refine package import

nn_meter/__init__.py

@@ -7,20 +7,26 @@ try:
 except ModuleNotFoundError:
     __version__ = 'UNKNOWN'
 
-from .nn_meter import (
+import logging
-    nnMeter,
+from functools import partial, partialmethod
+
+from .predictor import (
+    nnMeterPredictor,
     load_latency_predictor,
     list_latency_predictors,
+    latency_metrics
+)
+from .ir_converter import (
     model_file_to_graph,
-    model_to_graph,
+    model_to_graph
+)
+from .utils import (
     create_user_configs,
     change_user_data_folder
 )
-from .utils.utils import download_from_url
+from .dataset import bench_dataset
-from .predictor import latency_metrics
+from .utils import download_from_url
-from .dataset import bench_dataset # TODO: add GNNDataloader and GNNDataset here @wenxuan
+
-import logging
-from functools import partial, partialmethod
 
 logging.KEYINFO = 22
 logging.addLevelName(logging.KEYINFO, 'KEYINFO')

nn_meter/dataset/bench_dataset.py

@@ -1,13 +1,12 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 import os, sys
-from nn_meter.predictor import latency_metrics
+import logging
+import jsonlines
 from glob import glob
 
-from nn_meter.nn_meter import list_latency_predictors, load_latency_predictor, get_user_data_folder
+from nn_meter.predictor import latency_metrics, list_latency_predictors, load_latency_predictor
-from nn_meter import download_from_url
+from nn_meter.utils import download_from_url, get_user_data_folder
-import jsonlines
-import logging
 
 
 __user_dataset_folder__ = os.path.join(get_user_data_folder(), 'dataset')

nn_meter/dataset/gnn_dataloader.py

@@ -1,16 +1,18 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-import torch
-import jsonlines
 import os
 import random
+import torch
+import jsonlines
 from .bench_dataset import bench_dataset
-from nn_meter.nn_meter import get_user_data_folder
+from nn_meter.utils import get_user_data_folder
-from nn_meter.utils.utils import try_import_dgl
+from nn_meter.utils.import_package import try_import_dgl
+
 
 RAW_DATA_URL = "https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/datasets.zip"
 __user_dataset_folder__ = os.path.join(get_user_data_folder(), 'dataset')
 
+
 hws = [
     "cortexA76cpu_tflite21",
     "adreno640gpu_tflite21",

nn_meter/ir_converter/frozenpb_converter/frozenpb_converter.py

@@ -2,10 +2,10 @@
 # Licensed under the MIT license.
 import numpy as np
 
-from nn_meter.utils.graph_tool import ModelGraph
 from .frozenpb_parser import FrozenPbParser
 from .shape_inference import ShapeInference
 from .shape_fetcher import ShapeFetcher
+from nn_meter.utils.graph_tool import ModelGraph
 
 class FrozenPbConverter:
     def __init__(self, file_name):

nn_meter/ir_converter/frozenpb_converter/frozenpb_parser.py

@@ -1,12 +1,13 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.utils import try_import_tensorflow
-from .protobuf_helper import ProtobufHelper
-from .shape_fetcher import ShapeFetcher
-import copy
 import re
+import copy
 import logging
 
+from .protobuf_helper import ProtobufHelper
+from nn_meter.utils.import_package import try_import_tensorflow
+
+
 logging = logging.getLogger(__name__)
 
 

nn_meter/ir_converter/frozenpb_converter/shape_fetcher.py

@@ -1,9 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.utils import try_import_tensorflow
 import numpy as np
 from typing import List
-
+from nn_meter.utils.utils import try_import_tensorflow
 
 class ShapeFetcher:
     def __init__(self, input_graph):

nn_meter/ir_converter/frozenpb_converter/shape_inference.py

@@ -1,10 +1,10 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from .protobuf_helper import ProtobufHelper as ph
 from functools import reduce
 import copy
 import math
 import logging
+from .protobuf_helper import ProtobufHelper as ph
 
 
 logging = logging.getLogger(__name__)

nn_meter/ir_converter/onnx_converter/converter.py

@@ -1,11 +1,11 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.utils import try_import_onnx
+import logging
 import networkx as nx
+from itertools import chain
 from .utils import get_tensor_shape
 from .constants import SLICE_TYPE
-from itertools import chain
+from nn_meter.utils.import_package import try_import_onnx
-import logging
 
 
 class OnnxConverter:

