 149, in create
    output_activation=output_activation,
  File "C:\Users\Silver\AppData\Local\Programs\Python\Python37\lib\site-packages\tf_neat\cppn.py", line 199, in create_cppn
    assert len(leaf_names) == len(genome_config.input_keys)
AssertionError`

To add to this, I'm confused about the input coordinates and output coordinates. Is there no documentation on what this is supposed to mean? I thought possibly my NEAT config is conflicting with that but I'm seriously unsure of how to resolve it.

RuntimeError                              Traceback (most recent call last)
<ipython-input-2-621c87ec8634> in <module>
    113 
    114 if __name__ == "__main__":
--> 115     run()  # pylint: disable=no-value-for-parameter

<ipython-input-2-621c87ec8634> in run(n_generations, n_processes)
    103     pop.add_reporter(logger)
    104 
--> 105     winner = pop.run(eval_genomes, n_generations)
    106 
    107     print(f'Best Genome: {winner}')

/usr/local/lib/python3.7/dist-packages/neat/population.py in run(self, fitness_function, n)
     87 
     88             # Evaluate all genomes using the user-provided function.
---> 89             fitness_function(list(iteritems(self.population)), self.config)
     90 
     91             # Gather and report statistics.

<ipython-input-2-621c87ec8634> in eval_genomes(genomes, config)
     93                     print(genome)
     94 
---> 95                     raise e
     96 
     97     pop = neat.Population(config)

<ipython-input-2-621c87ec8634> in eval_genomes(genomes, config)
     88                 try:
     89                     genome.fitness = evaluator.eval_genome(
---> 90                         genome, config, debug=DEBUG and i % 100 == 0
     91                     )
     92                 except Exception as e:

~/AHN_experiment_Tmaze/pytorch_neat/multi_env_eval.py in eval_genome(self, genome, config, debug)
     43                     net, states, debug=True, step_num=step_num)
     44             else:
---> 45                 actions = self.activate_net(net, states)
     46             assert len(actions) == len(self.envs)
     47             for i, (env, action, done) in enumerate(zip(self.envs, actions, dones)):

<ipython-input-2-621c87ec8634> in activate_net(net, states, debug, step_num)
     41         print(net.delta_w_node)
     42         print("W init: ", net.input_to_output[0])
---> 43     outputs = net.activate(states)#.to('cpu').numpy()
     44     if debug and (step_num - 1) % 100 == 0:
     45         print(f"\nStep: {step_num - 1}")

~/AHN_experiment_Tmaze/pytorch_neat/adaptive_linear_net.py in activate(self, inputs)
    105             ).unsqueeze(2)
    106 
--> 107             outputs = self.activation(self.input_to_output.matmul(inputs))
    108 
    109             input_activs = inputs.transpose(1, 2).expand(

RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_bmm

neat-python==0.92
numpy==1.15.2+mkl
gym==0.10.5
click==6.7
torch==0.4.0
torchvision==0.2.1

def dense_from_coo(shape, conns, dtype=torch.float64):
    mat = torch.zeros(shape, dtype=dtype)
    idxs, weights = conns
    if len(idxs) == 0:
        return mat
    rows, cols = np.array(idxs).transpose()
    mat[torch.tensor(rows), torch.tensor(cols)] = torch.tensor(
        weights, dtype=dtype)
    return mat

def dense_from_coo(shape, conns, dtype=torch.float64):
    mat = torch.zeros(shape, dtype=dtype)
    idxs, weights = conns
    if len(idxs) == 0:
        return mat
    rows, cols = np.array(idxs, dtype=np.int64).transpose()
    mat[torch.tensor(rows), torch.tensor(cols)] = torch.tensor(
        weights, dtype=dtype)
    return mat

Saving checkpoint to neat-checkpoint-0
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-53-0f6fae74326a> in <module>()
     54 
     55 if __name__ == "__main__":
---> 56     run(100)

4 frames
<ipython-input-53-0f6fae74326a> in run(n_generations)
     50     pop.add_reporter(neat.Checkpointer(1))
     51 
---> 52     pop.run(eval_genomes, n_generations)
     53 
     54 

/usr/local/lib/python3.6/dist-packages/neat/population.py in run(self, fitness_function, n)
    127             self.species.speciate(self.config, self.population, self.generation)
    128 
--> 129             self.reporters.end_generation(self.config, self.population, self.species)
    130 
    131             self.generation += 1

/usr/local/lib/python3.6/dist-packages/neat/reporting.py in end_generation(self, config, population, species_set)
     34     def end_generation(self, config, population, species_set):
     35         for r in self.reporters:
---> 36             r.end_generation(config, population, species_set)
     37 
     38     def post_evaluate(self, config, population, species, best_genome):

/usr/local/lib/python3.6/dist-packages/neat/checkpoint.py in end_generation(self, config, population, species_set)
     57 
     58         if checkpoint_due:
---> 59             self.save_checkpoint(config, population, species_set, self.current_generation)
     60             self.last_generation_checkpoint = self.current_generation
     61             self.last_time_checkpoint = time.time()

/usr/local/lib/python3.6/dist-packages/neat/checkpoint.py in save_checkpoint(self, config, population, species_set, generation)
     68         with gzip.open(filename, 'w', compresslevel=5) as f:
     69             data = (generation, config, population, species_set, random.getstate())
---> 70             pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
     71 
     72     @staticmethod

TypeError: cannot serialize '_io.TextIOWrapper' object

 File "/pytorch_neat/cppn.py", line 108, in __call__
    return self.get_activs(shape)
  File "/pytorch_neat/cppn.py", line 93, in get_activs
    self.activs = self.activate(xs, shape)
  File "/pytorch_neat/cppn.py", line 87, in activate
    raise Exception("Failed to activate node {}".format(self.name))
Exception: Failed to activate node delta_w