def main(config, out_dir): if out_dir is not None: tlogger.set_log_dir(out_dir) log_dir = tlogger.log_dir() if not os.path.exists(log_dir): os.makedirs(log_dir) tlogger.info(json.dumps(config, indent=4, sort_keys=True)) tlogger.info('Logging to: {}'.format(log_dir)) Model = neuroevolution.models.__dict__[config['model']] all_tstart = time.time() def make_env(b): tlogger.info('GA: Creating environment for game: %s' % config["game"]) return gym_tensorflow.make(game=config["game"], batch_size=b) tlogger.info('GA: Creating Concurent Workers') worker = ConcurrentWorkers(make_env, Model, batch_size=64) tlogger.info('GA: Concurent Workers Created') with WorkerSession(worker) as sess: noise = SharedNoiseTable() rs = np.random.RandomState() cached_parents = [] results = [] def make_offspring(): if len(cached_parents) == 0: return worker.model.randomize(rs, noise) else: assert len(cached_parents) == config['selection_threshold'] parent = cached_parents[rs.randint(len(cached_parents))] theta, seeds = worker.model.mutate(parent, rs, noise, mutation_power=state.sample( state.mutation_power)) #print("tetha len: %d, seeds len: %d" % (len(theta), len(seeds))) return theta, seeds tlogger.info('GA: Start timing') tstart = time.time() try: load_file = os.path.join(log_dir, 'snapshot.pkl') with open(load_file, 'rb+') as file: state = pickle.load(file) tlogger.info("Loaded iteration {} from {}".format( state.it, load_file)) except FileNotFoundError: tlogger.info('Failed to load snapshot') state = TrainingState(config) if 'load_population' in config: tlogger.info('Loading population') state.copy_population(config['load_population']) # Cache first population if needed (on restart) if state.population and config['selection_threshold'] > 0: tlogger.info("Caching parents") cached_parents.clear() if state.elite in state.population[:config['selection_threshold']]: cached_parents.extend([ (worker.model.compute_weights_from_seeds(noise, o.seeds), o.seeds) for o in state.population[:config['selection_threshold']] ]) else: cached_parents.append((worker.model.compute_weights_from_seeds( noise, state.elite.seeds), state.elite.seeds)) cached_parents.extend([ (worker.model.compute_weights_from_seeds(noise, o.seeds), o.seeds) for o in state.population[:config['selection_threshold'] - 1] ]) tlogger.info("Done caching parents") while True: tstart_iteration = time.time() if state.timesteps_so_far >= config['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break frames_computed_so_far = sess.run(worker.steps_counter) assert (len(cached_parents) == 0 and state.it == 0 ) or len(cached_parents) == config['selection_threshold'] tasks = [ make_offspring() for _ in range(config['population_size']) ] for seeds, episode_reward, episode_length in worker.monitor_eval( tasks, max_frames=state.tslimit * 4): results.append( Offspring(seeds, [episode_reward], [episode_length])) state.num_frames += sess.run( worker.steps_counter) - frames_computed_so_far state.it += 1 tlogger.record_tabular('Iteration', state.it) tlogger.record_tabular('MutationPower', state.sample(state.mutation_power)) # Trim unwanted results results = results[:config['population_size']] assert len(results) == config['population_size'] rewards = np.array([a.fitness for a in results]) population_timesteps = sum([a.training_steps for a in results]) state.population = sorted(results, key=lambda x: x.fitness, reverse=True) tlogger.record_tabular('PopulationEpRewMax', np.max(rewards)) tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards)) tlogger.record_tabular('PopulationEpCount', len(rewards)) tlogger.record_tabular('PopulationTimesteps', population_timesteps) tlogger.record_tabular('NumSelectedIndividuals', config['selection_threshold']) tlogger.info('Evaluate population') validation_population = state.population[:config[ 'validation_threshold']] if state.elite is not None: validation_population = [state.elite ] + validation_population[:-1] validation_tasks = [(worker.model.compute_weights_from_seeds( noise, validation_population[x].seeds, cache=cached_parents), validation_population[x].seeds) for x in range(config['validation_threshold'])] _, population_validation, population_validation_len = zip( *worker.monitor_eval_repeated( validation_tasks, max_frames=state.tslimit * 4, num_episodes=config['num_validation_episodes'])) population_validation = [np.mean(x) for x in population_validation] population_validation_len = [ np.sum(x) for x in population_validation_len ] time_elapsed_this_iter = time.time() - tstart_iteration state.time_elapsed += time_elapsed_this_iter population_elite_idx = np.argmax(population_validation) state.elite = validation_population[population_elite_idx] elite_theta = worker.model.compute_weights_from_seeds( noise, state.elite.seeds, cache=cached_parents) _, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated( [(elite_theta, state.elite.seeds)], max_frames=None, num_episodes=config['num_test_episodes'])[0] # Log Results validation_timesteps = sum(population_validation_len) timesteps_this_iter = population_timesteps + validation_timesteps state.timesteps_so_far += timesteps_this_iter state.validation_timesteps_so_far += validation_timesteps # Log tlogger.record_tabular( 'TruncatedPopulationRewMean', np.mean([a.fitness for a in validation_population])) tlogger.record_tabular('TruncatedPopulationValidationRewMean', np.mean(population_validation)) tlogger.record_tabular('TruncatedPopulationEliteValidationRew', np.max(population_validation)) tlogger.record_tabular("TruncatedPopulationEliteIndex", population_elite_idx) tlogger.record_tabular('TruncatedPopulationEliteSeeds', state.elite.seeds) tlogger.record_tabular('TruncatedPopulationEliteTestRewMean', np.mean(population_elite_evals)) tlogger.record_tabular('TruncatedPopulationEliteTestEpCount', len(population_elite_evals)) tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum', np.sum(population_elite_evals_timesteps)) if