Make pylint happy (#1649)

Update python sdk and nni_annotation to pass pylint rules

.gitignore

@@ -69,3 +69,6 @@ build
 *.egg-info
 
 .vscode
+
+# In case you place source code in ~/nni/
+/experiments

src/sdk/pynni/nni/__init__.py

@@ -19,16 +19,14 @@
 # ==================================================================================================
 
 
-# pylint: disable=wildcard-import
-
 from .trial import *
 from .smartparam import *
 from .nas_utils import training_update
 
 class NoMoreTrialError(Exception):
-    def __init__(self,ErrorInfo):
+    def __init__(self, ErrorInfo):
         super().__init__(self)
-        self.errorinfo=ErrorInfo
+        self.errorinfo = ErrorInfo
 
     def __str__(self):
-        return self.errorinfo
+        return self.errorinfo
\ No newline at end of file

src/sdk/pynni/nni/__main__.py

@@ -27,9 +27,10 @@ import logging
 import json
 import importlib
 
+from .common import enable_multi_thread, enable_multi_phase
 from .constants import ModuleName, ClassName, ClassArgs, AdvisorModuleName, AdvisorClassName
-from nni.common import enable_multi_thread, enable_multi_phase
-from nni.msg_dispatcher import MsgDispatcher
+from .msg_dispatcher import MsgDispatcher
+
 logger = logging.getLogger('nni.main')
 logger.debug('START')
 
@@ -44,7 +45,7 @@ def augment_classargs(input_class_args, classname):
                 input_class_args[key] = value
     return input_class_args
 
-def create_builtin_class_instance(classname, jsonstr_args, is_advisor = False):
+def create_builtin_class_instance(classname, jsonstr_args, is_advisor=False):
     if is_advisor:
         if classname not in AdvisorModuleName or \
             importlib.util.find_spec(AdvisorModuleName[classname]) is None:
@@ -130,55 +131,15 @@ def main():
 
     if args.advisor_class_name:
         # advisor is enabled and starts to run
-        if args.advisor_class_name in AdvisorModuleName:
-            dispatcher = create_builtin_class_instance(
-                args.advisor_class_name,
-                args.advisor_args, True)
-        else:
-            dispatcher = create_customized_class_instance(
-                args.advisor_directory,
-                args.advisor_class_filename,
-                args.advisor_class_name,
-                args.advisor_args)
-        if dispatcher is None:
-            raise AssertionError('Failed to create Advisor instance')
-        try:
-            dispatcher.run()
-        except Exception as exception:
-            logger.exception(exception)
-            raise
+        _run_advisor(args)
+
     else:
         # tuner (and assessor) is enabled and starts to run
-        tuner = None
-        assessor = None
-        if args.tuner_class_name in ModuleName:
-            tuner = create_builtin_class_instance(
-                args.tuner_class_name,
-                args.tuner_args)
-        else:
-            tuner = create_customized_class_instance(
-                args.tuner_directory,
-                args.tuner_class_filename,
-                args.tuner_class_name,
-                args.tuner_args)
-
-        if tuner is None:
-            raise AssertionError('Failed to create Tuner instance')
-
+        tuner = _create_tuner(args)
         if args.assessor_class_name:
-            if args.assessor_class_name in ModuleName:
-                assessor = create_builtin_class_instance(
-                    args.assessor_class_name,
-                    args.assessor_args)
-            else:
-                assessor = create_customized_class_instance(
-                    args.assessor_directory,
-                    args.assessor_class_filename,
-                    args.assessor_class_name,
-                    args.assessor_args)
-            if assessor is None:
-                raise AssertionError('Failed to create Assessor instance')
-
+            assessor = _create_assessor(args)
+        else:
+            assessor = None
         dispatcher = MsgDispatcher(tuner, assessor)
 
         try:
@@ -193,6 +154,59 @@ def main():
                 assessor._on_error()
             raise
 
+
+def _run_advisor(args):
+    if args.advisor_class_name in AdvisorModuleName:
+        dispatcher = create_builtin_class_instance(
+            args.advisor_class_name,
+            args.advisor_args, True)
+    else:
+        dispatcher = create_customized_class_instance(
+            args.advisor_directory,
+            args.advisor_class_filename,
+            args.advisor_class_name,
+            args.advisor_args)
+    if dispatcher is None:
+        raise AssertionError('Failed to create Advisor instance')
+    try:
+        dispatcher.run()
+    except Exception as exception:
+        logger.exception(exception)
+        raise
+
+
+def _create_tuner(args):
+    if args.tuner_class_name in ModuleName:
+        tuner = create_builtin_class_instance(
+            args.tuner_class_name,
+            args.tuner_args)
+    else:
+        tuner = create_customized_class_instance(
+            args.tuner_directory,
+            args.tuner_class_filename,
+            args.tuner_class_name,
+            args.tuner_args)
+    if tuner is None:
+        raise AssertionError('Failed to create Tuner instance')
+    return tuner
+
+
+def _create_assessor(args):
+    if args.assessor_class_name in ModuleName:
+        assessor = create_builtin_class_instance(
+            args.assessor_class_name,
+            args.assessor_args)
+    else:
+        assessor = create_customized_class_instance(
+            args.assessor_directory,
+            args.assessor_class_filename,
+            args.assessor_class_name,
+            args.assessor_args)
+    if assessor is None:
+        raise AssertionError('Failed to create Assessor instance')
+    return assessor
+
+
 if __name__ == '__main__':
     try:
         main()

src/sdk/pynni/nni/assessor.py

@@ -31,7 +31,6 @@ class AssessResult(Enum):
     Bad = False
 
 class Assessor(Recoverable):
-    # pylint: disable=no-self-use,unused-argument
 
     def assess_trial(self, trial_job_id, trial_history):
         """Determines whether a trial should be killed. Must override.
@@ -46,21 +45,20 @@ class Assessor(Recoverable):
         trial_job_id: identifier of the trial (str).
         success: True if the trial successfully completed; False if failed or terminated.
         """
-        pass
 
     def load_checkpoint(self):
         """Load the checkpoint of assessr.
         path: checkpoint directory for assessor
         """
         checkpoin_path = self.get_checkpoint_path()
-        _logger.info('Load checkpoint ignored by assessor, checkpoint path: %s' % checkpoin_path)
+        _logger.info('Load checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 
     def save_checkpoint(self):
         """Save the checkpoint of assessor.
         path: checkpoint directory for assessor
         """
         checkpoin_path = self.get_checkpoint_path()
-        _logger.info('Save checkpoint ignored by assessor, checkpoint path: %s' % checkpoin_path)
+        _logger.info('Save checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 
     def _on_exit(self):
         pass

src/sdk/pynni/nni/batch_tuner/batch_tuner.py

@@ -100,7 +100,7 @@ class BatchTuner(Tuner):
         data:
             a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
         """
-        if len(self.values) == 0:
+        if not self.values:
             logger.info("Search space has not been initialized, skip this data import")
             return
 

src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py

@@ -51,7 +51,7 @@ def create_parameter_id():
     int
         parameter id
     """
-    global _next_parameter_id  # pylint: disable=global-statement
+    global _next_parameter_id
     _next_parameter_id += 1
     return _next_parameter_id - 1
 
@@ -80,7 +80,7 @@ def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-
     return params_id
 
 
-class Bracket(object):
+class Bracket:
     """
     A bracket in BOHB, all the information of a bracket is managed by
     an instance of this class.
@@ -98,7 +98,7 @@ class Bracket(object):
 	max_budget : float
 		The largest budget to consider. Needs to be larger than min_budget!
 		The budgets will be geometrically distributed
-        :math:`a^2 + b^2 = c^2 \sim \eta^k` for :math:`k\in [0, 1, ... , num\_subsets - 1]`.
+        :math:`a^2 + b^2 = c^2 \\sim \\eta^k` for :math:`k\\in [0, 1, ... , num\\_subsets - 1]`.
     optimize_mode: str
         optimize mode, 'maximize' or 'minimize'
     """
@@ -169,7 +169,7 @@ class Bracket(object):
             If we have generated new trials after this trial end, we will return a new trial parameters.
             Otherwise, we will return None.
         """
-        global _KEY  # pylint: disable=global-statement
+        global _KEY
         self.num_finished_configs[i] += 1
         logger.debug('bracket id: %d, round: %d %d, finished: %d, all: %d',
                      self.s, self.i, i, self.num_finished_configs[i], self.num_configs_to_run[i])
@@ -377,8 +377,10 @@ class BOHB(MsgDispatcherBase):
         if self.curr_s < 0:
             logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
             self.curr_s = self.s_max
-        self.brackets[self.curr_s] = Bracket(s=self.curr_s, s_max=self.s_max, eta=self.eta,
-                                    max_budget=self.max_budget, optimize_mode=self.optimize_mode)
+        self.brackets[self.curr_s] = Bracket(
+            s=self.curr_s, s_max=self.s_max, eta=self.eta,
+            max_budget=self.max_budget, optimize_mode=self.optimize_mode
+        )
         next_n, next_r = self.brackets[self.curr_s].get_n_r()
         logger.debug(
             'new SuccessiveHalving iteration, next_n=%d, next_r=%d', next_n, next_r)
@@ -599,7 +601,7 @@ class BOHB(MsgDispatcherBase):
             logger.debug('bracket id = %s, metrics value = %s, type = %s', s, value, data['type'])
             s = int(s)
 
-            # add <trial_job_id, parameter_id> to self.job_id_para_id_map here, 
+            # add <trial_job_id, parameter_id> to self.job_id_para_id_map here,
             # because when the first parameter_id is created, trial_job_id is not known yet.
             if data['trial_job_id'] in self.job_id_para_id_map:
                 assert self.job_id_para_id_map[data['trial_job_id']] == data['parameter_id']
@@ -643,14 +645,14 @@ class BOHB(MsgDispatcherBase):
         """
         _completed_num = 0
         for trial_info in data:
-            logger.info("Importing data, current processing progress %s / %s" %(_completed_num, len(data)))
+            logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
             _completed_num += 1
             assert "parameter" in trial_info
             _params = trial_info["parameter"]
             assert "value" in trial_info
             _value = trial_info['value']
             if not _value:
-                logger.info("Useless trial data, value is %s, skip this trial data." %_value)
+                logger.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
             budget_exist_flag = False
             barely_params = dict()
@@ -662,7 +664,7 @@ class BOHB(MsgDispatcherBase):
                     barely_params[keys] = _params[keys]
             if not budget_exist_flag:
                 _budget = self.max_budget
-                logger.info("Set \"TRIAL_BUDGET\" value to %s (max budget)" %self.max_budget)
+                logger.info("Set \"TRIAL_BUDGET\" value to %s (max budget)", self.max_budget)
             if self.optimize_mode is OptimizeMode.Maximize:
                 reward = -_value
             else:

src/sdk/pynni/nni/bohb_advisor/config_generator.py

@@ -28,7 +28,6 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import logging
-import traceback
 
 import ConfigSpace
 import ConfigSpace.hyperparameters
@@ -39,7 +38,7 @@ import statsmodels.api as sm
 
 logger = logging.getLogger('BOHB_Advisor')
 
-class CG_BOHB(object):
+class CG_BOHB:
     def __init__(self, configspace, min_points_in_model=None,
                  top_n_percent=15, num_samples=64, random_fraction=1/3,
                  bandwidth_factor=3, min_bandwidth=1e-3):
@@ -77,8 +76,8 @@ class CG_BOHB(object):
             self.min_points_in_model = len(self.configspace.get_hyperparameters())+1
 
         if self.min_points_in_model < len(self.configspace.get_hyperparameters())+1:
-            logger.warning('Invalid min_points_in_model value. Setting it to %i'%(len(self.configspace.get_hyperparameters())+1))
-            self.min_points_in_model =len(self.configspace.get_hyperparameters())+1
+            logger.warning('Invalid min_points_in_model value. Setting it to %i', len(self.configspace.get_hyperparameters()) + 1)
+            self.min_points_in_model = len(self.configspace.get_hyperparameters()) + 1
 
         self.num_samples = num_samples
         self.random_fraction = random_fraction
@@ -107,9 +106,9 @@ class CG_BOHB(object):
         self.kde_models = dict()
 
     def largest_budget_with_model(self):
-        if len(self.kde_models) == 0:
-            return(-float('inf'))
-        return(max(self.kde_models.keys()))
+        if not self.kde_models:
+            return -float('inf')
+        return max(self.kde_models.keys())
 
     def sample_from_largest_budget(self, info_dict):
         """We opted for a single multidimensional KDE compared to the
@@ -162,11 +161,11 @@ class CG_BOHB(object):
             val = minimize_me(vector)
 
             if not np.isfinite(val):
-                logger.warning('sampled vector: %s has EI value %s'%(vector, val))
-                logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
-                logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
-                logger.warning("l(x) = %s"%(l(vector)))
-                logger.warning("g(x) = %s"%(g(vector)))
+                logger.warning('sampled vector: %s has EI value %s', vector, val)
+                logger.warning("data in the KDEs:\n%s\n%s", kde_good.data, kde_bad.data)
+                logger.warning("bandwidth of the KDEs:\n%s\n%s", kde_good.bw, kde_bad.bw)
+                logger.warning("l(x) = %s", l(vector))
+                logger.warning("g(x) = %s", g(vector))
 
