[Bugbash] promote pruning v2 (#4733)

examples/model_compress/pruning/mobilenetv2_end2end/utils.py → examples/model_compress/pruning/legacy/mobilenetv2_end2end/utils.py

@@ -7,10 +7,10 @@ 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
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 

examples/model_compress/pruning/speedup/model_speedup.py → examples/model_compress/pruning/legacy/speedup/model_speedup.py

@@ -5,7 +5,7 @@ import time
 import torch
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from cifar10.vgg import VGG
 from mnist.lenet import LeNet
 

examples/model_compress/pruning/transformers/transformer_pruning.py → examples/model_compress/pruning/legacy/transformers/transformer_pruning.py

@@ -9,7 +9,7 @@ import torch
 from torch.utils.data.dataloader import DataLoader
 from tqdm.auto import tqdm
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 from nni.algorithms.compression.pytorch.pruning import TransformerHeadPruner
 
 import datasets

examples/model_compress/pruning/v2/level_pruning_torch.py → examples/model_compress/pruning/level_pruning_torch.py

@@ -14,11 +14,11 @@ import torch
 from torchvision import datasets, transforms
 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
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import LevelPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/movement_pruning_glue.py → examples/model_compress/pruning/movement_pruning_glue.py

@@ -15,7 +15,7 @@ from transformers import (
 )
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import MovementPruner
+from nni.compression.pytorch.pruning import MovementPruner
 
 
 task_to_keys = {

examples/model_compress/pruning/v2/norm_pruning_torch.py → examples/model_compress/pruning/norm_pruning_torch.py

@@ -15,11 +15,11 @@ from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
 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
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import L1NormPruner, L2NormPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / '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 → examples/model_compress/pruning/scheduler_torch.py

@@ -4,12 +4,12 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
+from nni.compression.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
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/simple_pruning_torch.py → examples/model_compress/pruning/simple_pruning_torch.py

@@ -4,11 +4,11 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
+from nni.compression.pytorch.pruning import L1NormPruner
 from nni.compression.pytorch.speedup import ModelSpeedup
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py → examples/model_compress/pruning/simulated_anealing_pruning_torch.py

@@ -13,10 +13,10 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import SimulatedAnnealingPruner
+from nni.compression.pytorch.pruning import SimulatedAnnealingPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/slim_pruning_torch.py → examples/model_compress/pruning/slim_pruning_torch.py

@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 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 SlimPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import SlimPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")