[v2.0] Refactor code hierarchy (part 2) (#2987)

nni/metis_tuner/lib_acquisition_function.py → nni/algorithms/hpo/metis_tuner/lib_acquisition_function.py

@@ -11,7 +11,7 @@ import numpy
 from scipy.stats import norm
 from scipy.optimize import minimize
 
-import nni.metis_tuner.lib_data as lib_data
+from . import lib_data
 
 
 def next_hyperparameter_expected_improvement(fun_prediction,

nni/metis_tuner/metis_tuner.py → nni/algorithms/hpo/metis_tuner/metis_tuner.py

@@ -15,14 +15,14 @@ import numpy as np
 from schema import Schema, Optional
 
 from nni import ClassArgsValidator
-import nni.metis_tuner.lib_constraint_summation as lib_constraint_summation
-import nni.metis_tuner.lib_data as lib_data
-import nni.metis_tuner.Regression_GMM.CreateModel as gmm_create_model
-import nni.metis_tuner.Regression_GMM.Selection as gmm_selection
-import nni.metis_tuner.Regression_GP.CreateModel as gp_create_model
-import nni.metis_tuner.Regression_GP.OutlierDetection as gp_outlier_detection
-import nni.metis_tuner.Regression_GP.Prediction as gp_prediction
-import nni.metis_tuner.Regression_GP.Selection as gp_selection
+from . import lib_constraint_summation
+from . import lib_data
+from .Regression_GMM import CreateModel as gmm_create_model
+from .Regression_GMM import Selection as gmm_selection
+from .Regression_GP import CreateModel as gp_create_model
+from .Regression_GP import OutlierDetection as gp_outlier_detection
+from .Regression_GP import Prediction as gp_prediction
+from .Regression_GP import Selection as gp_selection
 from nni.tuner import Tuner
 from nni.utils import OptimizeMode, extract_scalar_reward
 

nni/networkmorphism_tuner/bayesian.py → nni/algorithms/hpo/networkmorphism_tuner/bayesian.py

@@ -13,9 +13,9 @@ from scipy.optimize import linear_sum_assignment
 from sklearn.metrics.pairwise import rbf_kernel
 
 from nni.utils import OptimizeMode
-from nni.networkmorphism_tuner.graph_transformer import transform
-from nni.networkmorphism_tuner.utils import Constant
-from nni.networkmorphism_tuner.layers import is_layer
+from .graph_transformer import transform
+from .utils import Constant
+from .layers import is_layer
 
 
 def layer_distance(a, b):

nni/networkmorphism_tuner/graph.py → nni/algorithms/hpo/networkmorphism_tuner/graph.py

@@ -9,7 +9,7 @@ from queue import Queue
 import numpy as np
 import torch
 
-from nni.networkmorphism_tuner.layer_transformer import (
+from .layer_transformer import (
     add_noise,
     wider_bn,
     wider_next_conv,
@@ -20,7 +20,7 @@ from nni.networkmorphism_tuner.layer_transformer import (
     init_conv_weight,
     init_bn_weight,
 )
-from nni.networkmorphism_tuner.layers import (
+from .layers import (
     StubAdd,
     StubConcatenate,
     StubReLU,
@@ -36,7 +36,7 @@ from nni.networkmorphism_tuner.layers import (
     layer_description_extractor,
     layer_description_builder,
 )
-from nni.networkmorphism_tuner.utils import Constant
+from .utils import Constant
 
 
 class NetworkDescriptor:

nni/networkmorphism_tuner/graph_transformer.py → nni/algorithms/hpo/networkmorphism_tuner/graph_transformer.py

@@ -5,8 +5,8 @@ from copy import deepcopy
 
 from random import randrange, sample
 
-from nni.networkmorphism_tuner.graph import NetworkDescriptor
-from nni.networkmorphism_tuner.layers import (
+from .graph import NetworkDescriptor
+from .layers import (
     StubDense,
     StubReLU,
     get_batch_norm_class,
@@ -15,7 +15,7 @@ from nni.networkmorphism_tuner.layers import (
     get_pooling_class,
     is_layer,
 )
-from nni.networkmorphism_tuner.utils import Constant
+from .utils import Constant
 
 
 def to_wider_graph(graph):

nni/networkmorphism_tuner/layer_transformer.py → nni/algorithms/hpo/networkmorphism_tuner/layer_transformer.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from nni.networkmorphism_tuner.layers import (
+from .layers import (
     StubDense,
     StubReLU,
     get_batch_norm_class,

nni/networkmorphism_tuner/layers.py → nni/algorithms/hpo/networkmorphism_tuner/layers.py

@@ -7,7 +7,7 @@ from collections.abc import Iterable
 import torch
 from torch import nn
 from torch.nn import functional
-from nni.networkmorphism_tuner.utils import Constant
+from .utils import Constant
 
 
 class AvgPool(nn.Module):

nni/networkmorphism_tuner/networkmorphism_tuner.py → nni/algorithms/hpo/networkmorphism_tuner/networkmorphism_tuner.py

@@ -10,10 +10,10 @@ import os
 from schema import Optional, Schema
 from nni.tuner import Tuner
 from nni.utils import OptimizeMode, extract_scalar_reward
-from nni.networkmorphism_tuner.bayesian import BayesianOptimizer
-from nni.networkmorphism_tuner.nn import CnnGenerator, MlpGenerator
-from nni.networkmorphism_tuner.utils import Constant
-from nni.networkmorphism_tuner.graph import graph_to_json, json_to_graph
+from .bayesian import BayesianOptimizer
+from .nn import CnnGenerator, MlpGenerator
+from .utils import Constant
+from .graph import graph_to_json, json_to_graph
 from nni import ClassArgsValidator
 
 logger = logging.getLogger("NetworkMorphism_AutoML")

nni/networkmorphism_tuner/nn.py → nni/algorithms/hpo/networkmorphism_tuner/nn.py

@@ -3,14 +3,14 @@
 
 from abc import abstractmethod
 
-from nni.networkmorphism_tuner.graph import Graph
-from nni.networkmorphism_tuner.layers import (StubDense, StubDropout1d,
+from .graph import Graph
+from .layers import (StubDense, StubDropout1d,
                                               StubReLU, get_batch_norm_class,
                                               get_conv_class,
                                               get_dropout_class,
                                               get_global_avg_pooling_class,
                                               get_pooling_class)
-from nni.networkmorphism_tuner.utils import Constant
+from .utils import Constant
 
 
 class NetworkGenerator: