🔧 build(laneline): laneline_code

Tools/expand_output_tensor_dim.py

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+import onnx 
+import numpy as np
+from onnx import numpy_helper
+from printk import print_colored_box, print_colored_box_line
+
+
+def expand_dim(model_path):
+ # 加载原始模型
+ onnx_model = onnx.load(model_path)
+
+ # 获取模型的输出张量
+ output_tensor = None
+ for node in onnx_model.graph.output:
+ if node.name == "output": # 将 "output" 替换模型中实际的输出张量名称
+ output_tensor = node
+ break
+ print(output_tensor)
+ # 修改输出张量的维度
+ if output_tensor is not None:
+ output_tensor.type.tensor_type.shape.dim.insert(0, onnx.TensorShapeProto.Dimension(dim_value=1))
+
+ # 保存修改后的模型
+ onnx.save(onnx_model, "./modified_model.onnx")
+ print_colored_box("The modified ONNX model is saved in ./modified_model.onnx")
+
+
+if __name__ == "__main__":
+ model_path="/Users/bruce/Downloads/Chip_test_models/backbone_224_224/regnety_002.onnx"
+ expand_dim(model_path)

Tools/img_resize_crop_gridsample.py

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+import torch
+import torchvision.transforms as T
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+def rotate_with_grid_sample(image, angle):
+ # 将PIL图像转换为torch张量
+ transform = T.Compose([T.ToTensor()])
+ tensor = transform(image).unsqueeze(0) # 添加batch维度
+
+ # 创建旋转网格
+ theta = torch.tensor([
+ [np.cos(np.radians(angle)), np.sin(-np.radians(angle)), 0],
+ [np.sin(np.radians(angle)), np.cos(np.radians(angle)), 0]
+ ], dtype=torch.float)
+
+ grid = torch.nn.functional.affine_grid(theta.unsqueeze(0), tensor.size(), align_corners=False)
+
+ # 使用grid_sample进行采样
+ rotated_tensor = torch.nn.functional.grid_sample(tensor, grid, align_corners=False)
+
+ # 将结果张量转换回PIL图像
+ rotated_image = T.ToPILImage()(rotated_tensor.squeeze(0))
+ return rotated_image
+
+
+def visualize_image_operations(image_path):
+ # 加载图像
+ img = Image.open(image_path)
+
+ # 裁剪图像（例如，裁剪中心区域）
+ width, height = img.size
+ new_width, new_height = width // 2, height // 2
+ left = (width - new_width) // 2
+ top = (height - new_height) // 2
+ right = (width + new_width) // 2
+ bottom = (height + new_height) // 2
+ cropped_img = img.crop((left, top, right, bottom))
+
+ # 调整大小（例如，将图像缩小一半）
+ resized_img = img.resize((width // 2, height // 2))
+
+ # 网格采样：这里我们使用旋转作为示例
+ angle = 45 # 旋转角度
+ rotated_image = rotate_with_grid_sample(img, angle)
+
+ # 可视化
+ plt.figure(figsize=(10, 8))
+
+ plt.subplot(2, 2, 1)
+ plt.imshow(img)
+ plt.title('Original Image')
+
+ plt.subplot(2, 2, 2)
+ plt.imshow(cropped_img)
+ plt.title('Cropped Image')
+
+ plt.subplot(2, 2, 3)
+ plt.imshow(resized_img)
+ plt.title('Resized Image')
+
+ plt.subplot(2, 2, 4)
+ plt.imshow(rotated_image)
+ plt.title(f'Rotated Image by {angle} Degrees')
+
+ plt.tight_layout()
+ plt.show()
+
+
+# 替换以下路径为你的图片路径
+image_path = "/Users/bruce/PycharmProjects/Pytorch_learning/Deploy/face_torch_3.png"
+visualize_image_operations(image_path)