def coach_adc(model, dataset, arch, data_loader, validate_fn, save_checkpoint_fn):
task_parameters = TaskParameters(framework_type="tensorflow",
experiment_path="./experiments/test")
extra_params = {'save_checkpoint_secs': None,
'render': True}
task_parameters.__dict__.update(extra_params)
# Create a dictionary of parameters that Coach will handover to CNNEnvironment
# Once it creates it.
if True:
exploration_noise = 0.5
#exploration_noise = 0.25
exploitation_decay = 0.996
graph_manager.env_params.additional_simulator_parameters = {
'model': model,
'dataset': dataset,
'arch': arch,
'data_loader': data_loader,
'validate_fn': validate_fn,
'save_checkpoint_fn': save_checkpoint_fn,
#'action_range': (0.10, 0.95),
'action_range': (0.70, 0.95),
'onehot_encoding': False,
'normalize_obs': True,
'desired_reduction': None,
'reward_fn': lambda top1, top5, vloss, total_macs: -1 * (1-top5/100) * math.log(total_macs)
#'reward_fn': lambda top1, total_macs: -1 * (1-top1/100) * math.log(total_macs)
#'reward_fn': lambda top1, total_macs: -1 * max(1-top1/100, 0.25) * math.log(total_macs)
#'reward_fn': lambda top1, total_macs: -1 * (1-top1/100) * math.log(total_macs/100000)
#'reward_fn': lambda top1, total_macs: top1/100 * total_macs/self.dense_model_macs
}
else:
exploration_noise = 0.5
#exploration_noise = 0.25
exploitation_decay = 0.996
graph_manager.env_params.additional_simulator_parameters = {
'model': model,
'dataset': dataset,
'arch': arch,
'data_loader': data_loader,
'validate_fn': validate_fn,
'save_checkpoint_fn': save_checkpoint_fn,
'action_range': (0.10, 0.95),
'onehot_encoding': False,
'normalize_obs': True,
'desired_reduction': 1.5e8,
'reward_fn': lambda top1, total_macs: top1/100
#'reward_fn': lambda top1, total_macs: min(top1/100, 0.75)
}
#msglogger.debug('Experiment configuarion:\n' + json.dumps(graph_manager.env_params.additional_simulator_parameters, indent=2))
steps_per_episode = 13
agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([(ConstantSchedule(exploration_noise),
EnvironmentSteps(100*steps_per_episode)),
(ExponentialSchedule(exploration_noise, 0, exploitation_decay),
EnvironmentSteps(300*steps_per_episode))])
graph_manager.create_graph(task_parameters)
graph_manager.improve()
def __init__(self):
super().__init__()
self.architecture_num_q_heads = 10
self.bootstrapped_data_sharing_probability = 1.0
self.epsilon_schedule = PieceWiseSchedule([
(LinearSchedule(1, 0.1, 1000000), EnvironmentSteps(1000000)),
(LinearSchedule(0.1, 0.01, 4000000), EnvironmentSteps(4000000))
])
self.lamb = 0.1
def coach_adc(model, dataset, arch, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
# task_parameters = TaskParameters(framework_type="tensorflow",
# experiment_path="./experiments/test")
# extra_params = {'save_checkpoint_secs': None,
# 'render': True}
# task_parameters.__dict__.update(extra_params)
task_parameters = TaskParameters(experiment_path=logger.get_experiment_path('adc'))
conv_cnt = count_conv_layer(model)