np.mean(population_validation) > state.curr_solution_val: state.curr_solution = state.elite.seeds state.curr_solution_val = np.mean(population_validation) state.curr_solution_test = np.mean(population_elite_evals) tlogger.record_tabular('ValidationTimestepsThisIter', validation_timesteps) tlogger.record_tabular('ValidationTimestepsSoFar', state.validation_timesteps_so_far) tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter) tlogger.record_tabular( 'TimestepsPerSecondThisIter', timesteps_this_iter / (time.time() - tstart_iteration)) tlogger.record_tabular('TimestepsComputed', state.num_frames) tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far) tlogger.record_tabular('TimeElapsedThisIter', time_elapsed_this_iter) tlogger.record_tabular('TimeElapsedThisIterTotal', time.time() - tstart_iteration) tlogger.record_tabular('TimeElapsed', state.time_elapsed) tlogger.record_tabular('TimeElapsedTotal', time.time() - all_tstart) tlogger.dump_tabular() tlogger.info('Current elite: {}'.format(state.elite.seeds)) fps = state.timesteps_so_far / (time.time() - tstart) tlogger.info( 'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'. format(fps, (time.time() - all_tstart) / 3600, (config['timesteps'] - state.timesteps_so_far) / fps / 3600)) if state.adaptive_tslimit: if np.mean( [a.training_steps >= state.tslimit for a in results]) > state.incr_tslimit_threshold: state.tslimit = min( state.tslimit * state.tslimit_incr_ratio, state.tslimit_max) tlogger.info('Increased threshold to {}'.format( state.tslimit)) os.makedirs(log_dir, exist_ok=True) save_file = os.path.join(log_dir, 'snapshot.pkl') with open(save_file, 'wb+') as file: pickle.dump(state, file) #copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it))) tlogger.info("Saved iteration {} to {}".format( state.it, save_file)) if state.timesteps_so_far >= config['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break results.clear() if config['selection_threshold'] > 0: tlogger.info("Caching parents") new_parents = [] if state.elite in state.population[:config[ 'selection_threshold']]: new_parents.extend([ (worker.model.compute_weights_from_seeds( noise, o.seeds, cache=cached_parents), o.seeds) for o in state.population[:config['selection_threshold']] ]) else: new_parents.append( (worker.model.compute_weights_from_seeds( noise, state.elite.seeds, cache=cached_parents), state.elite.seeds)) new_parents.extend([ (worker.model.compute_weights_from_seeds( noise, o.seeds, cache=cached_parents), o.seeds) for o in state.population[:config['selection_threshold'] - 1] ]) cached_parents.clear() cached_parents.extend(new_parents) tlogger.info("Done caching parents") return float(state.curr_solution_test), float(state.curr_solution_val)
def main(**exp): log_dir = tlogger.log_dir() tlogger.info(json.dumps(exp, indent=4, sort_keys=True)) tlogger.info('Logging to: {}'.format(log_dir)) Model = neuroevolution.models.__dict__[exp['model']] all_tstart = time.time() def make_env(b): return gym_tensorflow.make(game=exp["game"], batch_size=b) worker = ConcurrentWorkers(make_env, Model, batch_size=64) with WorkerSession(worker) as sess: noise = SharedNoiseTable() rs = np.random.RandomState() tlogger.info('Start timing') tstart = time.time() try: load_file = os.path.join(log_dir, 'snapshot.pkl') with open(load_file, 'rb+') as file: state = pickle.load(file) tlogger.info("Loaded iteration {} from {}".format( state.it, load_file)) except FileNotFoundError: tlogger.info('Failed to load snapshot') state = TrainingState(exp) if 'load_from' in exp: dirname = os.path.join(os.path.dirname(__file__), '..', 'neuroevolution', 'ga_legacy.py') load_from = exp['load_from'].format(**exp) os.system('python {} {} seeds.pkl'.format(dirname, load_from)) with open('seeds.pkl', 'rb+') as file: seeds = pickle.load(file) state.set_theta( worker.model.compute_weights_from_seeds(noise, seeds)) tlogger.info('Loaded initial theta from {}'.format(load_from)) else: state.initialize(rs, noise, worker.model) def make_offspring(state): for i in range(exp['population_size'] // 2): idx = noise.sample_index(rs, worker.model.num_params) mutation_power = state.sample(state.mutation_power) pos_theta = worker.model.compute_mutation( noise, state.theta, idx, mutation_power) yield (pos_theta, idx) neg_theta = worker.model.compute_mutation( noise, state.theta, idx, -mutation_power) diff = (np.max( np.abs((pos_theta + neg_theta) / 2 - state.theta))) assert diff < 1e-5, 'Diff too large: {}'.format(diff) yield (neg_theta, idx) tlogger.info('Start training') _, initial_performance, _ = worker.monitor_eval_repeated( [(state.theta, 0)], max_frames=None, num_episodes=exp['num_test_episodes'])[0] while True: tstart_iteration = time.time() if state.timesteps_so_far >= exp['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break frames_computed_so_far = sess.run(worker.steps_counter) tlogger.info('Evaluating perturbations') iterator = iter( worker.monitor_eval(make_offspring(state), max_frames=state.tslimit * 4)) results = [] for pos_seeds, pos_reward, pos_length in iterator: neg_seeds, neg_reward, neg_length = next(iterator) assert pos_seeds == neg_seeds results.append( Offspring(pos_seeds, [pos_reward, neg_reward], [pos_length, neg_length])) state.num_frames += sess.run( worker.steps_counter) - frames_computed_so_far state.it += 1 tlogger.record_tabular('Iteration', state.it) tlogger.record_tabular('MutationPower', state.sample(state.mutation_power)) tlogger.record_tabular('TimestepLimitPerEpisode', state.tslimit) # Trim unwanted results results = results[:exp['population_size'] // 2] assert len(results) == exp['population_size'] // 2 rewards = np.array([b for a in results for b in a.rewards]) results_timesteps = np.array([a.training_steps for a in results]) timesteps_this_iter = sum([a.training_steps for a in results]) state.timesteps_so_far += timesteps_this_iter tlogger.record_tabular('PopulationEpRewMax', np.max(rewards)) tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards)) tlogger.record_tabular('PopulationEpRewMedian', np.median(rewards)) tlogger.record_tabular('PopulationEpCount', len(rewards)) tlogger.record_tabular('PopulationTimesteps', timesteps_this_iter) # Update Theta returns_n2 = np.array([a.rewards for a in results]) noise_inds_n = [a.seeds for a in results] if exp['return_proc_mode'] == 'centered_rank': proc_returns_n2 = compute_centered_ranks(returns_n2) else: raise NotImplementedError(exp['return_proc_mode']) # Compute and take step g, count = batched_weighted_sum( proc_returns_n2[:, 0] - proc_returns_n2[:, 1], (noise.get(idx, worker.model.num_params) for idx in noise_inds_n), batch_size=500) # NOTE: gradients are scaled by \theta g /= returns_n2.size assert g.shape == ( worker.model.num_params, ) and g.dtype == np.float32 and count == len(noise_inds_n) update_ratio, state.theta = state.optimizer.update(-g + exp['l2coeff'] * state.theta) time_elapsed_this_iter = time.time() - tstart_iteration state.time_elapsed += time_elapsed_this_iter tlogger.info('Evaluate elite') _, test_evals, test_timesteps = worker.monitor_eval_repeated( [(state.theta, 0)], max_frames=None, num_episodes=exp['num_test_episodes'])[0] test_timesteps = sum(test_timesteps) # Log Results tlogger.record_tabular('TestRewMean', np.mean(test_evals)) tlogger.record_tabular('TestRewMedian', np.median(test_evals)) tlogger.record_tabular('TestEpCount', len(test_evals)) tlogger.record_tabular('TestEpLenSum', test_timesteps) tlogger.record_tabular('InitialRewMax', np.max(initial_performance)) tlogger.record_tabular('InitialRewMean', np.mean(initial_performance)) tlogger.record_tabular('InitialRewMedian', np.median(initial_performance)) tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter) tlogger.record_tabular( 'TimestepsPerSecondThisIter', timesteps_this_iter / (time.time() - tstart_iteration)) tlogger.record_tabular('TimestepsComputed', state.num_frames) tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far) tlogger.record_tabular('TimeElapsedThisIter', time_elapsed_this_iter) tlogger.record_tabular('TimeElapsedThisIterTotal', time.time() - tstart_iteration) tlogger.record_tabular('TimeElapsed', state.time_elapsed) tlogger.record_tabular('TimeElapsedTotal', time.time() - all_tstart) tlogger.dump_tabular() fps = state.timesteps_so_far / (time.time() - tstart) tlogger.info( 'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'. format(fps, (time.time() - all_tstart) / 3600, (exp['timesteps'] - state.timesteps_so_far) / fps / 3600)) if state.adaptive_tslimit: if np.mean( [a.training_steps >= state.tslimit for a in results]) > state.incr_tslimit_threshold: state.tslimit = min( state.tslimit * state.tslimit_incr_ratio, state.tslimit_max) tlogger.info('Increased threshold to {}'.format( state.tslimit)) os.makedirs(log_dir, exist_ok=True) save_file = os.path.join(log_dir, 'snapshot.pkl') with open(save_file, 'wb+') as file: pickle.dump(state, file) #copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it))) tlogger.info("Saved iteration {} to {}".format( state.it, save_file)) if state.timesteps_so_far >= exp['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break results.clear()
def run_model(self): self.build_model() mnist = input_data.read_data_sets(self.data_dir, one_hot=True) log_dir = self.log_dir + '/Flip{}batch{}scale{}lr{}'.format( self.isFlip, self.batch_size, self.scale, self.learning_rate) tlogger.start(log_dir) for k, v in self.args.__dict__.items(): tlogger.log('{}: {}'.format(k, v)) with tf.Session() as sess: # merged = tf.summary.merge_all() # train_writer = tf.summary.FileWriter(self.log_dir + '/Flip{}train{}scale{}lr{}' # .format(self.isFlip, self.batch_size, self.scale, self.learning_rate), sess.graph) # test_writer = tf.summary.FileWriter(self.log_dir + '/Flip{}test{}scale{}lr{}' # .format(self.isFlip, self.batch_size, self.scale, self.learning_rate)) # saver = tf.train.Saver(max_to_keep=40) sess.run(tf.global_variables_initializer()) # M = mnist.train.labels.shape[0] // self.batch_size M = 55000 tstart = time.time() for i in range(self.args.num_iterations): start = time.time() if self.args.lr_decay: step_size = self.piecewise_learning_rate(i) sess.run(tf.assign(self.learning_rate, step_size)) batch = mnist.train.next_batch(self.batch_size) _, train_KL, train_accuracy, train_loss, train_cross = sess.run( [ self.train_step, self.KL, self.accuracy, self.loss, self.cross_entropy ], feed_dict={ self.x: batch[0], self.y_: batch[1], self.M: M, self.n: batch[0].shape[0], self.isTrain: True }) if i % 100 == 0: # train_writer.add_summary(train_summary, i) tlogger.log('********** Iteration {} **********'.format(i)) tlogger.record_tabular("train_loss", train_loss) tlogger.record_tabular("train_cross", train_cross) tlogger.record_tabular("train_KL", train_KL) tlogger.record_tabular("train_acc", train_accuracy) # print('Train accuracy, Loss at step %s: %s, %s' % (i, train_accuracy, train_loss)) xs, ys = mnist.test.images, mnist.test.labels test_accuracy, test_loss, test_KL, test_cross = sess.run( [ self.accuracy, self.loss, self.KL, self.cross_entropy ], feed_dict={ self.x: xs, self.y_: ys, self.M: M, self.n: xs.shape[0], self.isTrain: False }) # test_writer.add_summary(test_summary, i) # print('Test accuracy at step %s: %s' % (i, test_accuracy)) tlogger.record_tabular("test_loss", test_loss) tlogger.record_tabular("test_cross", test_cross) tlogger.record_tabular("test_KL", test_KL) tlogger.record_tabular("test_acc", test_accuracy) tlogger.record_tabular("TimeElapsed", time.time() - tstart) tlogger.dump_tabular() tlogger.stop()