                 # right now, this happens because a KDE does not contain all values for a categorical parameter
                 # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
@@ -181,19 +180,15 @@ class CG_BOHB(object):
                 best_vector = vector
 
         if best_vector is None:
-            logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
+            logger.debug("Sampling based optimization with %i samples failed -> using random configuration", self.num_samples)
             sample = self.configspace.sample_configuration().get_dictionary()
             info_dict['model_based_pick'] = False
 
         else:
-            logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
-            for i, hp_value in enumerate(best_vector):
-                if isinstance(
-                    self.configspace.get_hyperparameter(
-                        self.configspace.get_hyperparameter_by_idx(i)
-                    ),
-                    ConfigSpace.hyperparameters.CategoricalHyperparameter
-                ):
+            logger.debug('best_vector: %s, %s, %s, %s', best_vector, best, l(best_vector), g(best_vector))
+            for i, _ in enumerate(best_vector):
+                hp = self.configspace.get_hyperparameter(self.configspace.get_hyperparameter_by_idx(i))
+                if isinstance(hp, ConfigSpace.hyperparameters.CategoricalHyperparameter):
                     best_vector[i] = int(np.rint(best_vector[i]))
             sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
 
@@ -224,12 +219,12 @@ class CG_BOHB(object):
 
         # If no model is available, sample from prior
         # also mix in a fraction of random configs
-        if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
+        if not self.kde_models.keys() or np.random.rand() < self.random_fraction:
             sample = self.configspace.sample_configuration()
             info_dict['model_based_pick'] = False
 
         if sample is None:
-            sample, info_dict= self.sample_from_largest_budget(info_dict)
+            sample, info_dict = self.sample_from_largest_budget(info_dict)
 
         sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
             configuration_space=self.configspace,
@@ -245,10 +240,10 @@ class CG_BOHB(object):
         for i in range(array.shape[0]):
             datum = np.copy(array[i])
             nan_indices = np.argwhere(np.isnan(datum)).flatten()
-            while(np.any(nan_indices)):
+            while np.any(nan_indices):
                 nan_idx = nan_indices[0]
-                valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
-                if len(valid_indices) > 0:
+                valid_indices = np.argwhere(np.isfinite(array[:, nan_idx])).flatten()
+                if valid_indices:
                     # pick one of them at random and overwrite all NaN values
                     row_idx = np.random.choice(valid_indices)
                     datum[nan_indices] = array[row_idx, nan_indices]
@@ -260,8 +255,8 @@ class CG_BOHB(object):
                     else:
                         datum[nan_idx] = np.random.randint(t)
                 nan_indices = np.argwhere(np.isnan(datum)).flatten()
-            return_array[i,:] = datum
-        return(return_array)
+            return_array[i, :] = datum
+        return return_array
 
     def new_result(self, loss, budget, parameters, update_model=True):
         """
@@ -305,7 +300,7 @@ class CG_BOHB(object):
         # a) if not enough points are available
         if len(self.configs[budget]) <= self.min_points_in_model - 1:
             logger.debug("Only %i run(s) for budget %f available, need more than %s \
-            -> can't build model!"%(len(self.configs[budget]), budget, self.min_points_in_model+1))
+            -> can't build model!", len(self.configs[budget]), budget, self.min_points_in_model+1)
             return
         # b) during warnm starting when we feed previous results in and only update once
         if not update_model:
@@ -345,5 +340,5 @@ class CG_BOHB(object):
         }
 
         # update probs for the categorical parameters for later sampling
-        logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n'
-                     %(budget, n_good, n_bad, np.min(train_losses)))
+        logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n',
+                     budget, n_good, n_bad, np.min(train_losses))

src/sdk/pynni/nni/common.py

@@ -41,8 +41,8 @@ class _LoggerFileWrapper(TextIOBase):
 
     def write(self, s):
         if s != '\n':
-            time = datetime.now().strftime(_time_format)
-            self.file.write('[{}] PRINT '.format(time) + s + '\n')
+            cur_time = datetime.now().strftime(_time_format)
+            self.file.write('[{}] PRINT '.format(cur_time) + s + '\n')
             self.file.flush()
         return len(s)
 

src/sdk/pynni/nni/compression/tensorflow/builtin_pruners.py

@@ -29,7 +29,7 @@ class LevelPruner(Pruner):
 
 
 class AGP_Pruner(Pruner):
-    """An automated gradual pruning algorithm that prunes the smallest magnitude 
+    """An automated gradual pruning algorithm that prunes the smallest magnitude
     weights to achieve a preset level of network sparsity.
 
     Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the
@@ -92,5 +92,5 @@ class AGP_Pruner(Pruner):
     def update_epoch(self, epoch, sess):
         sess.run(self.assign_handler)
         sess.run(tf.assign(self.now_epoch, int(epoch)))
-        for k in self.if_init_list.keys():
+        for k in self.if_init_list:
             self.if_init_list[k] = True

src/sdk/pynni/nni/compression/tensorflow/builtin_quantizers.py

@@ -2,7 +2,7 @@ import logging
 import tensorflow as tf
 from .compressor import Quantizer
 
-__all__ = [ 'NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer' ]
+__all__ = ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer']
 
 _logger = logging.getLogger(__name__)
 
@@ -12,7 +12,7 @@ class NaiveQuantizer(Quantizer):
     """
     def __init__(self, config_list):
         super().__init__(config_list)
-        self.layer_scale = { }
+        self.layer_scale = {}
 
     def quantize_weight(self, weight, config, op_name, **kwargs):
         new_scale = tf.reduce_max(tf.abs(weight)) / 127
@@ -33,17 +33,17 @@ class QAT_Quantizer(Quantizer):
             - q_bits
         """
         super().__init__(config_list)
-        
+
     def quantize_weight(self, weight, config, **kwargs):
         a = tf.stop_gradient(tf.reduce_min(weight))
         b = tf.stop_gradient(tf.reduce_max(weight))
         n = tf.cast(2 ** config['q_bits'], tf.float32)
         scale = b-a/(n-1)
-        
+
         # use gradient_override_map to change round to idetity for gradient
         with tf.get_default_graph().gradient_override_map({'Round': 'Identity'}):
             qw = tf.round((weight-a)/scale)*scale +a
-        
+
         return qw
 
 
@@ -58,7 +58,7 @@ class DoReFaQuantizer(Quantizer):
             - q_bits
         """
         super().__init__(config_list)
-        
+
     def quantize_weight(self, weight, config, **kwargs):
         a = tf.math.tanh(weight)
         b = a/(2*tf.reduce_max(tf.abs(weight))) + 0.5

src/sdk/pynni/nni/compression/tensorflow/compressor.py

-import tensorflow as tf
 import logging
+import tensorflow as tf
 from . import default_layers
 
 _logger = logging.getLogger(__name__)
@@ -51,17 +51,14 @@ class Compressor:
         Compressors can optionally overload this method to do model-specific initialization.
         It is guaranteed that only one model will be bound to each compressor instance.
         """
-        pass
-    
+
     def update_epoch(self, epoch, sess):
         """If user want to update mask every epoch, user can override this method
         """
-        pass
-    
+
     def step(self, sess):
         """If user want to update mask every step, user can override this method
         """
-        pass
 
 
     def _instrument_layer(self, layer, config):
@@ -84,10 +81,9 @@ class Compressor:
 
 
 class Pruner(Compressor):
-    """Abstract base TensorFlow pruner"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base TensorFlow pruner
+    """
 
     def calc_mask(self, weight, config, op, op_type, op_name):
         """Pruners should overload this method to provide mask for weight tensors.
@@ -105,7 +101,7 @@ class Pruner(Compressor):
         # not sure what will happen if the weight is calculated from other operations
         weight_index = _detect_weight_index(layer)
         if weight_index is None:
-            _logger.warning('Failed to detect weight for layer {}'.format(layer.name))
+            _logger.warning('Failed to detect weight for layer %s', layer.name)
             return
         weight_op = layer.op.inputs[weight_index].op
         weight = weight_op.inputs[0]
@@ -115,10 +111,9 @@ class Pruner(Compressor):
 
 
 class Quantizer(Compressor):
-    """Abstract base TensorFlow quantizer"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base TensorFlow quantizer
+    """
 
     def quantize_weight(self, weight, config, op, op_type, op_name):
         raise NotImplementedError("Quantizer must overload quantize_weight()")
@@ -126,7 +121,7 @@ class Quantizer(Compressor):
     def _instrument_layer(self, layer, config):
         weight_index = _detect_weight_index(layer)
         if weight_index is None:
-            _logger.warning('Failed to detect weight for layer {}'.format(layer.name))
+            _logger.warning('Failed to detect weight for layer %s', layer.name)
             return
         weight_op = layer.op.inputs[weight_index].op
         weight = weight_op.inputs[0]
@@ -138,7 +133,7 @@ def _detect_weight_index(layer):
     index = default_layers.op_weight_index.get(layer.type)
     if index is not None:
         return index
-    weight_indices = [ i for i, op in enumerate(layer.op.inputs) if op.name.endswith('Variable/read') ]
+    weight_indices = [i for i, op in enumerate(layer.op.inputs) if op.name.endswith('Variable/read')]
     if len(weight_indices) == 1:
         return weight_indices[0]
     return None

src/sdk/pynni/nni/compression/torch/builtin_pruners.py

@@ -36,7 +36,7 @@ class LevelPruner(Pruner):
 
 
 class AGP_Pruner(Pruner):
-    """An automated gradual pruning algorithm that prunes the smallest magnitude 
+    """An automated gradual pruning algorithm that prunes the smallest magnitude
     weights to achieve a preset level of network sparsity.
 
