[CodeStyle][py2][U004] unecessary explicit object inheritance in class definition

paddle/infrt/tests/models/efficientnet-b4/net/utils.py

@@ -239,7 +239,7 @@ def efficientnet_params(model_name):
     return params_dict[model_name]
 
 
-class BlockDecoder(object):
+class BlockDecoder:
     """Block Decoder for readability, straight from the official TensorFlow repository"""
 
     @staticmethod

paddle/phi/api/yaml/generator/api_base.py

@@ -19,7 +19,7 @@
 PREFIX_META_TENSOR_NAME = 'meta_'
 
 
-class BaseAPI(object):
+class BaseAPI:
     def __init__(self, api_item_yaml):
         self.api = self.get_api_name(api_item_yaml)
 

python/paddle/audio/functional/window.py

@@ -19,7 +19,7 @@
 from paddle import Tensor
 
 
-class WindowFunctionRegister(object):
+class WindowFunctionRegister:
     def __init__(self):
         self._functions_dict = dict()
 

python/paddle/autograd/py_layer.py

@@ -21,7 +21,7 @@
 __all__ = []
 
 
-class LegacyPyLayerContext(object):
+class LegacyPyLayerContext:
     """
     The object of this class is a context that is used in PyLayer to enhance the function.
 
@@ -131,7 +131,7 @@ def __new__(cls, name, temp_bases, attrs):
     return type.__new__(impl, "impl", (), {})
 
 
-class CPyLayer(object):
+class CPyLayer:
     @classmethod
     @dygraph_only
     def apply(cls, *args, **kwargs):
@@ -336,7 +336,7 @@ def backward(ctx, dy):
         )
 
 
-class EagerPyLayerContext(object):
+class EagerPyLayerContext:
     def save_for_backward(self, *tensors):
         """
         Saves given tensors that backward need. Use ``saved_tensor`` in the `backward` to get the saved tensors.

python/paddle/dataset/imikolov.py

@@ -31,7 +31,7 @@
 MD5 = '30177ea32e27c525793142b6bf2c8e2d'
 
 
-class DataType(object):
+class DataType:
     NGRAM = 1
     SEQ = 2
 

python/paddle/dataset/movielens.py

@@ -38,7 +38,7 @@
 MD5 = 'c4d9eecfca2ab87c1945afe126590906'
 
 
-class MovieInfo(object):
+class MovieInfo:
     """
     Movie id, title and categories information are stored in MovieInfo.
     """
@@ -69,7 +69,7 @@ def __repr__(self):
         return self.__str__()
 
 
-class UserInfo(object):
+class UserInfo:
     """
     User id, gender, age, and job information are stored in UserInfo.
     """

python/paddle/distributed/auto_parallel/cluster_v2.py

@@ -116,7 +116,7 @@ def mesh(self):
         return self._mesh
 
 
-# class Cluster(object):
+# class Cluster:
 #     """
 #     The cluster represents the hardware resource.
 #     """

python/paddle/distributed/auto_parallel/converter.py

@@ -19,7 +19,7 @@
 from ..utils.log_utils import get_logger
 
 
-class Converter(object):
+class Converter:
     """
     Converter is a class object for auto parallel to convert tensors from
     one parallel strategy to another one. Tensors will merge and slice value

python/paddle/distributed/auto_parallel/cost_model.py

@@ -35,7 +35,7 @@ class CostNodeType(Enum):
     NOP = 5
 
 
-class Cost(object):
+class Cost:
     def __init__(self):
         self.runtime = None
         self.static_mem = None
@@ -49,7 +49,7 @@ class CostModelMode(Enum):
     MIXED = 3
 
 
-class CostNode(object):
+class CostNode:
     def __init__(self, node, node_type, id=None):
         self.id = id
         self.node = node
@@ -172,7 +172,7 @@ def init_comp_cost(self, cost_data):
             self.cost = 0.0
 
 
-class PipeEvent(object):
+class PipeEvent:
     def __init__(self, stage_id, event_name, duration, start_time=-1):
         self.stage_id = stage_id
         self.name = event_name
@@ -181,7 +181,7 @@ def __init__(self, stage_id, event_name, duration, start_time=-1):
         self.e_time = -1
 
 
-class CostModel(object):
+class CostModel:
     def __init__(
         self,
         mode=CostModelMode.BENCHMARKING,

python/paddle/distributed/auto_parallel/dist_context.py

@@ -1146,7 +1146,7 @@ def prepare_context(self, src_op):
         return kinputs, koutputs
 
 
-class BlockState(object):
+class BlockState:
     def __init__(self):
         self.nblock = 0
         self.forward_indices = []

python/paddle/distributed/auto_parallel/helper.py

@@ -192,7 +192,7 @@ def clear(self):
         self.states = defaultdict(bool)
 
 
-class ProgramHelper(object):
+class ProgramHelper:
     """
     A Helper class for Engine to provides different Program IR according specified 'mode'.
     """

python/paddle/distributed/auto_parallel/interface.py

@@ -220,7 +220,7 @@ def __call__(self, *args, **kwargs):
 _g_collections = {}
 
 
-class CollectionNames(object):
+class CollectionNames:
     FETCHES = "fetches"
     LOGGING = "logging"
 

python/paddle/distributed/auto_parallel/partitioner.py

@@ -32,7 +32,7 @@
 ]
 
 
-class Partitioner(object):
+class Partitioner:
     """
     warning:: Partitioner is experimental and subject to change.
 

python/paddle/distributed/auto_parallel/process_mesh.py

@@ -39,7 +39,7 @@ def reset_current_process_mesh():
     _g_current_process_mesh = _g_previous_process_mesh
 
 
-class ProcessMesh(object):
+class ProcessMesh:
     """
     The `Processmesh` object describes the topology of the used processes.
 

python/paddle/distributed/auto_parallel/strategy.py

@@ -16,7 +16,7 @@
 from . import constants
 
 
-class BaseConfig(object):
+class BaseConfig:
     def __init__(self, category, config_dict=None):
         self._category = category
         self._config_dict = None

python/paddle/distributed/auto_parallel/tuner/config.py

@@ -25,7 +25,7 @@ def _get_pass_config(strategy, pass_name):
     return config
 
 
-class TuningConfig(object):
+class TuningConfig:
     """
     A uniform config wrap:
     distributed strategy: the user defined configuration for optimization pass

python/paddle/distributed/auto_parallel/tuner/recorder.py

@@ -18,7 +18,7 @@
 import numpy as np
 
 
-class MetricRecord(object):
+class MetricRecord:
     """
     One record for a single metric at a given execution step.
     """
@@ -62,7 +62,7 @@ def __repr__(self):
         return "MetricRecord(value={}, step={})".format(self.value, self.step)
 
 
-class MetricRecords(object):
+class MetricRecords:
     """
     Records of a single metric across different executions.
     """
@@ -143,7 +143,7 @@ def from_state(cls, state):
         return records
 
 
-class MetricsRecorder(object):
+class MetricsRecorder:
     """
     Record the values for all metrics.
     """

python/paddle/distributed/auto_parallel/tuner/storable.py

@@ -18,7 +18,7 @@
 import json
 
 
-class Storable(object):
+class Storable:
     def get_state(self):
         raise NotImplementedError
 

python/paddle/distributed/auto_parallel/tuner/tunable_space.py

@@ -22,7 +22,7 @@
 from .tunable_variable import FloatRange
 
 
-class TunableSpace(object):
+class TunableSpace:
     """
     A TunableSpace is constructed by the tunable variables.
     """

python/paddle/distributed/auto_parallel/tuner/tunable_variable.py

@@ -18,7 +18,7 @@
 import numpy as np
 
 
-class TunableVariable(object):
+class TunableVariable:
     """
     Tunablevariable base class.
     """

python/paddle/distributed/collective.py

@@ -1702,7 +1702,7 @@ def irecv(tensor, src=None, group=None):
         raise RuntimeError("Only support eager dygraph mode.")
 
 
-class P2POp(object):
+class P2POp:
     """
     A class that makes point-to-point operations for "batch_isend_irecv".
 

python/paddle/distributed/elastic.py

@@ -16,7 +16,7 @@
 import os
 
 
-class Command(object):
+class Command:
     def __init__(self, server, name):
         import etcd3
 

python/paddle/distributed/entry_attr.py

@@ -15,7 +15,7 @@
 __all__ = []
 
 
-class EntryAttr(object):
+class EntryAttr:
     """
     Entry Config for paddle.static.nn.sparse_embedding with Parameter Server.
 

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -65,7 +65,7 @@ def check_configs_key(msg, config, field_name):
         assert key in key_list, "key:{} not in {}".format(key, field_name)
 
 
-class DistributedJobInfo(object):
+class DistributedJobInfo:
     """
     DistributedJobInfo will serialize all distributed training information
     Just for inner use: 1) debug 2) replicate experiments
@@ -106,7 +106,7 @@ def _set_distributed_strategy(self, dist_strategy):
 ReduceStrategyFleet = int
 
 
-class DistributedStrategy(object):
+class DistributedStrategy:
     __lock_attr = False
 
     def __init__(self):

python/paddle/distributed/fleet/base/meta_optimizer_factory.py

@@ -26,7 +26,7 @@
 meta_optimizer_names.remove("HeterParallelOptimizer")
 
 
-class MetaOptimizerFactory(object):
+class MetaOptimizerFactory:
     def __init__(self):
         pass
 

python/paddle/distributed/fleet/base/role_maker.py

@@ -35,7 +35,7 @@ class Role:
     COORDINATOR = 5
 
 
-class Gloo(object):
+class Gloo:
     """
     Gloo is a universal class for barrier and collective communication
     """
@@ -383,7 +383,7 @@ def all_gather(self, input, comm_world="worker"):
         return output
 
 
-class RoleMakerBase(object):
+class RoleMakerBase:
     """
     RoleMakerBase is a base class for assigning a role to current process
     in distributed training.

python/paddle/distributed/fleet/base/runtime_factory.py

@@ -17,7 +17,7 @@
 __all__ = []
 
 
-class RuntimeFactory(object):
+class RuntimeFactory:
     def __init__(self):
         pass
 

python/paddle/distributed/fleet/base/strategy_compiler.py

@@ -106,7 +106,7 @@ def maximum_path_len_algo(optimizer_list):
     return candidate
 
 
-class StrategyCompilerBase(object):
+class StrategyCompilerBase:
     def __init__(self):
         pass
 

python/paddle/distributed/fleet/base/topology.py

@@ -23,7 +23,7 @@
 _HYBRID_PARALLEL_GROUP = None
 
 
-class ParallelMode(object):
+class ParallelMode:
     """
     There are all the parallel modes currently supported:
     - DATA_PARALLEL: Distribute input data to different devices.
@@ -47,7 +47,7 @@ class ParallelMode(object):
     SHARDING_PARALLEL = 3
 
 
-class CommunicateTopology(object):
+class CommunicateTopology:
     def __init__(
         self,
         hybrid_group_names=["data", "pipe", "sharding", "model"],
@@ -133,7 +133,7 @@ def get_rank_from_stage(self, global_rank, **kwargs):
         return self.get_rank(**tf)
 
 
-class HybridCommunicateGroup(object):
+class HybridCommunicateGroup:
     def __init__(self, topology):
         self.nranks = paddle.distributed.get_world_size()
         self.global_rank = paddle.distributed.get_rank()
@@ -410,7 +410,7 @@ def get_rank_from_stage(self, stage_id, **kwargs):
         )
 
 
-class _CommunicateGroup(object):
+class _CommunicateGroup:
     """tmp for static"""
 
     def __init__(self):

python/paddle/distributed/fleet/base/util_factory.py

@@ -31,7 +31,7 @@
 __all__ = []
 
 
-class UtilFactory(object):
+class UtilFactory:
     def _create_util(self, context=None):
         util = UtilBase()
         if context is not None and "valid_strategy" in context:
@@ -41,7 +41,7 @@ def _create_util(self, context=None):
         return util
 
 
-class UtilBase(object):
+class UtilBase:
     def __init__(self):
         self.role_maker = None
         self.dist_strategy = None