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 get_config_args(self,
parser: argparse.ArgumentParser) -> argparse.Namespace:
"""
Returns a Namespace object with all the user-specified configuration options needed to launch.
This implementation uses argparse to take arguments from the CLI, but this can be over-ridden by
another method that gets its configuration from elsewhere. An equivalent method however must
return an identically structured Namespace object, which conforms to the structure defined by
get_argument_parser.
This method parses the arguments that the user entered, does some basic validation, and
modification of user-specified values in short form to be more explicit.
:param parser: a parser object which implicitly defines the format of the Namespace that
is expected to be returned.
:return: the parsed arguments as a Namespace
"""
args = parser.parse_args()
if args.nocolor:
screen.set_use_colors(False)
# if no arg is given
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
# list available presets
if args.list:
self.display_all_presets_and_exit()
# Read args from config file for distributed Coach.
if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
coach_config = ConfigParser({
'image': '',
'memory_backend': 'redispubsub',
'data_store': 's3',
's3_end_point': 's3.amazonaws.com',
's3_bucket_name': '',
's3_creds_file': ''
})
try:
coach_config.read(args.distributed_coach_config_path)
args.image = coach_config.get('coach', 'image')
args.memory_backend = coach_config.get('coach',
'memory_backend')
args.data_store = coach_config.get('coach', 'data_store')
if args.data_store == 's3':
args.s3_end_point = coach_config.get(
'coach', 's3_end_point')
args.s3_bucket_name = coach_config.get(
'coach', 's3_bucket_name')
args.s3_creds_file = coach_config.get(
'coach', 's3_creds_file')
except Error as e:
screen.error(
"Error when reading distributed Coach config file: {}".
format(e))
if args.image == '':
screen.error("Image cannot be empty.")
data_store_choices = ['s3', 'nfs']
if args.data_store not in data_store_choices:
screen.warning("{} data store is unsupported.".format(
args.data_store))
screen.error(
"Supported data stores are {}.".format(data_store_choices))
memory_backend_choices = ['redispubsub']
if args.memory_backend not in memory_backend_choices:
screen.warning("{} memory backend is not supported.".format(
args.memory_backend))
screen.error("Supported memory backends are {}.".format(
memory_backend_choices))
if args.data_store == 's3':
if args.s3_bucket_name == '':
screen.error("S3 bucket name cannot be empty.")
if args.s3_creds_file == '':
args.s3_creds_file = None
if args.play and args.distributed_coach:
screen.error("Playing is not supported in distributed Coach.")
# replace a short preset name with the full path
if args.preset is not None:
args.preset = self.expand_preset(args.preset)
# validate the checkpoints args
if args.checkpoint_restore_dir is not None and not os.path.exists(
args.checkpoint_restore_dir):
screen.error(
"The requested checkpoint folder to load from does not exist.")
# validate the checkpoints args
if args.checkpoint_restore_file is not None and not glob(
args.checkpoint_restore_file + '*'):
screen.error(
"The requested checkpoint file to load from does not exist.")
# no preset was given. check if the user requested to play some environment on its own
if args.preset is None and args.play and not args.environment_type:
screen.error(
'When no preset is given for Coach to run, and the user requests human control over '
'the environment, the user is expected to input the desired environment_type and level.'
'\nAt least one of these parameters was not given.')
elif args.preset and args.play:
screen.error(
"Both the --preset and the --play flags were set. These flags can not be used together. "
"For human control, please use the --play flag together with the environment type flag (-et)"
)
elif args.preset is None and not args.play:
screen.error(
"Please choose a preset using the -p flag or use the --play flag together with choosing an "
"environment type (-et) in order to play the game.")
# get experiment name and path
args.experiment_name = logger.get_experiment_name(args.experiment_name)
args.experiment_path = logger.get_experiment_path(args.experiment_name)
if args.play and args.num_workers > 1:
screen.warning(
"Playing the game as a human is only available with a single worker. "
"The number of workers will be reduced to 1")
args.num_workers = 1
args.framework = Frameworks[args.framework.lower()]
# checkpoints
args.checkpoint_save_dir = os.path.join(
args.experiment_path,
'checkpoint') if args.checkpoint_save_secs is not None else None
if args.export_onnx_graph and not args.checkpoint_save_secs:
screen.warning(
"Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. "
"The --export_onnx_graph will have no effect.")
return args
def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace:
"""
Parse the arguments that the user entered
:param parser: the argparse command line parser
:return: the parsed arguments
"""
args = parser.parse_args()
# if no arg is given
if len(sys.argv) == 1:
parser.print_help()
exit(0)
# list available presets
preset_names = list_all_presets()
if args.list:
screen.log_title("Available Presets:")
for preset in sorted(preset_names):
print(preset)
sys.exit(0)
# replace a short preset name with the full path
if args.preset is not None:
if args.preset.lower() in [p.lower() for p in preset_names]:
args.preset = "{}.py:graph_manager".format(
os.path.join(get_base_dir(), 'presets', args.preset))
else:
args.preset = "{}".format(args.preset)
# if a graph manager variable was not specified, try the default of :graph_manager
if len(args.preset.split(":")) == 1:
args.preset += ":graph_manager"
# verify that the preset exists
preset_path = args.preset.split(":")[0]
if not os.path.exists(preset_path):
screen.error("The given preset ({}) cannot be found.".format(
args.preset))
# verify that the preset can be instantiated
try:
short_dynamic_import(args.preset, ignore_module_case=True)
except TypeError as e:
traceback.print_exc()
screen.error('Internal Error: ' + str(e) +
"\n\nThe given preset ({}) cannot be instantiated.".
format(args.preset))
# validate the checkpoints args
if args.checkpoint_restore_dir is not None and not os.path.exists(
args.checkpoint_restore_dir):
screen.error(
"The requested checkpoint folder to load from does not exist.")
# no preset was given. check if the user requested to play some environment on its own
if args.preset is None and args.play:
if args.environment_type:
args.agent_type = 'Human'
else:
screen.error(
'When no preset is given for Coach to run, and the user requests human control over '
'the environment, the user is expected to input the desired environment_type and level.'
'\nAt least one of these parameters was not given.')
elif args.preset and args.play:
screen.error(
"Both the --preset and the --play flags were set. These flags can not be used together. "
"For human control, please use the --play flag together with the environment type flag (-et)"
)
elif args.preset is None and not args.play:
screen.error(
"Please choose a preset using the -p flag or use the --play flag together with choosing an "
"environment type (-et) in order to play the game.")
# get experiment name and path
args.experiment_name = logger.get_experiment_name(args.experiment_name)
args.experiment_path = logger.get_experiment_path(args.experiment_name)
if args.play and args.num_workers > 1:
screen.warning(
"Playing the game as a human is only available with a single worker. "
"The number of workers will be reduced to 1")
args.num_workers = 1
args.framework = Frameworks[args.framework.lower()]
# checkpoints
args.save_checkpoint_dir = os.path.join(
args.experiment_path,
'checkpoint') if args.save_checkpoint_secs is not None else None
return args
