Test preprocessing

examples/inception_v3/tests/preprocessing.py

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+import sys
+
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import numpy as np
+
+sys.path.append("../../../tools")
+from utils import print_cpp_vector
+
+torch.manual_seed(0)
+
+def generate_random_image(size=(24, 24)):
+    # Generate a random RGB image
+    random_image = np.random.randint(0, 256, size=(*size, 3), dtype=np.uint8)
+    return Image.fromarray(random_image)
+
+def preprocess_image(image, resize=16, crop=16):
+    preprocess = transforms.Compose([
+        transforms.Resize(resize),
+        transforms.CenterCrop(crop),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    return preprocess(image)
+
+def normalize_tensor(tensor):
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    return normalize(tensor)
+
+
+def gen_preprocess_test_result():
+    # Generate a random image
+    random_image = generate_random_image()
+    random_image.save("resources/test_image.jpg")
+
+    # Preprocess the image
+    preprocessed = preprocess_image(random_image)
+
+    # Print the preprocessed data
+    print("Preprocessed image data:")
+    print_cpp_vector(preprocessed.flatten(), "output")
+
+
+def gen_normalize_test_result():
+    input_tensor = torch.rand(3, 8, 8)
+
+    print("Input tensor: ")
+    print_cpp_vector(input_tensor.flatten(), "input")
+
+    normalized = normalize_tensor(input_tensor)
+    print_cpp_vector(normalized.flatten(), "expected_output")
+
+
+if __name__ == "__main__":
+    # print("Preprocess Test\n")
+    # gen_preprocess_test_result()
+
+    print("\nNormalize Test\n")
+    gen_normalize_test_result()