smartparam.py

# Copyright (c) Microsoft Corporation. All rights reserved.
#
# MIT License
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish, distribute,
# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# substantial portions of the Software.
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# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# ==================================================================================================


import random
import numpy as np

from .env_vars import trial_env_vars
from . import trial
from .nas_utils import classic_mode, enas_mode, oneshot_mode, darts_mode


__all__ = [
    'choice',
    'randint',
    'uniform',
    'quniform',
    'loguniform',
    'qloguniform',
    'normal',
    'qnormal',
    'lognormal',
    'qlognormal',
    'function_choice',
    'mutable_layer'
]


# pylint: disable=unused-argument

if trial_env_vars.NNI_PLATFORM is None:
    def choice(*options, name=None):
        return random.choice(options)

    def randint(upper, name=None):
        return random.randrange(upper)

    def uniform(low, high, name=None):
        return random.uniform(low, high)

    def quniform(low, high, q, name=None):
        assert high > low, 'Upper bound must be larger than lower bound'
        return np.clip(round(random.uniform(low, high) / q) * q, low, high)

    def loguniform(low, high, name=None):
        assert low > 0, 'Lower bound must be positive'
        return np.exp(random.uniform(np.log(low), np.log(high)))

    def qloguniform(low, high, q, name=None):
        return np.clip(round(loguniform(low, high) / q) * q, low, high)

    def normal(mu, sigma, name=None):
        return random.gauss(mu, sigma)

    def qnormal(mu, sigma, q, name=None):
        return round(random.gauss(mu, sigma) / q) * q

    def lognormal(mu, sigma, name=None):
        return np.exp(random.gauss(mu, sigma))

    def qlognormal(mu, sigma, q, name=None):
        return round(lognormal(mu, sigma) / q) * q

    def function_choice(*funcs, name=None):
        return random.choice(funcs)()

    def mutable_layer():
        raise RuntimeError('Cannot call nni.mutable_layer in this mode')

else:

    def choice(options, name=None, key=None):
        return options[_get_param(key)]

    def randint(upper, name=None, key=None):
        return _get_param(key)

    def uniform(low, high, name=None, key=None):
        return _get_param(key)

    def quniform(low, high, q, name=None, key=None):
        return _get_param(key)

    def loguniform(low, high, name=None, key=None):
        return _get_param(key)

    def qloguniform(low, high, q, name=None, key=None):
        return _get_param(key)

    def normal(mu, sigma, name=None, key=None):
        return _get_param(key)

    def qnormal(mu, sigma, q, name=None, key=None):
        return _get_param(key)

    def lognormal(mu, sigma, name=None, key=None):
        return _get_param(key)

    def qlognormal(mu, sigma, q, name=None, key=None):
        return _get_param(key)

    def function_choice(funcs, name=None, key=None):
        return funcs[_get_param(key)]()

    def mutable_layer(
            mutable_id,
            mutable_layer_id,
            funcs,
            funcs_args,
            fixed_inputs,
            optional_inputs,
            optional_input_size,
            mode='classic_mode',
            tf=None):
        '''execute the chosen function and inputs.
        Below is an example of chosen function and inputs:
        {
            "mutable_id": {
                "mutable_layer_id": {
                    "chosen_layer": "pool",
                    "chosen_inputs": ["out1", "out3"]
                }
            }
        }
        Parameters:
        ---------------
        mutable_id: the name of this mutable_layer block (which could have multiple mutable layers)
        mutable_layer_id: the name of a mutable layer in this block
        funcs: dict of function calls
        funcs_args:
        fixed_inputs:
        optional_inputs: dict of optional inputs
        optional_input_size: number of candidate inputs to be chosen
        tf: tensorflow module
        '''
        if mode == 'classic_mode':
            return classic_mode(mutable_id,
                            mutable_layer_id,
                            funcs,
                            funcs_args,
                            fixed_inputs,
                            optional_inputs,
                            optional_input_size)
        assert tf is not None, 'Internal Error: Tensorflow should not be None in modes other than classic_mode'
        if mode == 'enas_mode':
            return enas_mode(mutable_id,
                            mutable_layer_id,
                            funcs,
                            funcs_args,
                            fixed_inputs,
                            optional_inputs,
                            optional_input_size,
                            tf)
        if mode == 'oneshot_mode':
            return oneshot_mode(mutable_id,
                            mutable_layer_id,
                            funcs,
                            funcs_args,
                            fixed_inputs,
                            optional_inputs,
                            optional_input_size,
                            tf)
        if mode == 'darts_mode':
            return darts_mode(mutable_id,
                            mutable_layer_id,
                            funcs,
                            funcs_args,
                            fixed_inputs,
                            optional_inputs,
                            optional_input_size,
                            tf)
        raise RuntimeError('Unrecognized mode: %s' % mode)

    def _get_param(key):
        if trial._params is None:
            trial.get_next_parameter()
        return trial.get_current_parameter(key)