From 09e64f8037a53f042c3ef0abbb20e52a4ef14e5c Mon Sep 17 00:00:00 2001
From: LordMathis <matus@namesny.com>
Date: Thu, 30 May 2024 19:31:28 +0200
Subject: [PATCH] Reformat python files

---
 examples/alexnet/alexnet.py           | 25 -------------------------
 examples/inception_v3/inception_v3.py | 15 +++++++--------
 2 files changed, 7 insertions(+), 33 deletions(-)

diff --git a/examples/alexnet/alexnet.py b/examples/alexnet/alexnet.py
index 63bb580..bcd0a36 100644
--- a/examples/alexnet/alexnet.py
+++ b/examples/alexnet/alexnet.py
@@ -1,35 +1,10 @@
 import sys
 
-import torch
 import torchvision
-from PIL import Image
-from torchvision import transforms
 
 sys.path.append('../../tools')  # Ugly hack 
 from utils import export_model_weights, print_model_parameters
 
-
-def predict(model, image_path):
-    input_image = Image.open(image_path)
-    preprocess = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(227),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-    ])
-    input_tensor = preprocess(input_image)
-    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
-
-    # move the input and model to GPU for speed if available
-    if torch.cuda.is_available():
-        input_batch = input_batch.to('cuda')
-        model.to('cuda')
-
-    with torch.no_grad():
-        output = model(input_batch)
-        print(torch.argmax(output))
-
-
 if __name__ == "__main__":
     alexnet = torchvision.models.alexnet(weights=torchvision.models.AlexNet_Weights.DEFAULT)
     print_model_parameters(alexnet)  # print layer names and number of parameters
diff --git a/examples/inception_v3/inception_v3.py b/examples/inception_v3/inception_v3.py
index f052fd1..a5eadad 100644
--- a/examples/inception_v3/inception_v3.py
+++ b/examples/inception_v3/inception_v3.py
@@ -1,16 +1,15 @@
 import torchvision
 import sys
 
-sys.path.append('../../tools')  # Ugly hack 
-from utils import export_model_weights, print_model_parameters, predict
+sys.path.append("../../tools")  # Ugly hack
+from utils import export_model_weights, print_model_parameters
 
 
 if __name__ == "__main__":
-    inception = torchvision.models.inception_v3(weights=torchvision.models.Inception_V3_Weights.DEFAULT)
+    inception = torchvision.models.inception_v3(
+        weights=torchvision.models.Inception_V3_Weights.DEFAULT
+    )
     inception.eval()
 
-    # print_model_parameters(inception)  # print layer names and number of parameters
-
-    # export_model_weights(inception, 'inception_v3_weights.bin')
-
-    print(predict(inception, "./margot.jpg"))
+    print_model_parameters(inception)  # print layer names and number of parameters
+    export_model_weights(inception, "inception_v3_weights.bin")