def main(exp, log_dir): log_dir = tlogger.log_dir(log_dir) snap_idx = 0 snapshots = [] tlogger.info(json.dumps(exp, indent=4, sort_keys=True)) tlogger.info('Logging to: {}'.format(log_dir)) Model = neuroevolution.models.__dict__[exp['model']] all_tstart = time.time() def make_env(b): return gym_tensorflow.make(game=exp["game"], batch_size=b) worker = ConcurrentWorkers(make_env, Model, batch_size=64) with WorkerSession(worker) as sess: rs = np.random.RandomState() noise = None state = None cached_parents = [] results = [] def make_offspring(): if len(cached_parents) == 0: return worker.model.randomize(rs, noise) else: assert len(cached_parents) == exp['selection_threshold'] parent = cached_parents[rs.randint(len(cached_parents))] return worker.model.mutate(parent, rs, noise, mutation_power=state.sample( state.mutation_power)) tlogger.info('Start timing') tstart = time.time() load_file = os.path.join(log_dir, 'snapshot.pkl') if 'load_from' in exp: filename = os.path.join(log_dir, exp['load_from']) with open(filename, 'rb+') as file: state = pickle.load(file) state.timesteps_so_far = 0 # Reset timesteps to 0 state.it = 0 state.max_reward = 0 state.max_avg = 0 state.max_sd = 0 tlogger.info('Loaded initial policy from {}'.format(filename)) elif os.path.exists(load_file): try: with open(load_file, 'rb+') as file: state = pickle.load(file) tlogger.info("Loaded iteration {} from {}".format( state.it, load_file)) except FileNotFoundError: tlogger.info('Failed to load snapshot') if not noise: tlogger.info("Generating new noise table") noise = SharedNoiseTable() else: tlogger.info("Using noise table from snapshot") if not state: tlogger.info("Generation new TrainingState") state = TrainingState(exp) if 'load_population' in exp: state.copy_population(exp['load_population']) # Cache first population if needed (on restart) if state.population and exp['selection_threshold'] > 0: tlogger.info("Caching parents") cached_parents.clear() if state.elite in state.population[:exp['selection_threshold']]: cached_parents.extend([ (worker.model.compute_weights_from_seeds(noise, o.seeds), o.seeds) for o in state.population[:exp['selection_threshold']] ]) else: cached_parents.append((worker.model.compute_weights_from_seeds( noise, state.elite.seeds), state.elite.seeds)) cached_parents.extend([ (worker.model.compute_weights_from_seeds(noise, o.seeds), o.seeds) for o in state.population[:exp['selection_threshold'] - 1] ]) tlogger.info("Done caching parents") while True: tstart_iteration = time.time() if state.timesteps_so_far >= exp['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break frames_computed_so_far = sess.run(worker.steps_counter) assert (len(cached_parents) == 0 and state.it == 0) or len(cached_parents) == exp['selection_threshold'] tasks = [make_offspring() for _ in range(exp['population_size'])] for seeds, episode_reward, episode_length in worker.monitor_eval( tasks, max_frames=state.tslimit * 4): results.append( Offspring(seeds, [episode_reward], [episode_length])) state.num_frames += sess.run( worker.steps_counter) - frames_computed_so_far state.it += 1 tlogger.record_tabular('Iteration', state.it) tlogger.record_tabular('MutationPower', state.sample(state.mutation_power)) # Trim unwanted results results = results[:exp['population_size']] assert len(results) == exp['population_size'] rewards = np.array([a.fitness for a in results]) population_timesteps = sum([a.training_steps for a in results]) state.population = sorted(results, key=lambda x: x.fitness, reverse=True) state.max_reward = save_best_pop_member(state.max_reward, np.max(rewards), state, state.population[0]) tlogger.record_tabular('PopulationEpRewMax', np.max(rewards)) tlogger.record_tabular('PopulationEpRewMean', np.mean(rewards)) tlogger.record_tabular('PopulationEpCount', len(rewards)) tlogger.record_tabular('PopulationTimesteps', population_timesteps) tlogger.record_tabular('NumSelectedIndividuals', exp['selection_threshold']) tlogger.info('Evaluate population') validation_population = state.population[:exp[ 'validation_threshold']] if state.elite is not None: validation_population = [state.elite ] + validation_population[:-1] validation_tasks = [(worker.model.compute_weights_from_seeds( noise, validation_population[x].seeds, cache=cached_parents), validation_population[x].seeds) for x in range(exp['validation_threshold'])] _, population_validation, population_validation_len = zip( *worker.monitor_eval_repeated( validation_tasks, max_frames=state.tslimit * 4, num_episodes=exp['num_validation_episodes'])) it_max_avg = np.max([np.mean(x) for x in population_validation]) it_max_sd = np.max([np.std(x) for x in population_validation]) state.max_avg = np.max([state.max_avg, it_max_avg]) state.max_sd = np.max([state.max_sd, it_max_sd]) tlogger.info("Max Average: {}".format(state.max_avg)) tlogger.info("Max Std: {}".format(state.max_sd)) fitness_results = [(np.mean(x), np.std(x)) for x in population_validation] with open(os.path.join(log_dir, 'fitness.log'), 'a') as f: f.write("{},{},{}: {}\n".format( state.it, state.max_avg, state.max_sd, ','.join([ "({},{})".format(x[0], x[1]) for x in fitness_results ]))) population_fitness = [ fitness(x[0], x[1], state.max_avg, state.max_sd) for x in fitness_results ] tlogger.info("Fitness: {}".format(population_fitness)) population_validation_len = [ np.sum(x) for x in population_validation_len ] time_elapsed_this_iter = time.time() - tstart_iteration state.time_elapsed += time_elapsed_this_iter population_elite_idx = np.argmin(population_fitness) state.elite = validation_population[population_elite_idx] elite_theta = worker.model.compute_weights_from_seeds( noise, state.elite.seeds, cache=cached_parents) _, population_elite_evals, population_elite_evals_timesteps = worker.monitor_eval_repeated( [(elite_theta, state.elite.seeds)], max_frames=None, num_episodes=exp['num_test_episodes'])[0] # Log Results validation_timesteps = sum(population_validation_len) timesteps_this_iter = population_timesteps + validation_timesteps state.timesteps_so_far += timesteps_this_iter state.validation_timesteps_so_far += validation_timesteps # Log tlogger.record_tabular( 'TruncatedPopulationRewMean', np.mean([a.fitness for a in