# Create a dictionary of parameters that Coach will handover to CNNEnvironment
# Once it creates it.
services = distiller.utils.MutableNamedTuple({
'validate_fn': validate_fn,
'save_checkpoint_fn': save_checkpoint_fn,
'train_fn': train_fn})
app_args = distiller.utils.MutableNamedTuple({
'dataset': dataset,
'arch': arch,
'optimizer_data': optimizer_data})
if True:
amc_cfg = distiller.utils.MutableNamedTuple({
#'action_range': (0.20, 0.95),
'action_range': (0.20, 0.80),
'onehot_encoding': False,
'normalize_obs': True,
'desired_reduction': None,
'reward_fn': lambda top1, top5, vloss, total_macs: -1 * (1-top1/100) * math.log(total_macs),
'conv_cnt': conv_cnt,
'max_reward': -1000})
else:
amc_cfg = distiller.utils.MutableNamedTuple({
'action_range': (0.10, 0.95),
'onehot_encoding': False,
'normalize_obs': True,
'desired_reduction': 1.5e8,
'reward_fn': lambda top1, top5, vloss, total_macs: top1/100,
#'reward_fn': lambda top1, total_macs: min(top1/100, 0.75),
'conv_cnt': conv_cnt,
'max_reward': -1000})
# These parameters are passed to the Distiller environment
graph_manager.env_params.additional_simulator_parameters = {'model': model,
'app_args': app_args,
'amc_cfg': amc_cfg,
'services': services}
exploration_noise = 0.5
exploitation_decay = 0.996
steps_per_episode = conv_cnt
agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
(ConstantSchedule(exploration_noise), EnvironmentSteps(100*steps_per_episode)),
(ExponentialSchedule(exploration_noise, 0, exploitation_decay), EnvironmentSteps(300*steps_per_episode))])
graph_manager.create_graph(task_parameters)
graph_manager.improve()
def test_piece_wise_schedule():
# decreasing schedule
schedule = PieceWiseSchedule(
[(LinearSchedule(1, 3, 10), EnvironmentSteps(5)),
(ConstantSchedule(4), EnvironmentSteps(10)),
(ExponentialSchedule(3, 1, 0.99), EnvironmentSteps(10))
]
)
target_values = np.append(np.linspace(1, 2, 6), np.ones(11)*4)
for i in range(16):
assert round(schedule.current_value, 4) == round(target_values[i], 4)
schedule.step()
current_power = 1
for i in range(10):
assert round(schedule.current_value, 4) == round(3*current_power, 4)
current_power *= 0.99
schedule.step()
def do_adc_internal(model, args, optimizer_data, validate_fn,
save_checkpoint_fn, train_fn):
dataset = args.dataset
arch = args.arch
perform_thinning = True # args.amc_thinning
num_ft_epochs = args.amc_ft_epochs
action_range = args.amc_action_range
np.random.seed()
conv_cnt = count_conv_layer(model)
msglogger.info("Executing AMC: RL agent - %s RL library - %s",
args.amc_agent_algo, RLLIB)
# Create a dictionary of parameters that Coach will handover to DistillerWrapperEnvironment
# Once it creates it.
services = distiller.utils.MutableNamedTuple({
'validate_fn': validate_fn,
'save_checkpoint_fn': save_checkpoint_fn,
'train_fn': train_fn
})
app_args = distiller.utils.MutableNamedTuple({
'dataset':
dataset,
'arch':
arch,
'optimizer_data':
optimizer_data
})
amc_cfg = distiller.utils.MutableNamedTuple({
'protocol':
args.amc_protocol,
'agent_algo':
args.amc_agent_algo,
'perform_thinning':
perform_thinning,
'num_ft_epochs':
num_ft_epochs,
'action_range':
action_range,
'conv_cnt':
conv_cnt,
'reward_frequency':
args.amc_reward_frequency
})
#net_wrapper = NetworkWrapper(model, app_args, services)
#return sample_networks(net_wrapper, services)
if args.amc_protocol == "accuracy-guaranteed":
amc_cfg.target_density = None
amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: -(
1 - top1 / 100) * math.log(total_macs)
amc_cfg.action_constrain_fn = None
elif args.amc_protocol == "mac-constrained":
amc_cfg.target_density = args.amc_target_density
amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: top1 / 100 #(90.5 - top1) / 10
amc_cfg.action_constrain_fn = DistillerWrapperEnvironment.get_action
elif args.amc_protocol == "mac-constrained-experimental":
amc_cfg.target_density = args.amc_target_density
amc_cfg.reward_fn = experimental_reward_fn
amc_cfg.action_constrain_fn = None
else:
raise ValueError("{} is not supported currently".format(
args.amc_protocol))
steps_per_episode = conv_cnt
if args.amc_agent_algo == "DDPG":
amc_cfg.heatup_noise = 0.5
amc_cfg.initial_training_noise = 0.5
amc_cfg.training_noise_decay = 0.996 # 0.998
amc_cfg.num_heatup_epochs = args.amc_heatup_epochs
amc_cfg.num_training_epochs = args.amc_training_epochs
training_noise_duration = amc_cfg.num_training_epochs * steps_per_episode
heatup_duration = amc_cfg.num_heatup_epochs * steps_per_episode
if amc_cfg.agent_algo == "Random-policy":
return random_agent(
DistillerWrapperEnvironment(model, app_args, amc_cfg, services))
if RLLIB == "spinup":
msglogger.info("AMC: Using spinup")
env1 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
env2 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
ddpg_spinup(env1, env2)
else:
msglogger.info("AMC: Using coach")
