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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *.axmodel filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+out_test-256.jpg filter=lfs diff=lfs merge=lfs -text

ax650/realesrgan-x4-256.axmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c14f22de6970fa4dc5a6bb70a6286f856070d4a5cdd8eaf444ea174222fc17fb
+size 22633271

ax650/realesrgan-x4.axmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42d96844928ecc7269e2842dcc95935f9e539d8fc0207accd96f0777f87f7acc
+size 19253211

main.py

@@ -0,0 +1,93 @@
+import cv2
+import numpy as np
+import axengine as ort
+import time
+import argparse
+
+def get_model(model_path: str) -> ort.InferenceSession:
+    model = ort.InferenceSession(model_path)
+
+    for input in model.get_inputs():
+        print(input.name, input.shape, input.dtype)
+
+    for output in model.get_outputs():
+        print(output.name, output.shape, output.dtype)
+    width = model.get_inputs()[0].shape[2]
+    height = model.get_inputs()[0].shape[1]
+    
+    return model, width, height
+
+
+def preprocess_image(image, width=64, height=64):
+    # 获取原始图像的高度和宽度
+    h, w = image.shape[:2]
+    
+    # 计算调整大小的比例
+    scale_ratio = min(width / w, height / h)
+    
+    # 根据比例计算新的高度和宽度，同时保持原图宽高比
+    new_w = int(w * scale_ratio)
+    new_h = int(h * scale_ratio)
+    
+    # 调整图像大小，保持原图宽高比
+    resized_img = cv2.resize(image, (new_w, new_h))
+    
+    # 创建一个具有目标尺寸的空白图像（黑色背景）
+    letterboxed_img = np.full((height, width, 3), (0, 0, 0), dtype=np.uint8)
+    
+    # 计算将调整大小后的图像放置在letterbox中的起始点
+    top = (height - new_h) // 2
+    left = (width - new_w) // 2
+    
+    # 将调整大小后的图像放入letterboxed图像中
+    letterboxed_img[top:top + new_h, left:left + new_w] = resized_img
+    
+    # 添加批次维度
+    data = np.expand_dims(letterboxed_img, axis=0)
+    
+    return data
+
+
+'''
+def preprocess_image(image, width=64, height=64):
+    data = cv2.resize(image, (width, height))
+    data = np.expand_dims(data, axis=0)
+    return data
+'''
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Process an image with a given model.")
+    parser.add_argument('--input', type=str, required=True, help='Path to the input image.')
+    parser.add_argument('--output', type=str, required=True, help='Path to save the output image.')
+    parser.add_argument('--model', type=str, required=True, help='Path to the model file (.axmodel).')
+
+    args = parser.parse_args()
+
+    model, width, height = get_model(args.model)
+    
+    img = cv2.imread(args.input)
+    
+    print("Original Image Shape:", img.shape)
+    img = preprocess_image(img, width, height)
+    print("Preprocessed Image Shape:", img.shape)
+    
+    # 开始计时
+    start_time = time.time()
+
+    # 执行推理
+    output = model.run(None, {"input.1": img})[0]
+
+    # 结束计时并计算耗时（毫秒）
+    end_time = time.time()
+    elapsed_ms = (end_time - start_time) * 1000  # 秒转毫秒
+    print(f"Inference Time: {elapsed_ms:.2f} ms")
+
+    print("Output Shape:", output.shape)
+    
+    output[output>1] = 1
+    output[output<0] = 0
+    output_img = (output * 255).astype(np.uint8)[0]
+    
+    print("Final Output Image Shape:", output_img.shape)
+    cv2.imwrite(args.output, output_img)
+ 

onnx/realesrgan-x4-256.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:279da2949cfc4f4f87ca90df784e443e304ed82b8cbc27b40b995c745cbd3d5c
+size 66993533

onnx/realesrgan-x4.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10a7a075719220ee6627124473ce57c74b7ef336b57bed4508d9353eaa8f17ef
+size 66991363