     Michael Zhu and Suyog Gupta, "To prune, or not to prune: exploring the
@@ -102,5 +102,5 @@ class AGP_Pruner(Pruner):
     def update_epoch(self, epoch):
         if epoch > 0:
             self.now_epoch = epoch
-            for k in self.if_init_list.keys():
+            for k in self.if_init_list:
                 self.if_init_list[k] = True

src/sdk/pynni/nni/compression/torch/builtin_quantizers.py

@@ -2,7 +2,7 @@ import logging
 import torch
 from .compressor import Quantizer
 
-__all__ = [ 'NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer' ]
+__all__ = ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer']
 
 logger = logging.getLogger(__name__)
 
@@ -62,7 +62,7 @@ class DoReFaQuantizer(Quantizer):
 
     def quantize_weight(self, weight, config, **kwargs):
         out = weight.tanh()
-        out = out /( 2 * out.abs().max()) + 0.5
+        out = out / (2 * out.abs().max()) + 0.5
         out = self.quantize(out, config['q_bits'])
         out = 2 * out -1
         return out

src/sdk/pynni/nni/compression/torch/compressor.py

-import torch
 import logging
+import torch
 from . import default_layers
 
 _logger = logging.getLogger(__name__)
@@ -43,17 +43,14 @@ class Compressor:
         Users can optionally overload this method to do model-specific initialization.
         It is guaranteed that only one model will be bound to each compressor instance.
         """
-        pass
 
     def update_epoch(self, epoch):
         """if user want to update model every epoch, user can override this method
         """
-        pass
 
     def step(self):
         """if user want to update model every step, user can override this method
         """
-        pass
 
     def _instrument_layer(self, layer, config):
         raise NotImplementedError()
@@ -75,10 +72,9 @@ class Compressor:
 
 
 class Pruner(Compressor):
-    """Abstract base PyTorch pruner"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Abstract base PyTorch pruner
+    """
 
     def calc_mask(self, weight, config, op, op_type, op_name):
         """Pruners should overload this method to provide mask for weight tensors.
@@ -93,17 +89,17 @@ class Pruner(Compressor):
         # create a wrapper forward function to replace the original one
         assert layer._forward is None, 'Each model can only be compressed once'
         if not _check_weight(layer.module):
-            _logger.warning('Module {} does not have parameter "weight"'.format(layer.name))
+            _logger.warning('Module %s does not have parameter "weight"', layer.name)
             return
         layer._forward = layer.module.forward
 
-        def new_forward(*input):
+        def new_forward(*inputs):
             # apply mask to weight
             old_weight = layer.module.weight.data
             mask = self.calc_mask(old_weight, config, op=layer.module, op_type=layer.type, op_name=layer.name)
             layer.module.weight.data = old_weight.mul(mask)
             # calculate forward
-            ret = layer._forward(*input)
+            ret = layer._forward(*inputs)
             # recover original weight
             layer.module.weight.data = old_weight
             return ret
@@ -112,10 +108,9 @@ class Pruner(Compressor):
 
 
 class Quantizer(Compressor):
-    """Base quantizer for pytorch quantizer"""
-
-    def __init__(self, config_list):
-        super().__init__(config_list)
+    """
+    Base quantizer for pytorch quantizer
+    """
 
     def __call__(self, model):
         self.compress(model)
@@ -130,15 +125,15 @@ class Quantizer(Compressor):
     def _instrument_layer(self, layer, config):
         assert layer._forward is None, 'Each model can only be compressed once'
         if not _check_weight(layer.module):
-            _logger.warning('Module {} does not have parameter "weight"'.format(layer.name))
+            _logger.warning('Module %s does not have parameter "weight"', layer.name)
             return
         layer._forward = layer.module.forward
 