# When we import the graph_manager from the ADC_DDPG preset, we implicitly instruct
# Coach to create and use our DistillerWrapperEnvironment environment.
# So Distiller calls Coach, which creates the environment, trains the agent, and ends.
if args.amc_agent_algo == "DDPG":
from examples.automated_deep_compression.presets.ADC_DDPG import graph_manager, agent_params
agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule(
[(ConstantSchedule(amc_cfg.heatup_noise),
EnvironmentSteps(heatup_duration)),
(ExponentialSchedule(amc_cfg.initial_training_noise, 0,
amc_cfg.training_noise_decay),
EnvironmentSteps(training_noise_duration))])
# agent_params.exploration.noise_percentage_schedule = ConstantSchedule(0)
elif "ClippedPPO" in args.amc_agent_algo:
from examples.automated_deep_compression.presets.ADC_ClippedPPO import graph_manager, agent_params
# These parameters are passed to the Distiller environment
graph_manager.env_params.additional_simulator_parameters = {
'model': model,
'app_args': app_args,
'amc_cfg': amc_cfg,
'services': services
}
coach_logs_dir = os.path.join(msglogger.logdir, 'coach')
os.mkdir(coach_logs_dir)
task_parameters = TaskParameters(experiment_path=coach_logs_dir)
graph_manager.create_graph(task_parameters)
graph_manager.improve()
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(250000)
schedule_params.evaluation_steps = EnvironmentSteps(135000)
schedule_params.heatup_steps = EnvironmentSteps(50000)
#########
# Agent #
#########
agent_params = DDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.0001
agent_params.network_wrappers['main'].middleware_parameters.scheme = MiddlewareScheme.Empty
agent_params.network_wrappers['main'].heads_parameters = [DuelingQHeadParameters()]
agent_params.network_wrappers['main'].clip_gradients = 10
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(40000)
agent_params.exploration.epsilon_schedule = PieceWiseSchedule(
[(LinearSchedule(1, 0.1, 1000000), EnvironmentSteps(1000000)),
(LinearSchedule(0.1, 0.01, 10000000), EnvironmentSteps(1000000)),
(ConstantSchedule(0.001), EnvironmentSteps(10000000))]
)
agent_params.memory = PrioritizedExperienceReplayParameters()
agent_params.memory.beta = LinearSchedule(0.4, 1, 12500000) # 12.5M training iterations = 50M steps = 200M frames
###############
# Environment #
###############
env_params = Atari()
env_params.level = SingleLevelSelection(atari_deterministic_v4)
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False
agent_params.network_wrappers['actor'].heads_parameters[
0].activation_function = 'sigmoid'
#agent_params.network_wrappers['critic'].clip_gradients = 100
#agent_params.network_wrappers['actor'].clip_gradients = 100
agent_params.algorithm.rate_for_copying_weights_to_target = 0.01 # Tau pg. 11
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(
1)
agent_params.algorithm.discount = 1
agent_params.memory.max_size = (MemoryGranularity.Transitions, 2000)
agent_params.exploration = TruncatedNormalParameters(
) # AdditiveNoiseParameters()
steps_per_episode = 13
agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
(ConstantSchedule(0.5), EnvironmentSteps(100 * steps_per_episode)),
(ExponentialSchedule(0.5, 0,
0.996), EnvironmentSteps(300 * steps_per_episode))
])
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
agent_params.input_filter = MujocoInputFilter()
agent_params.output_filter = MujocoOutputFilter()
agent_params.network_wrappers['actor'].learning_rate = 0.0001
agent_params.network_wrappers['critic'].learning_rate = 0.001
##############################
# Gym #
##############################
env_params = GymEnvironmentParameters()
env_params.level = '../automated_deep_compression/ADC.py:CNNEnvironment'
vis_params = VisualizationParameters()
