[Bugbash] update example import path (#4423)

examples/model_compress/pruning/amc/amc_train.py

@@ -22,7 +22,8 @@ from nni.compression.pytorch import ModelSpeedup
 from data import get_dataset
 from utils import AverageMeter, accuracy, progress_bar
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 

examples/model_compress/pruning/auto_pruners_torch.py

@@ -8,6 +8,7 @@ In this example, we present the usage of automatic pruners (NetAdapt, AutoCompre
 
 import argparse
 import os
+import sys
 import json
 import torch
 from torch.optim.lr_scheduler import StepLR, MultiStepLR
@@ -18,12 +19,13 @@ from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner,
 from nni.compression.pytorch import ModelSpeedup
 from nni.compression.pytorch.utils.counter import count_flops_params
 
-import sys
-sys.path.append('../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 from cifar10.resnet import ResNet18, ResNet50
 
+
 def get_data(dataset, data_dir, batch_size, test_batch_size):
     '''
     get data

examples/model_compress/pruning/basic_pruners_torch.py

@@ -17,7 +17,8 @@ import torch
 from torch.optim.lr_scheduler import StepLR, MultiStepLR
 from torchvision import datasets, transforms
 
-sys.path.append('../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 from cifar10.resnet import ResNet18

examples/model_compress/pruning/finetune_kd_torch.py

@@ -8,23 +8,20 @@ Run basic_pruners_torch.py first to get the masks of the pruned model. Then pass
 
 import argparse
 import os
-import time
+import sys
 from copy import deepcopy
 
-import nni
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.optim as optim
 from nni.compression.pytorch import ModelSpeedup
-from torch.optim.lr_scheduler import MultiStepLR, StepLR
-from torchvision import datasets, transforms
+from torch.optim.lr_scheduler import MultiStepLR
 from basic_pruners_torch import get_data
 
-import sys
-sys.path.append('../models')
-from cifar10.vgg import VGG
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from mnist.lenet import LeNet
+from cifar10.vgg import VGG
 
 class DistillKL(nn.Module):
     """Distilling the Knowledge in a Neural Network"""
@@ -73,7 +70,6 @@ def get_model_optimizer_scheduler(args, device, test_loader, criterion):
     m_speedup = ModelSpeedup(model_s, dummy_input, args.mask_path, device)
     m_speedup.speedup_model()
 
-
     module_list = nn.ModuleList([])
     module_list.append(model_s)
     module_list.append(model_t)

examples/model_compress/pruning/mobilenetv2_end2end/utils.py

@@ -2,14 +2,15 @@
 # Licensed under the MIT license.
 
 import os
+import sys
 import torch
 from torch.utils.data import Dataset, DataLoader
 import torchvision.transforms as transforms
 import numpy as np
 from nni.compression.pytorch.utils.counter import count_flops_params
 
-import sys
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 

examples/model_compress/pruning/speedup/model_speedup.py

 import os
+import sys
 import argparse
 import time
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import datasets, transforms
 
-import sys
-sys.path.append('../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 from mnist.lenet import LeNet
 

examples/model_compress/pruning/v2/activation_pruning_torch.py

@@ -19,7 +19,8 @@ from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ActivationAPoZRankPruner, ActivationMeanRankPruner
 from nni.algorithms.compression.v2.pytorch.utils import trace_parameters
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/admm_pruning_torch.py

@@ -18,7 +18,8 @@ from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ADMMPruner
 from nni.algorithms.compression.v2.pytorch.utils import trace_parameters
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/amc_pruning_torch.py

@@ -8,7 +8,8 @@ from torch.optim.lr_scheduler import MultiStepLR
 from nni.algorithms.compression.v2.pytorch.pruning import AMCPruner
 from nni.compression.pytorch.utils.counter import count_flops_params
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/auto_compress_pruner.py

@@ -7,7 +7,8 @@ from torchvision import datasets, transforms
 from nni.algorithms.compression.v2.pytorch.pruning import AutoCompressPruner
 from nni.algorithms.compression.v2.pytorch.utils import trace_parameters
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/fpgm_pruning_torch.py

@@ -18,7 +18,8 @@ from nni.compression.pytorch import ModelSpeedup
 from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import FPGMPruner
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/iterative_pruning_torch.py

@@ -19,7 +19,8 @@ from nni.algorithms.compression.v2.pytorch.pruning import (
     LotteryTicketPruner
 )
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/level_pruning_torch.py

@@ -17,7 +17,8 @@ from torch.optim.lr_scheduler import MultiStepLR
 from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import LevelPruner
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/norm_pruning_torch.py

@@ -18,7 +18,8 @@ from nni.compression.pytorch import ModelSpeedup
 from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import L1NormPruner, L2NormPruner
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/scheduler_torch.py

@@ -8,7 +8,8 @@ from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
 from nni.algorithms.compression.v2.pytorch.pruning.tools import AGPTaskGenerator
 from nni.algorithms.compression.v2.pytorch.pruning.basic_scheduler import PruningScheduler
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/simple_pruning_torch.py

@@ -7,7 +7,8 @@ from torchvision import datasets, transforms
 from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
 from nni.compression.pytorch.speedup import ModelSpeedup
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py

@@ -15,7 +15,8 @@ from torchvision import datasets, transforms
 
 from nni.algorithms.compression.v2.pytorch.pruning import SimulatedAnnealingPruner
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/slim_pruning_torch.py

@@ -19,7 +19,8 @@ from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import SlimPruner
 from nni.algorithms.compression.v2.pytorch.utils import trace_parameters
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/taylorfo_pruning_torch.py

@@ -19,7 +19,8 @@ from nni.compression.pytorch.utils.counter import count_flops_params
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import TaylorFOWeightPruner
 from nni.algorithms.compression.v2.pytorch.utils import trace_parameters
 
-sys.path.append('../../models')
+from pathlib import Path
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")