-        def new_forward(*input):
+        def new_forward(*inputs):
             weight = layer.module.weight.data
             new_weight = self.quantize_weight(weight, config, op=layer.module, op_type=layer.type, op_name=layer.name)
             layer.module.weight.data = new_weight
-            return layer._forward(*input)
+            return layer._forward(*inputs)
 
         layer.module.forward = new_forward
 

src/sdk/pynni/nni/constants.py

@@ -71,4 +71,4 @@ AdvisorModuleName = {
 AdvisorClassName = {
     'Hyperband': 'Hyperband',
     'BOHB': 'BOHB'
-}
+}
\ No newline at end of file

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -50,7 +50,7 @@ class CurvefittingAssessor(Assessor):
             self.higher_better = False
         else:
             self.higher_better = True
-            logger.warning('unrecognized optimize_mode', optimize_mode)
+            logger.warning('unrecognized optimize_mode %s', optimize_mode)
         # Start forecasting when historical data reaches start step
         self.start_step = start_step
         # Record the compared threshold
@@ -81,9 +81,9 @@ class CurvefittingAssessor(Assessor):
             else:
                 self.set_best_performance = True
                 self.completed_best_performance = self.trial_history[-1]
-            logger.info('Updated complted best performance, trial job id:', trial_job_id)
+            logger.info('Updated complted best performance, trial job id: %s', trial_job_id)
         else:
-            logger.info('No need to update, trial job id: ', trial_job_id)
+            logger.info('No need to update, trial job id: %s', trial_job_id)
 
     def assess_trial(self, trial_job_id, trial_history):
         """assess whether a trial should be early stop by curve fitting algorithm
@@ -105,7 +105,7 @@ class CurvefittingAssessor(Assessor):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        self.trial_job_id = trial_job_id
+        trial_job_id = trial_job_id
         self.trial_history = trial_history
         if not self.set_best_performance:
             return AssessResult.Good
@@ -122,7 +122,7 @@ class CurvefittingAssessor(Assessor):
             # Predict the final result
             curvemodel = CurveModel(self.target_pos)
             predict_y = curvemodel.predict(trial_history)
-            logger.info('Prediction done. Trial job id = ', trial_job_id, '. Predict value = ', predict_y)
+            logger.info('Prediction done. Trial job id = %s. Predict value = %s', trial_job_id, predict_y)
             if predict_y is None:
                 logger.info('wait for more information to predict precisely')
                 return AssessResult.Good
@@ -130,7 +130,10 @@ class CurvefittingAssessor(Assessor):
 
             end_time = datetime.datetime.now()
             if (end_time - start_time).seconds > 60:
-                logger.warning('Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = ', self.trial_job_id, 'Trial History = ', self.trial_history)
+                logger.warning(
+                    'Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = %s Trial History = %s',
+                    trial_job_id, self.trial_history
+                )
 
             if self.higher_better:
                 if predict_y > standard_performance:
@@ -142,4 +145,4 @@ class CurvefittingAssessor(Assessor):
                 return AssessResult.Bad
 
         except Exception as exception:
-            logger.exception('unrecognize exception in curvefitting_assessor', exception)
+            logger.exception('unrecognize exception in curvefitting_assessor %s', exception)

src/sdk/pynni/nni/curvefitting_assessor/model_factory.py

@@ -18,7 +18,7 @@
 import logging
 import numpy as np
 from scipy import optimize
-from .curvefunctions import *
+from .curvefunctions import *  # pylint: disable=wildcard-import,unused-wildcard-import
 
 # Number of curve functions we prepared, more details can be found in "curvefunctions.py"
 NUM_OF_FUNCTIONS = 12
@@ -33,7 +33,7 @@ LEAST_FITTED_FUNCTION = 4
 
 logger = logging.getLogger('curvefitting_Assessor')
 
-class CurveModel(object):
+class CurveModel:
     """Build a Curve Model to predict the performance
 
     Algorithm: https://github.com/Microsoft/nni/blob/master/src/sdk/pynni/nni/curvefitting_assessor/README.md
@@ -83,7 +83,7 @@ class CurveModel(object):
                 # Ignore exceptions caused by numerical calculations
                 pass
             except Exception as exception:
-                logger.critical("Exceptions in fit_theta:", exception)
+                logger.critical("Exceptions in fit_theta: %s", exception)
 
     def filter_curve(self):
         """filter the poor performing curve
@@ -113,7 +113,7 @@ class CurveModel(object):
             if y < median + epsilon and y > median - epsilon:
                 self.effective_model.append(model)
         self.effective_model_num = len(self.effective_model)
-        logger.info('List of effective model: ', self.effective_model)
+        logger.info('List of effective model: %s', self.effective_model)
 