nn_meter/ir_converter/onnx_converter/utils.py

@@ -1,7 +1,5 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-
-
 def get_tensor_shape(tensor):
     shape = []
     for dim in tensor.type.tensor_type.shape.dim:

nn_meter/ir_converter/torch_converter/converter.py

@@ -1,11 +1,9 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.utils import try_import_onnx, try_import_torch, try_import_onnxsim, try_import_nni
 import tempfile
-from nn_meter.ir_converter.onnx_converter import OnnxConverter
+from ..onnx_converter import OnnxConverter
-
-
 from .opset_map import nni_attr_map, nni_type_map
+from nn_meter.utils.import_package import try_import_onnx, try_import_torch, try_import_onnxsim, try_import_nni
 
 
 def _nchw_to_nhwc(shapes):

nn_meter/ir_converter/utils.py

@@ -2,11 +2,12 @@
 # Licensed under the MIT license.
 import json
 import logging
-from nn_meter.utils.utils import try_import_onnx, try_import_torch, try_import_torchvision_models
+from nn_meter.utils.import_package import try_import_onnx, try_import_torch, try_import_torchvision_models
 from .onnx_converter import OnnxConverter
 from .frozenpb_converter import FrozenPbConverter
 from .torch_converter import NNIBasedTorchConverter, OnnxBasedTorchConverter, NNIIRConverter
 
+
 def model_file_to_graph(filename: str, model_type: str, input_shape=(1, 3, 224, 224), apply_nni=False):
     """
     read the given file and convert the model in the file content to nn-Meter IR graph object 
@@ -106,10 +107,12 @@ def onnx_model_to_graph(model):
     converter = OnnxConverter(model)
     return converter.convert()
 
+
 def nni_model_to_graph(model):
     converter = NNIIRConverter(model)
     return converter.convert()
 
+
 def torch_model_to_graph(model, input_shape=(1, 3, 224, 224), apply_nni=False):
     torch = try_import_torch()
     args = torch.randn(*input_shape)

nn_meter/kernel_detector/__init__.py

@@ -1,3 +1,3 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from .detection.detector import KernelDetector
+from .kernel_detector import KernelDetector

nn_meter/kernel_detector/detection/__init__.py

@@ -1,2 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.

nn_meter/kernel_detector/fusionlib/utils.py

@@ -3,7 +3,7 @@
 import os
 import json
 from nn_meter.utils.graph_tool import ModelGraph
-from nn_meter.kernel_detector.utils.ir_tools import convert_nodes
+from ..utils.ir_tools import convert_nodes
 
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))

nn_meter/kernel_detector/kernel_detector.py

@@ -1,11 +1,10 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.kernel_detector.rulelib.rule_reader import RuleReader
-from nn_meter.kernel_detector.rulelib.rule_splitter import RuleSplitter
 from nn_meter.utils.graph_tool import ModelGraph
-from nn_meter.kernel_detector.utils.constants import DUMMY_TYPES
+from .utils.constants import DUMMY_TYPES
-from nn_meter.kernel_detector.utils.ir_tools import convert_nodes
+from .utils.ir_tools import convert_nodes
-# import logging
+from .rulelib.rule_reader import RuleReader
+from .rulelib.rule_splitter import RuleSplitter
 
 
 class KernelDetector:

nn_meter/kernel_detector/rulelib/rule_reader.py

@@ -1,8 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 import json
+from ..fusionlib import get_fusion_unit
 from nn_meter.utils.graph_tool import ModelGraph
-from nn_meter.kernel_detector.fusionlib import get_fusion_unit
 
 
 class RuleReader:

nn_meter/kernel_detector/rulelib/rule_splitter.py

@@ -1,9 +1,9 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 from .rule_reader import RuleReader
+from ..utils.match_helper import MatchHelper
+from ..utils.fusion_aware_graph import FusionAwareGraph
 from nn_meter.utils.graph_tool import ModelGraph
-from nn_meter.kernel_detector.utils.match_helper import MatchHelper
-from nn_meter.kernel_detector.utils.fusion_aware_graph import FusionAwareGraph
 
 
 class RuleSplitter:

nn_meter/kernel_detector/utils/fusion_aware_graph.py

@@ -1,8 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.graph_tool import ModelGraph
-from .union_find import UF
 import networkx as nx
+from .union_find import UF
+from nn_meter.utils.graph_tool import ModelGraph
 
 
 class FusionAwareGraph:

nn_meter/nn_meter_cli.py

@@ -5,12 +5,7 @@ import os
 import sys
 import argparse
 import logging
-from nn_meter.nn_meter import *
+from nn_meter import *
-
-__user_config_folder__ = os.path.expanduser('~/.nn_meter/config')
-__user_data_folder__ = os.path.expanduser('~/.nn_meter/data')
-
-__predictors_cfg_filename__ = 'predictors.yaml'
 
 
 def list_latency_predictors_cli():
@@ -62,6 +57,7 @@ def apply_latency_predictor_cli(args):
     
     return result
 
+
 def get_nnmeter_ir_cli(args):
     """convert pb file or onnx file to nn-Meter IR graph according to the command line interface arguments
     """

nn_meter/predictor/__init__.py

@@ -1,5 +1,4 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from .predictors.utils import latency_metrics
+from .prediction.utils import latency_metrics
-
+from .nn_meter_predictor import nnMeterPredictor, list_latency_predictors, load_latency_predictor
-

nn_meter/predictor/nn_meter_predictor.py

@@ -1,18 +1,18 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from glob import glob
-from nn_meter.predictor.predictors.predict_by_kernel import nn_predict
-from nn_meter.kernel_detector import KernelDetector
-from nn_meter.ir_converter import model_file_to_graph, model_to_graph
-from .utils import loading_to_local
-
-import yaml
 import os
+import yaml
 import pkg_resources
 from shutil import copyfile
 from packaging import version
 import logging
+
+from .utils import loading_to_local
+from .prediction.predict_by_kernel import nn_predict
+from nn_meter.kernel_detector import KernelDetector
 from nn_meter.utils import load_config_file, get_user_data_folder
+from nn_meter.ir_converter import model_file_to_graph, model_to_graph
+
 
 __predictors_cfg_filename__ = 'predictors.yaml'
 

nn_meter/predictor/prediction/extract_feature.py

@@ -1,8 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
+import logging
 import numpy as np
 from sklearn.metrics import mean_squared_error
-import logging
 
 
 def get_flop(input_channel, output_channel, k, H, W, stride):

nn_meter/predictor/prediction/utils.py

@@ -1,9 +1,9 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-
 import numpy as np
 from sklearn.metrics import mean_squared_error
 
+
 def get_kernel_name(optype):
     """
     for many similar kernels, we use one kernel predictor since their latency difference is negligible,
@@ -34,6 +34,7 @@ def get_kernel_name(optype):
 
     return optype
 
+
 def get_accuracy(y_pred, y_true, threshold=0.01):
     a = (y_true - y_pred) / y_true
     b = np.where(abs(a) <= threshold)

nn_meter/predictor/utils.py

@@ -7,7 +7,7 @@ from zipfile import ZipFile
 from tqdm import tqdm
 import requests
 import logging
-from nn_meter.utils.utils import download_from_url
+from nn_meter.utils import download_from_url
 
 
 def loading_to_local(pred_info, dir="data/predictorzoo"):

nn_meter/utils/__init__.py

@@ -6,3 +6,4 @@ from config_manager import (
     change_user_data_folder,
     load_config_file
 )
+from utils import download_from_url

nn_meter/utils/config_manager.py

@@ -1,14 +1,9 @@
-from glob import glob
-from nn_meter.predictor.predictors.predict_by_kernel import nn_predict
-from nn_meter.kernel_detector import KernelDetector
-from nn_meter.ir_converter import model_file_to_graph, model_to_graph
-from nn_meter.predictor.load_predictors import loading_to_local
-
 import yaml
 import os
+import logging
 import pkg_resources
 from shutil import copyfile
-import logging
+
 
 __user_config_folder__ = os.path.expanduser('~/.nn_meter/config')
 __default_user_data_folder__ = os.path.expanduser('~/.nn_meter/data')

nn_meter/utils/import_package.py

@@ -1,7 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from packaging import version
 import logging
+from packaging import version
 
 
 def try_import_onnx(require_version = ["1.9.0"]):

nn_meter/utils/utils.py

@@ -20,7 +20,7 @@ def download_from_url(urladdr, ppath):
     if not os.path.isdir(ppath):
         os.makedirs(ppath)
 
-    # logging.keyinfo(f'Download from {urladdr}')
+    logging.keyinfo(f'Download from {urladdr}')
     response = requests.get(urladdr, stream=True)
     total_size_in_bytes = int(response.headers.get("content-length", 0))
     block_size = 2048  # 2 Kibibyte