Fix notebook model loading for updated qat

analogdevicesinc · Dec 17, 2024 · 51030d1 · 51030d1
1 parent 802d98e
commit 51030d1
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Showing 9 changed files with 8,466 additions and 183 deletions.
diff --git a/.python-version b/.python-version
@@ -1 +1 @@
-3.11.9
+3.11.8
diff --git a/notebooks/AutoEncoder_Evaluation.ipynb b/notebooks/AutoEncoder_Evaluation.ipynb
diff --git a/notebooks/Bayer2RGB_Evaluation.ipynb b/notebooks/Bayer2RGB_Evaluation.ipynb
diff --git a/notebooks/DatasetVisualization_pascalvoc.ipynb b/notebooks/DatasetVisualization_pascalvoc.ipynb
diff --git a/notebooks/FPNDetectorModelVisualization_pascalvoc_quantized.ipynb b/notebooks/FPNDetectorModelVisualization_pascalvoc_quantized.ipynb
diff --git a/notebooks/TinySSDModelVisualization_svhn.ipynb b/notebooks/TinySSDModelVisualization_svhn.ipynb
diff --git a/requirements.txt b/requirements.txt
@@ -17,6 +17,7 @@ pyffmpeg==2.4.2.18.1
 pytorch-metric-learning==2.6.0
 pytube==15.0.0
 qrcode==7.4.2
+seaborn==0.13.2
 scipy==1.14.0
 soundfile==0.12.1
 tensorboard==2.17.0

diff --git a/train.py b/train.py
@@ -1565,8 +1565,11 @@ def update_old_model_params(model_path, model_new):
         old_model_path = os.path.join(dir_path, new_file_name)
         os.rename(model_path, old_model_path)
         torch.save(model_old, model_path)
-        msglogger.info('Model `%s` is old. Missing parameters added with default values!',
-                       model_path)
+        if msglogger:
+            msglogger.info('Model `%s` is old. Missing parameters added with default values!',
+                        model_path)
+        else:
+            print(f'Model {model_path} is old. Missing parameters added with default values!')
 
 
 if __name__ == '__main__':

diff --git a/utils/autoencoder_eval_utils.py b/utils/autoencoder_eval_utils.py
@@ -10,6 +10,10 @@
 from torch import nn
 
 import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_style("white")
+
 
 DECAY_FACTOR = 1
 
@@ -186,19 +190,19 @@ def calc_ae_perf_metrics(reconstructions, inputs, labels, threshold, print_all=T
     """
 
     loss_fn = nn.MSELoss(reduce=False)
-    FP = 0
-    FN = 0
-    TP = 0
-    TN = 0
-
-    Recall = -1
-    Precision = -1
-    Accuracy = -1
-    F1 = -1
-    FPRate = -1
-
-    BalancedAccuracy = -1
-    TNRate = -1   # specificity (SPC), selectivity
+    FP = torch.tensor(0).to(inputs[0].device)
+    FN = torch.tensor(0).to(inputs[0].device)
+    TP = torch.tensor(0).to(inputs[0].device)
+    TN = torch.tensor(0).to(inputs[0].device)
+
+    Recall = torch.tensor(-1).to(inputs[0].device)
+    Precision = torch.tensor(-1).to(inputs[0].device)
+    Accuracy = torch.tensor(-1).to(inputs[0].device) 
+    F1 = torch.tensor(-1).to(inputs[0].device) 
+    FPRate = torch.tensor(-1).to(inputs[0].device) 
+
+    BalancedAccuracy = torch.tensor(-1).to(inputs[0].device) 
+    TNRate = torch.tensor(-1).to(inputs[0].device)    # specificity (SPC), selectivity
 
     for i, inputs_batch in enumerate(inputs):
         label_batch = labels[i]