validation_population])) tlogger.record_tabular('TruncatedPopulationValidationFitMean', np.mean(population_fitness)) tlogger.record_tabular('TruncatedPopulationValidationFitMax', np.max(population_fitness)) tlogger.record_tabular('TruncatedPopulationValidationFitMin', np.min(population_fitness)) tlogger.record_tabular('TruncatedPopulationValidationMaxAvg', state.max_avg) tlogger.record_tabular('TruncatedPopulationValidationMaxStd', state.max_sd) tlogger.record_tabular('TruncatedPopulationEliteValidationFitMin', np.min(population_fitness)) tlogger.record_tabular("TruncatedPopulationEliteIndex", population_elite_idx) tlogger.record_tabular('TruncatedPopulationEliteSeeds', state.elite.seeds) tlogger.record_tabular('TruncatedPopulationEliteTestRewMean', np.mean(population_elite_evals)) tlogger.record_tabular('TruncatedPopulationEliteTestRewStd', np.std(population_elite_evals)) tlogger.record_tabular('TruncatedPopulationEliteTestEpCount', len(population_elite_evals)) tlogger.record_tabular('TruncatedPopulationEliteTestEpLenSum', np.sum(population_elite_evals_timesteps)) if np.mean(population_validation) > state.curr_solution_val: state.curr_solution = state.elite.seeds state.curr_solution_val = np.mean(population_validation) state.curr_solution_test = np.mean(population_elite_evals) tlogger.record_tabular('ValidationTimestepsThisIter', validation_timesteps) tlogger.record_tabular('ValidationTimestepsSoFar', state.validation_timesteps_so_far) tlogger.record_tabular('TimestepsThisIter', timesteps_this_iter) tlogger.record_tabular( 'TimestepsPerSecondThisIter', timesteps_this_iter / (time.time() - tstart_iteration)) tlogger.record_tabular('TimestepsComputed', state.num_frames) tlogger.record_tabular('TimestepsSoFar', state.timesteps_so_far) tlogger.record_tabular('TimeElapsedThisIter', time_elapsed_this_iter) tlogger.record_tabular('TimeElapsedThisIterTotal', time.time() - tstart_iteration) tlogger.record_tabular('TimeElapsed', state.time_elapsed) tlogger.record_tabular('TimeElapsedTotal', time.time() - all_tstart) tlogger.dump_tabular() # tlogger.info('Current elite: {}'.format(state.elite.seeds)) fps = state.timesteps_so_far / (time.time() - tstart) tlogger.info( 'Timesteps Per Second: {:.0f}. Elapsed: {:.2f}h ETA {:.2f}h'. format(fps, (time.time() - all_tstart) / 3600, (exp['timesteps'] - state.timesteps_so_far) / fps / 3600)) if state.adaptive_tslimit: if np.mean( [a.training_steps >= state.tslimit for a in results]) > state.incr_tslimit_threshold: state.tslimit = min( state.tslimit * state.tslimit_incr_ratio, state.tslimit_max) tlogger.info('Increased threshold to {}'.format( state.tslimit)) snap_idx, snapshots = save_snapshot(state, log_dir, snap_idx, snapshots) # os.makedirs(log_dir, exist_ok=True) # copyfile(save_file, os.path.join(log_dir, 'snapshot_gen{:04d}.pkl'.format(state.it))) tlogger.info("Saved iteration {} to {}".format( state.it, snapshots[snap_idx - 1])) if state.timesteps_so_far >= exp['timesteps']: tlogger.info('Training terminated after {} timesteps'.format( state.timesteps_so_far)) break results.clear() if exp['selection_threshold'] > 0: tlogger.info("Caching parents") new_parents = [] if state.elite in state.population[: exp['selection_threshold']]: new_parents.extend([ (worker.model.compute_weights_from_seeds( noise, o.seeds, cache=cached_parents), o.seeds) for o in state.population[:exp['selection_threshold']] ]) else: new_parents.append( (worker.model.compute_weights_from_seeds( noise, state.elite.seeds, cache=cached_parents), state.elite.seeds)) new_parents.extend([ (worker.model.compute_weights_from_seeds( noise, o.seeds, cache=cached_parents), o.seeds) for o in state.population[:exp['selection_threshold'] - 1] ]) cached_parents.clear() cached_parents.extend(new_parents) tlogger.info("Done caching parents") return float(state.curr_solution_test), { 'val': float(state.curr_solution_val) }
def main( num_inner_iterations=64, noise_size=128, inner_loop_init_lr=0.2, inner_loop_init_momentum=0.5, training_iterations_schedule=5, min_training_iterations=4, lr=0.1, rms_momentum=0.9, final_relative_lr=1e-2, generator_batch_size=128, meta_batch_size=512, adam_epsilon=1e-8, adam_beta1=0.0, adam_beta2=0.999, num_meta_iterations=1000, starting_meta_iteration=1, max_elapsed_time=None, gradient_block_size=16, use_intermediate_losses=0, intermediate_losses_ratio=1.0, data_path='./data', meta_optimizer="adam", dataset='MNIST', logging_period=10, generator_type="cgtn", learner_type="base", validation_learner_type=None, warmup_iterations=None, warmup_learner="base", final_batch_norm_forward=False, # The following flag is used for architecture search (it maps iteration to a specific architecture) iteration_maps_seed=False, use_dataset_augmentation=False, training_schedule_backwards=True, evenly_distributed_labels=True, iterations_depth_schedule=100, use_encoder=True, decoder_loss_multiplier=1.0, load_from=None, virtual_batch_size=1, deterministic=False, seed=1, grad_bound=None, version=None, # dummy variable enable_checkpointing=True, randomize_width=False, step_by_step_validation=True, semisupervised_classifier_loss=True, semisupervised_student_loss=True, automl_class=None, inner_loop_optimizer="SGD", meta_learn_labels=False, device='cuda'): validation_learner_type = validation_learner_type or learner_type hvd.init() assert hvd.mpi_threads_supported() from mpi4py import MPI torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True lr = lr * virtual_batch_size * hvd.size() torch.cuda.set_device(hvd.local_rank()) # Load dataset img_shape, trainset, validationset, (testset_x, testset_y) = get_dataset( dataset, data_path, seed, device, with_augmentation=use_dataset_augmentation) validation_x, validation_y = zip(*validationset) validation_x = torch.stack(validation_x).to(device) validation_y = torch.as_tensor(validation_y).to(device) # Make each worker slightly different torch.manual_seed(seed + hvd.rank()) np.random.seed(seed + hvd.rank()) if generator_type == "semisupervised": unlabelled_trainset, trainset = torch.utils.data.random_split( trainset, [49500, 500]) data_loader = torch.utils.data.DataLoader(trainset, batch_size=meta_batch_size, shuffle=True, drop_last=True, num_workers=1, pin_memory=True) data_loader = EndlessDataLoader(data_loader) if generator_type == "cgtn": generator = CGTN( generator=Generator(noise_size + 10, img_shape), num_inner_iterations=num_inner_iterations, generator_batch_size=generator_batch_size, noise_size=noise_size, evenly_distributed_labels=evenly_distributed_labels, meta_learn_labels=bool(meta_learn_labels), ) elif generator_type == "cgtn_all_shuffled": generator = CGTNAllShuffled( generator=Generator(noise_size + 10, img_shape), num_inner_iterations=num_inner_iterations, generator_batch_size=generator_batch_size, noise_size=noise_size, evenly_distributed_labels=evenly_distributed_labels, ) elif generator_type == "cgtn_batch_shuffled": generator = CGTNBatchShuffled( generator=Generator(noise_size + 10, img_shape), num_inner_iterations=num_inner_iterations, generator_batch_size=generator_batch_size, noise_size=noise_size, evenly_distributed_labels=evenly_distributed_labels, ) elif generator_type == "gtn": generator = GTN( generator=Generator(noise_size + 10, img_shape), generator_batch_size=generator_batch_size, noise_size=noise_size, ) elif generator_type == "gaussian_cgtn": generator = GaussianCGTN( generator=Generator(noise_size + 10, img_shape), num_inner_iterations=num_inner_iterations, generator_batch_size=generator_batch_size, noise_size=noise_size, ) elif generator_type == "dataset": generator = UniformSamplingGenerator( torch.utils.data.DataLoader(trainset, batch_size=generator_batch_size, shuffle=True, drop_last=True), num_inner_iterations=num_inner_iterations, device=device, ) elif generator_type == "distillation": generator = DatasetDistillation( img_shape=img_shape, num_inner_iterations=num_inner_iterations, generator_batch_size=generator_batch_size, ) elif generator_type == "semisupervised": generator = SemisupervisedGenerator( torch.utils.data.DataLoader(unlabelled_trainset, batch_size=generator_batch_size, shuffle=True, drop_last=True), num_inner_iterations=num_inner_iterations, device=device, classifier=models.ClassifierLarger2(img_shape, batch_norm_momentum=0.9, randomize_width=False)) else: raise NotImplementedError() # Create meta-objective models if inner_loop_optimizer == "SGD": optimizers = [ inner_optimizers.SGD(inner_loop_init_lr, inner_loop_init_momentum, num_inner_iterations) ] elif inner_loop_optimizer == "RMSProp": optimizers = [ inner_optimizers.RMSProp(inner_loop_init_lr, inner_loop_init_momentum, num_inner_iterations) ] elif inner_loop_optimizer == "Adam": optimizers = [ inner_optimizers.Adam(inner_loop_init_lr, inner_loop_init_momentum, num_inner_iterations) ] else: raise ValueError( f"Inner loop optimizer '{inner_loop_optimizer}' not available") automl = (automl_class or AutoML)( generator=generator, optimizers=optimizers, ) if use_encoder: automl.encoder = Encoder(img_shape, output_size=noise_size) automl = automl.to(device) if meta_optimizer == "adam": optimizer = torch.optim.Adam(automl.parameters(), lr=lr, betas=(adam_beta1, adam_beta2), eps=adam_epsilon) elif meta_optimizer == "sgd": optimizer = torch.optim.SGD(automl.parameters(), lr=lr, momentum=rms_momentum) elif meta_optimizer == "RMS": optimizer = torch.optim.RMSprop(automl.parameters(), lr=lr, alpha=adam_beta1, momentum=rms_momentum, eps=adam_epsilon) else: raise NotImplementedError() scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, num_meta_iterations, lr * final_relative_lr) if hvd.rank() == 0: if load_from: state = torch.load(load_from) automl.load_state_dict(state["model"]) if lr > 0: optimizer.load_state_dict(state["optimizer"]) del state tlogger.info("loaded from:", load_from) total_num_parameters = 0 for name, value in automl.named_parameters(): tlogger.info("Optimizing parameter:", name, value.shape) total_num_parameters += np.prod(value.shape) tlogger.info("Total number of parameters:", int(total_num_parameters)) def compute_learner(learner, iterations=num_inner_iterations, keep_grad=True, callback=None): learner.model.train() names, params = list(zip(*learner.model.get_parameters())) buffers = list(zip(*learner.model.named_buffers())) if buffers: buffer_names, buffers = buffers else: buffer_names, buffers = None, () param_shapes = [p.shape for p in params] param_sizes = [np.prod(shape) for shape in param_shapes] param_end_point = np.cumsum(param_sizes) buffer_shapes = [p.shape for p in buffers] buffer_sizes = [np.prod(shape) for shape in buffer_shapes] buffer_end_point = np.cumsum(buffer_sizes) def split_params(fused_params): # return fused_params assert len(fused_params) == 1 return [ fused_params[0][end - size:end].reshape(shape) for end, size, shape in zip(param_end_point, param_sizes, param_shapes) ] def split_buffer(fused_params): if fused_params: # return fused_params assert len(fused_params) == 1 return [ fused_params[0][end - size:end].reshape(shape) for end, size, shape in zip(buffer_end_point, buffer_sizes, buffer_shapes) ] return fused_params # test = split_params(torch.cat([p.flatten() for p in params])) # assert all([np.allclose(params[i].detach().cpu(), test[i].detach().cpu()) for i in range(len(test))]) params = [torch.cat([p.flatten() for p in params])] buffers = [torch.cat([p.flatten() for p in buffers])] if buffers else buffers optimizer_state = learner.optimizer.initial_state(params) params = params, buffers initial_params = nest.map_structure(lambda p: None, params) losses = {} accuracies = {} def intermediate_loss(params): params = nest.pack_sequence_as(initial_params, params[1:]) params, buffers = params x, y = next(meta_generator) x = x.to(device, non_blocking=True) y = y.to(device, non_blocking=True) learner.model.set_parameters(list(zip(names, split_params(params)))) if buffer_names: learner.model.set_buffers( list(zip(buffer_names, split_buffer(buffers)))) learner.model.eval() pred = learner.model(x) if isinstance(pred, tuple): pred, aux_pred = pred loss = F.nll_loss(pred, y) + F.nll_loss(aux_pred, y) else: loss = F.nll_loss(pred, y) return loss * intermediate_losses_ratio if hasattr(automl.generator, "init"): generator_args = [automl.generator.init()] else: generator_args = [] def body(args): it, params, optimizer_state = args if training_schedule_backwards: x, y_one_hot = automl.generator(iterations - it - 1, *generator_args) else: x, y_one_hot = automl.generator(it, *generator_args) with torch.enable_grad(): if use_intermediate_losses > 0 and ( it >= use_intermediate_losses and it % use_intermediate_losses == 0): params = SurrogateLoss.apply(intermediate_loss, it, *nest.flatten(params)) params = nest.pack_sequence_as(initial_params, params[1:]) params, buffers = params for p in params: if not p.requires_grad: p.requires_grad = True learner.model.set_parameters( list(zip(names, split_params(params)))) if buffer_names: learner.model.set_buffers( list(zip(buffer_names, split_buffer(buffers)))) learner.model.train() output = learner.model(x) if isinstance(output, tuple): output1, output2 = output loss = -(output1 * y_one_hot).sum() * (1 / output1.shape[0]) loss = loss - (output2 * y_one_hot).sum() * (1 / output2.shape[0]) pred = output1 else: loss = -(output * y_one_hot).sum() * (1 / output.shape[0]) pred = output if it.item() not in losses: losses[it.item()] = loss.detach().cpu().item() accuracies[it.item()] = ( pred.max(-1).indices == y_one_hot.max(-1).indices).to( torch.float).mean().item() grads = grad(loss, params, create_graph=x.requires_grad, allow_unused=True) # assert len(grads) == len(names) new_params, optimizer_state = learner.optimizer( it, params, grads, optimizer_state) buffers = list(learner.model.buffers()) buffers = [torch.cat([b.flatten() for b in buffers])] if buffers else buffers if callback is not None: learner.model.set_parameters( list(zip(names, split_params(params)))) if buffer_names: learner.model.set_buffers( list(zip(buffer_names, split_buffer(buffers)))) callback(learner) return (it + 1, ( new_params, buffers, ), optimizer_state) last_state, params, optimizer_state = gradient_checkpointing( (torch.as_tensor(0), params, optimizer_state), body, iterations, block_size=gradient_block_size) assert last_state.item() == iterations params, buffers = params learner.model.set_parameters(list(zip(names, split_params(params)))) if buffer_names: learner.model.set_buffers( list(zip(buffer_names, split_buffer(buffers)))) if final_batch_norm_forward: x, _ = automl.generator(torch.randint(iterations, size=())) learner.model.train() learner.model(x) return learner, losses, accuracies tstart = time.time() meta_generator = iter(data_loader) hvd.broadcast_parameters(automl.state_dict(), root_rank=0) best_optimizers = {} validation_accuracy = None total_inner_iterations_so_far = 0 for iteration in range(starting_meta_iteration, num_meta_iterations + 1): last_iteration = time.time() # basic logging tlogger.record_tabular('Iteration', iteration) tlogger.record_tabular('lr', optimizer.param_groups[0]['lr']) # Train learner if training_iterations_schedule > 0: training_iterations = int( min( num_inner_iterations, min_training_iterations + (iteration - starting_meta_iteration) // training_iterations_schedule)) else: training_iterations = num_inner_iterations tlogger.record_tabular('training_iterations', training_iterations) total_inner_iterations_so_far += training_iterations tlogger.record_tabular('training_iterations_so_far', total_inner_iterations_so_far * hvd.size()) optimizer.zero_grad() for _ in range(virtual_batch_size): torch.cuda.empty_cache() meta_x, meta_y = next(meta_generator) meta_x = meta_x.to('cuda', non_blocking=True) meta_y = meta_y.to('cuda', non_blocking=True) tstart_forward = time.time() if generator_type != "semisupervised" or semisupervised_student_loss: # TODO: Learn batchnorm momentum and eps sample_learner_type = learner_type if warmup_iterations is not None and iteration < warmup_iterations: sample_learner_type = warmup_learner learner, encoding = automl.sample_learner( img_shape, device, allow_nas=False, randomize_width=randomize_width, learner_type=sample_learner_type, iteration_maps_seed=iteration_maps_seed, iteration=iteration, deterministic=deterministic, iterations_depth_schedule=iterations_depth_schedule) automl.train() if lr == 0.0: with torch.no_grad(): learner, intermediate_losses, intermediate_accuracies = compute_learner( learner, iterations=training_iterations, keep_grad=lr > 0.0) else: learner, intermediate_losses, intermediate_accuracies = compute_learner( learner, iterations=training_iterations, keep_grad=lr > 0.0) # TODO: remove this requirement params = list(learner.model.get_parameters()) learner.model.eval() # Evaluate learner on training and back-prop torch.cuda.empty_cache() pred = learner.model(meta_x) if isinstance(pred, tuple): pred, aux_pred = pred loss = F.nll_loss(pred, meta_y) + F.nll_loss( aux_pred, meta_y) else: loss = F.nll_loss(pred, meta_y) accuracy = (pred.max(-1).indices == meta_y).to( torch.float).mean() tlogger.record_tabular("TimeElapsedForward", time.time() - tstart_forward) num_parameters = sum([a[1].size().numel() for a in params]) tlogger.record_tabular("TrainingLearnerParameters", num_parameters) tlogger.record_tabular("optimizer", type(learner.optimizer).__name__) tlogger.record_tabular('meta_training_loss', loss.item()) tlogger.record_tabular('meta_training_accuracy', accuracy.item()) tlogger.record_tabular('training_accuracies', intermediate_accuracies) tlogger.record_tabular('training_losses', intermediate_losses) tlogger.record_tabular("dag", encoding) else: loss = torch.as_tensor(0.0) if lr > 0.0: tstart_backward = time.time() if generator_type != "semisupervised" or semisupervised_student_loss: loss.backward() if generator_type == "semisupervised" and semisupervised_classifier_loss: automl.generator.classifier.train() pred = automl.generator.classifier(meta_x) accuracy = (pred.max(-1).indices == meta_y).to( torch.float).mean() loss2 = F.nll_loss(pred, meta_y) loss2.backward() tlogger.record_tabular('meta_training_generator_loss', loss2.item()) tlogger.record_tabular('meta_training_generator_accuracy', accuracy.item()) loss = loss + loss2 del loss2 tlogger.record_tabular("TimeElapsedBackward", time.time() - tstart_backward) if use_encoder: # TODO: add loss weight meta_encoding = automl.encoder(meta_x) meta_y_one_hot = torch.zeros(meta_x.shape[0], 10, device=device) meta_y_one_hot.scatter_(1, meta_y.unsqueeze(-1), 1) meta_encoding = torch.cat([meta_encoding, meta_y_one_hot], -1) reconstruct = automl.generator.generator(meta_encoding) ae_loss = decoder_loss_multiplier * F.mse_loss( reconstruct, meta_x) ae_loss.backward() tlogger.record_tabular("decoder_loss", ae_loss.item()) if lr > 0.0: # If using distributed training aggregard gradients with Horovod maybe_allreduce_grads(automl) if grad_bound is not None: nn.utils.clip_grad_norm_(automl.parameters(), grad_bound) optimizer.step() if max_elapsed_time is not None: scheduler.step( round((time.time() - tstart) / max_elapsed_time * num_meta_iterations)) else: scheduler.step(iteration - 1) is_last_iteration = iteration == num_meta_iterations or ( max_elapsed_time is not None and time.time() - tstart > max_elapsed_time) if np.isnan(loss.item()): tlogger.info("NaN training loss, terminating") is_last_iteration = True is_last_iteration = MPI.COMM_WORLD.bcast(is_last_iteration, root=0) if iteration == 1 or iteration % logging_period == 0 or is_last_iteration: tstart_validation = time.time() val_loss, val_accuracy = [], [] test_loss, test_accuracy = [], [] if generator_type == "semisupervised": # Validation set evaluate_set(generator.classifier, validation_x, validation_y, "generator_validation") # Test set evaluate_set(generator.classifier, testset_x, testset_y, "generator_test") else: def compute_learner_callback(learner): # Validation set validation_loss, single_validation_accuracy, validation_accuracy = evaluate_set( learner.model, validation_x, validation_y, "validation") val_loss.append(validation_loss) val_accuracy.append(validation_accuracy) best_optimizers[type( learner.optimizer ).__name__] = single_validation_accuracy.item() # Test set loss, _, accuracy = evaluate_set(learner.model, testset_x, testset_y, "test") test_loss.append(loss) test_accuracy.append(accuracy) tlogger.info( "sampling another learner_type ({}) for validation".format( validation_learner_type)) learner, _ = automl.sample_learner( img_shape, device, allow_nas=False, learner_type=validation_learner_type, iteration_maps_seed=iteration_maps_seed, iteration=iteration, deterministic=deterministic, iterations_depth_schedule=iterations_depth_schedule) if step_by_step_validation: compute_learner_callback(learner) with torch.no_grad(): learner, _, _ = compute_learner( learner, iterations=training_iterations, keep_grad=False, callback=compute_learner_callback if step_by_step_validation else None) if not step_by_step_validation: compute_learner_callback(learner) tlogger.record_tabular("validation_losses", val_loss) tlogger.record_tabular("validation_accuracies", val_accuracy) validation_accuracy = val_accuracy[-1] tlogger.record_tabular("test_losses", test_loss) tlogger.record_tabular("test_accuracies", test_accuracy) # Extra logging tlogger.record_tabular('TimeElapsedIter', (tstart_validation - last_iteration) / virtual_batch_size) tlogger.record_tabular('TimeElapsedValidation', time.time() - tstart_validation) tlogger.record_tabular('TimeElapsed', time.time() - tstart) for k, v in best_optimizers.items(): tlogger.record_tabular("{}_last_accuracy".format(k), v) if hvd.rank() == 0: tlogger.dump_tabular() if (iteration == 1 or iteration % 1000 == 0 or is_last_iteration): with torch.no_grad(): if enable_checkpointing: batches = [] for it in range(num_inner_iterations): if training_schedule_backwards: x, y = automl.generator( num_inner_iterations - it - 1) else: x, y = automl.generator(it) batches.append( (x.cpu().numpy(), y.cpu().numpy())) batches = list(reversed(batches)) with open( os.path.join( tlogger.get_dir(), 'samples_{}.pkl'.format(iteration)), 'wb') as file: pickle.dump(batches, file) del batches tlogger.info( "Saved:", os.path.join( tlogger.get_dir(), 'samples_{}.pkl'.format(iteration))) ckpt = os.path.join( tlogger.get_dir(), 'checkpoint_{}.pkl'.format(iteration)) torch.save( { "optimizer": optimizer.state_dict(), "model": automl.state_dict(), }, ckpt) tlogger.info("Saved:", ckpt) if is_last_iteration: break elif hvd.rank() == 0: tlogger.info("training_loss:", loss.item()) return validation_accuracy
g /= returns_n2.size assert (g.shape == (policy.num_params, ) and g.dtype == np.float32 and count == len(noise_inds_n)) update_ratio = optimizer.update(-g + config.l2coeff * theta) # Update ob stat (we're never running the policy in the master, but we # might be snapshotting the policy). if policy.needs_ob_stat: policy.set_ob_stat(ob_stat.mean, ob_stat.std) step_tend = time.time() tlogger.record_tabular("EpRewMean", returns_n2.mean()) tlogger.record_tabular("EpRewStd", returns_n2.std()) tlogger.record_tabular("EpLenMean", lengths_n2.mean()) tlogger.record_tabular( "Norm", float(np.square(policy.get_trainable_flat()).sum())) tlogger.record_tabular("GradNorm", float(np.square(g).sum())) tlogger.record_tabular("UpdateRatio", float(update_ratio)) tlogger.record_tabular("EpisodesThisIter", lengths_n2.size) tlogger.record_tabular("EpisodesSoFar", episodes_so_far) tlogger.record_tabular("TimestepsThisIter", lengths_n2.sum()) tlogger.record_tabular("TimestepsSoFar", timesteps_so_far) tlogger.record_tabular("ObCount", ob_count_this_batch) tlogger.record_tabular("TimeElapsedThisIter", step_tend - step_tstart) tlogger.record_tabular("TimeElapsed", step_tend - tstart) tlogger.dump_tabular()