ruff linting

examples/FedSimSiam/client/data.py

@@ -1,11 +1,10 @@
 import os
 from math import floor
 
+import numpy as np
 import torch
 import torchvision
-import torchvision.transforms as transforms
-import numpy as np
-import os
+from torchvision import transforms
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
 abs_path = os.path.abspath(dir_path)

examples/FedSimSiam/client/model.py

@@ -1,9 +1,9 @@
-import torch.nn.functional as F
-from torchvision.models import resnet18
-import torch.nn as nn
 import collections
 
 import torch
+import torch.nn.functional as f
+from torch import nn
+from torchvision.models import resnet18
 
 from fedn.utils.helpers.helpers import get_helper
 
@@ -14,12 +14,12 @@
 def D(p, z, version='simplified'):  # negative cosine similarity
     if version == 'original':
         z = z.detach()  # stop gradient
-        p = F.normalize(p, dim=1)  # l2-normalize
-        z = F.normalize(z, dim=1)  # l2-normalize
+        p = f.normalize(p, dim=1)  # l2-normalize
+        z = f.normalize(z, dim=1)  # l2-normalize
         return -(p*z).sum(dim=1).mean()
 
     elif version == 'simplified':  # same thing, much faster. Scroll down, speed test in __main__
-        return - F.cosine_similarity(p, z.detach(), dim=-1).mean()
+        return - f.cosine_similarity(p, z.detach(), dim=-1).mean()
     else:
         raise Exception
 

examples/FedSimSiam/client/monitoring.py

@@ -1,23 +1,22 @@
-""" 
-knn monitor as in InstDisc https://arxiv.org/abs/1805.01978.
+""" knn monitor as in InstDisc https://arxiv.org/abs/1805.01978.
 This implementation follows http://github.com/zhirongw/lemniscate.pytorch and https://github.com/leftthomas/SimCLR
 """
-import torch.nn.functional as F
 import torch
+import torch.nn.functional as f
 
 
-def knn_monitor(net, memory_data_loader, test_data_loader, epoch, k=200, t=0.1, hide_progress=False):
+def knn_monitor(net, memory_data_loader, test_data_loader, k=200, t=0.1):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     net = net.to(device)
     net.eval()
     classes = len(memory_data_loader.dataset.classes)
-    total_top1, total_top5, total_num, feature_bank = 0.0, 0.0, 0, []
+    total_top1, total_num, feature_bank = 0.0, 0, []
     with torch.no_grad():
         # generate feature bank
         for data, target in memory_data_loader:
             # feature = net(data.cuda(non_blocking=True))
             feature = net(data.to(device))
-            feature = F.normalize(feature, dim=1)
+            feature = f.normalize(feature, dim=1)
             feature_bank.append(feature)
         # [D, N]
         feature_bank = torch.cat(feature_bank, dim=0).t().contiguous()
@@ -26,11 +25,10 @@ def knn_monitor(net, memory_data_loader, test_data_loader, epoch, k=200, t=0.1,
             memory_data_loader.dataset.targets, device=feature_bank.device)
         # loop test data to predict the label by weighted knn search
         for data, target in test_data_loader:
-            # data, target = data.cuda(
-            #     non_blocking=True), target.cuda(non_blocking=True)
-            data, target = data.to(device), target.to(device)
+            data, target = data.cuda(
+                non_blocking=True), target.cuda(non_blocking=True)
             feature = net(data)
-            feature = F.normalize(feature, dim=1)
+            feature = f.normalize(feature, dim=1)
 
             pred_labels = knn_predict(
                 feature, feature_bank, feature_labels, classes, k, t)

examples/FedSimSiam/client/train.py

@@ -1,17 +1,13 @@
 import os
 import sys
 
-import torch
-from torch.utils.data import Dataset
-from torchvision import transforms
-from PIL import Image
-import torch
-from torch.utils.data import DataLoader
 import numpy as np
-import torch.optim as optim
-
+import torch
 from data import load_data
 from model import load_parameters, save_parameters
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
 from utils import init_lrscheduler
 
 from fedn.utils.helpers.helpers import save_metadata

examples/FedSimSiam/client/utils.py

@@ -1,8 +1,8 @@
-import torch
 import numpy as np
+import torch
 
 
-class LR_Scheduler(object):
+class LrScheduler(object):
     def __init__(self, optimizer, warmup_epochs, warmup_lr, num_epochs, base_lr, final_lr, iter_per_epoch, constant_predictor_lr=False):
         self.base_lr = base_lr
         self.constant_predictor_lr = constant_predictor_lr
@@ -69,7 +69,7 @@ def init_lrscheduler(model, total_epochs, dataloader):
         momentum=momentum,
         weight_decay=weight_decay)
 
-    lr_scheduler = LR_Scheduler(
+    lr_scheduler = LrScheduler(
         optimizer, warmup_epochs, warmup_lr*batch_size/256,
         total_epochs, base_lr*batch_size/256, final_lr*batch_size/256,
         len(dataloader),

examples/FedSimSiam/client/validate.py

@@ -1,16 +1,15 @@
 import os
 import sys
 
-import torch
-from torch import nn
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as transforms
 import numpy as np
+import torch
+from data import load_knn_monitoring_dataset
+from model import load_parameters
+from monitoring import knn_monitor
 from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
 
-from model import load_parameters
-from data import load_data, load_knn_monitoring_dataset
-from monitoring import *
 from fedn.utils.helpers.helpers import save_metrics
 
 dir_path = os.path.dirname(os.path.realpath(__file__))