Add torch predict function

examples/alexnet/alexnet.py

@@ -1,20 +1,38 @@
-import torchvision
-import torch
 import sys
 
-from torchsummary import summary
+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(pretrained=True)
+    alexnet = torchvision.models.alexnet(weights=torchvision.models.AlexNet_Weights.DEFAULT)
     print_model_parameters(alexnet)  # print layer names and number of parameters
     export_model_weights(alexnet, 'alexnet_weights.bin')
-    print()
+    # predict('cat.jpg')
-
-    if torch.cuda.is_available():
-        alexnet.cuda()
-
-    summary(alexnet, (3, 227, 227))