     def predict_y(self, model, pos):
         """return the predict y of 'model' when epoch = pos
@@ -303,7 +303,7 @@ class CurveModel(object):
         """
         init_weight = np.ones((self.effective_model_num), dtype=np.float) / self.effective_model_num
         self.weight_samples = np.broadcast_to(init_weight, (NUM_OF_INSTANCE, self.effective_model_num))
-        for i in range(NUM_OF_SIMULATION_TIME):
+        for _ in range(NUM_OF_SIMULATION_TIME):
             # sample new value from Q(i, j)
             new_values = np.random.randn(NUM_OF_INSTANCE, self.effective_model_num) * STEP_SIZE + self.weight_samples
             new_values = self.normalize_weight(new_values)

src/sdk/pynni/nni/curvefitting_assessor/test.py

@@ -15,6 +15,7 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+
 import numpy as np
 import unittest
 

src/sdk/pynni/nni/evolution_tuner/evolution_tuner.py

@@ -40,7 +40,7 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
             _type = x[NodeType.TYPE]
             name = name + '-' + _type
             if _type == 'choice':
-                if oldy != None:
+                if oldy is not None:
                     _index = oldy[NodeType.INDEX]
                     y += json2space(x[NodeType.VALUE][_index],
                                     oldy[NodeType.VALUE], name=name+'[%d]' % _index)
@@ -49,15 +49,13 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
             y.append(name)
         else:
             for key in x.keys():
-                y += json2space(x[key], (oldy[key] if oldy !=
-                                         None else None), name+"[%s]" % str(key))
+                y += json2space(x[key], oldy[key] if oldy else None, name+"[%s]" % str(key))
     elif isinstance(x, list):
         for i, x_i in enumerate(x):
             if isinstance(x_i, dict):
                 if NodeType.NAME not in x_i.keys():
                     raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y += json2space(x_i, (oldy[i] if oldy !=
-                                  None else None), name+"[%d]" % i)
+            y += json2space(x_i, oldy[i] if oldy else None, name + "[%d]" % i)
     return y
 
 def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
@@ -74,36 +72,49 @@ def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeTyp
                     _index = random_state.randint(len(_value))
                     y = {
                         NodeType.INDEX: _index,
-                        NodeType.VALUE: json2parameter(x[NodeType.VALUE][_index],
-                                                             is_rand,
-                                                             random_state,
-                                                             None,
-                                                             Rand,
-                                                             name=name+"[%d]" % _index)
+                        NodeType.VALUE: json2parameter(
+                            x[NodeType.VALUE][_index],
+                            is_rand,
+                            random_state,
+                            None,
+                            Rand,
+                            name=name+"[%d]" % _index
+                        )
                     }
                 else:
-                    y = eval('parameter_expressions.' +
-                             _type)(*(_value + [random_state]))
+                    y = getattr(parameter_expressions, _type)(*(_value + [random_state]))
             else:
                 y = copy.deepcopy(oldy)
         else:
             y = dict()
             for key in x.keys():
-                y[key] = json2parameter(x[key], is_rand, random_state, oldy[key]
-                                        if oldy != None else None, Rand, name + "[%s]" % str(key))
+                y[key] = json2parameter(
+                    x[key],
+                    is_rand,
+                    random_state,
+                    oldy[key] if oldy else None,
+                    Rand,
+                    name + "[%s]" % str(key)
+                )
     elif isinstance(x, list):
         y = list()
         for i, x_i in enumerate(x):
             if isinstance(x_i, dict):
                 if NodeType.NAME not in x_i.keys():
                     raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y.append(json2parameter(x_i, is_rand, random_state, oldy[i]
-                                    if oldy != None else None, Rand, name + "[%d]" % i))
+            y.append(json2parameter(
+                x_i,
+                is_rand,
+                random_state,
+                oldy[i] if oldy else None,
+                Rand,
+                name + "[%d]" % i
+            ))
     else:
         y = copy.deepcopy(x)
     return y
 
-class Individual(object):
+class Individual:
     """
     Indicidual class to store the indv info.
     """

src/sdk/pynni/nni/gp_tuner/gp_tuner.py

@@ -151,16 +151,14 @@ class GPTuner(Tuner):
         """
         _completed_num = 0
         for trial_info in data:
-            logger.info("Importing data, current processing progress %s / %s" %
-                        (_completed_num, len(data)))
+            logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
             _completed_num += 1
             assert "parameter" in trial_info
             _params = trial_info["parameter"]
             assert "value" in trial_info
             _value = trial_info['value']
             if not _value:
-                logger.info(
-                    "Useless trial data, value is %s, skip this trial data." % _value)
+                logger.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
             self.supplement_data_num += 1
             _parameter_id = '_'.join(