faceID Part 2

.github/linters/.python-lint

@@ -7,7 +7,7 @@ ignored-classes = ModelProto
 max-line-length = 99
 [DESIGN]
 max-locals=100
-max-statements=300
+max-statements=350
 min-public-methods=1
 max-branches=120
 max-module-lines=5000

.pylintrc

@@ -7,7 +7,7 @@ ignored-classes = ModelProto
 max-line-length = 99
 [DESIGN]
 max-locals=100
-max-statements=300
+max-statements=350
 min-public-methods=1
 max-branches=120
 max-module-lines=5000

models/model_irse_DRL.py

@@ -30,17 +30,17 @@
 # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html
 #
 ###################################################################################################
-
+"""
+FaceID Teacher Model to be used for Knowledge Distillation
+"""
 from collections import namedtuple
 
 import torch
-import torch.nn as nn
+from torch import nn
 import torch.nn.functional as F
 import torchvision.transforms.functional as FT
 
 
-# Supports: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
-
 class DRL(nn.Module):
     """
     Dimensionality reduction layers
@@ -85,7 +85,7 @@ def __init__(self, resnet, DRL):
     def forward(self, x):
         """Forward prop"""
         if x.shape[1] == 6:
-            if (not self.Teacher_mode):
+            if not self.Teacher_mode:
                 self.Teacher_mode=True
             x = x[:,3: ,:,:]
             x_flip = FT.hflip(x)
@@ -101,7 +101,7 @@ class Flatten(nn.Module):
     """Flattens the input"""
     def forward(self, input):
         """Forward prop"""
-        return input.view(input.size(0), -1)
+        return input.view(input.size(0), -1) # pylint: disable=redefined-builtin
 
 
 def l2_norm(input, axis=1):

parsecmd.py

@@ -68,13 +68,16 @@ def get_parser(model_names, dataset_names):
                         help='when simulating, use "round()" in AvgPool operations '
                              '(default: use "floor()")')
     parser.add_argument('--dr', type=int, default=None,
-                        help='Embedding dimensionality for dimensionality reduction (default: None)')
+                        help='Embedding dimensionality for dimensionality'
+                             'reduction (default: None)')
     parser.add_argument('--scaf-margin', default=28.6,
-                                type=float, help='Margin hyperparameter for Sub-center ArcFace Loss')
+                                type=float, help='Margin hyperparameter'
+                                                 'for Sub-center ArcFace Loss')
     parser.add_argument('--scaf-scale', default=64,
                                 type=int, help='Scale hyperparameter for Sub-center ArcFace Loss')
     parser.add_argument('--backbone-checkpoint', type=str, default=None, metavar='PATH',
-                        help='path to checkpoint from which to load backbone weights (default: None)')
+                        help='path to checkpoint from which to load'
+                             'backbone weights (default: None)')
     parser.add_argument('--copy-output-folder', type=str, default=None, metavar='PATH',
                         help='Path to copy output folder (default: None)')
     parser.add_argument('--kd-relationbased', action='store_true', default=False,
@@ -104,7 +107,9 @@ def get_parser(model_names, dataset_names):
     optimizer_args.add_argument('--lr', '--learning-rate',
                                 type=float, metavar='LR', help='initial learning rate')
     optimizer_args.add_argument('--scaf-lr', default=1e-4,
-                                type=float, metavar='SCAF_LR', help='initial learning rate for Sub-center ArcFace Loss optimizer')
+                                type=float, metavar='SCAF_LR',
+                                  help='initial learning rate for Sub-center'
+                                       'ArcFace Loss optimizer')
     optimizer_args.add_argument('--momentum', default=0.9, type=float,
                                 metavar='M', help='momentum')
     optimizer_args.add_argument('--weight-decay', '--wd', default=1e-4, type=float,

train.py

@@ -396,7 +396,9 @@ def main():
 
     elif args.dr:
 
-        criterion = pml_losses.SubCenterArcFaceLoss(num_classes=args.num_classes, embedding_size=args.dr, margin=args.scaf_margin, scale=args.scaf_scale)
+        criterion = pml_losses.SubCenterArcFaceLoss(num_classes=args.num_classes,
+                                                    embedding_size=args.dr, margin=
+                                                    args.scaf_margin, scale=args.scaf_scale)
         if args.resumed_checkpoint_path:
             checkpoint = torch.load(args.resumed_checkpoint_path,
                                     map_location=lambda storage, loc: storage)
@@ -409,11 +411,9 @@ def main():
 
         distance_fn = CosineSimilarity()
         custom_knn = CustomKNN(distance_fn, batch_size=args.batch_size)
-        accuracy_calculator = AccuracyCalculator(knn_func=custom_knn, include=("precision_at_1",), k=1)
+        accuracy_calculator = AccuracyCalculator(knn_func=custom_knn,
+                                                 include=("precision_at_1",), k=1)
 
-        if args.validation_split != 0:
-            msglogger.info("WARNING: DR works with whole training set, overwriting validation split to 0")
-            args.validation_split = 0
 
     else:
         if not args.regression:
@@ -464,7 +464,8 @@ def main():
         compression_scheduler = distiller.file_config(model, optimizer, args.compress,
                                                       compression_scheduler,
                                                       (start_epoch-1)
-                                                      if args.resumed_checkpoint_path else None, loss_optimizer)
+                                                      if args.resumed_checkpoint_path
+                                                      else None, loss_optimizer)
     elif compression_scheduler is None:
         compression_scheduler = distiller.CompressionScheduler(model)
 
@@ -494,7 +495,8 @@ def main():
         dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt,
                                                 args.kd_teacher_wt)
         if args.kd_relationbased:
-            args.kd_policy = kd_relationbased.RelationBasedKDPolicy(model, teacher, dlw, args.act_mode_8bit)
+            args.kd_policy = kd_relationbased.RelationBasedKDPolicy(model, teacher,
+                                                                    dlw, args.act_mode_8bit)
         else:
             args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher,
                                                                    args.kd_temp, dlw)
@@ -997,16 +999,18 @@ def update_bn_stats(train_loader, model, args):
         _ = model(inputs)
 
 def get_all_embeddings(dataset, model):
+    """Get all embeddings from the test set"""
     tester = testers.BaseTester()
     return tester.get_all_embeddings(dataset, model)
 
 def scaf_test(val_loader, model, accuracy_calculator):
+    """Perform test for SCAF"""
     test_embeddings, test_labels = get_all_embeddings(val_loader.dataset, model)
     test_labels = test_labels.squeeze(1)
     accuracies = accuracy_calculator.get_accuracy(
         test_embeddings, test_embeddings, test_labels, test_labels, True
     )
-    msglogger.info("Test set accuracy (Precision@1) = {}".format(accuracies["precision_at_1"]))
+    msglogger.info(f"Test set accuracy (Precision@1) = {accuracies['precision_at_1']}")
     return accuracies["precision_at_1"], 0 , 0 , 0
 
 def validate(val_loader, model, criterion, loggers, args, epoch=-1, tflogger=None):
@@ -1255,7 +1259,8 @@ def save_tensor(t, f, regression=True):
                         target /= 128.
 
             if args.generate_sample is not None and args.act_mode_8bit and not sample_saved:
-                sample.generate(args.generate_sample, inputs, target, output, args.dataset, False, args.slice_sample)
+                sample.generate(args.generate_sample, inputs, target, output,
+                                args.dataset, False, args.slice_sample)
                 sample_saved = True
 
             if args.csv_prefix is not None: