561 lines
37 KiB
Text
561 lines
37 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52324a94",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Core\n",
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"from core.optimizer import run_pso_torch, run_de_torch, run_nm_torch\n",
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"from core.objective import set_global_context\n",
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"from core.cylinder import create_coordinate_grid\n",
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"\n",
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"# Imaging\n",
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"from imaging.preprocessing import process_single_image, process_dataset\n",
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"\n",
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"# Visualization\n",
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"from visualization.res_cyl_to_CT import cyl_and_CT\n",
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"from visualization.res_cyl_nifti import save_cyl\n",
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"\n",
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"# Config\n",
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"from config.device import get_device\n",
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"from config.constant import *\n",
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"\n",
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"import SimpleITK as sitk\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e680dfe",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ====== SEGMENTATION: SINGLE DATA ======\n",
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"\n",
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"image_path = \"/home/cyrou/CBT/CTSpine1K/data/colon/1.3.6.1.4.1.9328.50.4.0783.nii.gz\"\n",
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"label_path = \"/home/cyrou/CBT/CTSpine1K/label/colon/1.3.6.1.4.1.9328.50.4.0783_seg.nii.gz\"\n",
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"output_dir = \"/home/cyrou/CBT/Dataset\" # set by user\n",
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"\n",
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"process_single_image(image_path, label_path, output_dir_base=output_dir)\n",
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"# If you need to assign special labels, please use labels_to_process = [23, 24] .etc "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1961cf4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ====== SEGMENTATION: DATASET ======\n",
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"\n",
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"image_dir = \"/home/cyrou/CBT/CTSpine1K/data/colon/\"\n",
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"label_dir = \"/home/cyrou/CBT/CTSpine1K/label/colon/\"\n",
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"output_dir = \"/home/cyrou/CBT/Dataset\" # set by user\n",
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"\n",
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"process_dataset(image_dir, label_dir, output_dir, labels_to_process=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2bed470b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# ====== CASE 設定 ======\n",
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"cortical_path = \"/home/cyrou/CBT/Seg/Resample/standardized/1.3.6.1.4.1.9328.50.4.0121/L5_cortical.nii.gz\"\n",
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"binary_path = \"/home/cyrou/CBT/Seg/Resample/standardized/1.3.6.1.4.1.9328.50.4.0121/L5_binary.nii.gz\"\n",
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"roi_path = \"/home/cyrou/CBT/Seg/Resample/standardized/1.3.6.1.4.1.9328.50.4.0121/L5_roi2.nii.gz\"\n",
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"\n",
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"# ====== PSO ======\n",
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"swarm_size = 70\n",
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"max_iter = 100\n",
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"\n",
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"# ====== DEVICE ======\n",
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"device = get_device(3) \n",
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"\n",
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"# ====== OTHER ======\n",
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"spacing = [0.5, 0.5, 0.5]\n",
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"CBT = True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc17e584",
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"metadata": {},
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"outputs": [],
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"source": [
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"import SimpleITK as sitk\n",
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"import torch\n",
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"\n",
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"cortical_image = sitk.ReadImage(cortical_path)\n",
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"binary_image = sitk.ReadImage(binary_path)\n",
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"roi_image = sitk.ReadImage(roi_path)\n",
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"\n",
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"cortical_array = sitk.GetArrayFromImage(cortical_image)\n",
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"binary_array = sitk.GetArrayFromImage(binary_image)\n",
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"roi_array = sitk.GetArrayFromImage(roi_image) \n",
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"image_shape = binary_array.shape\n",
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"\n",
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"cortical_tensor = torch.tensor(cortical_array, device=device)\n",
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"binary_tensor = torch.tensor(binary_array, device=device)\n",
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"\n",
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"grid = create_coordinate_grid(image_shape, device)\n",
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"\n",
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"set_global_context(\n",
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" cortical=cortical_tensor,\n",
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" spine=binary_tensor,\n",
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" shape=image_shape,\n",
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" spacing_=spacing,\n",
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" device_=device,\n",
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" grid_=grid,\n",
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" use_tip_penalty=False \n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2c3a3e47",
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"metadata": {},
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"outputs": [],
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"source": [
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"best_l, loss_l, best_r, loss_r, total_time = run_de_torch(\n",
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" label_str=\"L5\",\n",
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" image1_path=cortical_path,\n",
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" image2_path=binary_path,\n",
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" image3_path=roi_path,\n",
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" folder=\"Output\",\n",
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" swarm_size=swarm_size,\n",
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" max_iter=max_iter,\n",
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" spacing=spacing,\n",
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" CBT=CBT,\n",
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" device=device,\n",
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" optimize_size=True,\n",
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" grid=grid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "94e1ee75",
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"metadata": {},
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"outputs": [],
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"source": [
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"best_l, loss_l, best_r, loss_r, total_time = run_nm_torch(\n",
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" label_str=\"L5\",\n",
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" image1_path=cortical_path,\n",
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" image2_path=binary_path,\n",
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" image3_path=roi_path,\n",
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" folder=\"Output\",\n",
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" swarm_size=swarm_size,\n",
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" max_iter=max_iter,\n",
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" spacing=spacing,\n",
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" CBT=CBT,\n",
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" device=device,\n",
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" optimize_size=True,\n",
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" grid=grid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be72d637",
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"metadata": {},
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"outputs": [],
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"source": [
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"best_l, loss_l, best_r, loss_r, total_time = run_pso_torch(\n",
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" label_str=\"L5\",\n",
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" image1_path=cortical_path,\n",
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" image2_path=binary_path,\n",
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" image3_path=roi_path,\n",
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" folder=\"Output\",\n",
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" swarm_size=swarm_size,\n",
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" max_iter=max_iter,\n",
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" spacing=spacing,\n",
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" CBT=CBT,\n",
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" device=device,\n",
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" optimize_size=True,\n",
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" grid=grid)\n",
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"\n",
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"cylinder_L, cylinder_R = save_cyl(best_l, best_r, spacing, roi_path, output_base='Output', CBT=True)\n",
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"\n",
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"cyl_and_CT(diameter_l=best_l[5],\n",
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" diameter_r=best_r[5],\n",
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" length_l=best_l[6],\n",
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" length_r=best_r[6],\n",
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" roi_image_path=roi_path,\n",
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" cylinder_1=cylinder_L,\n",
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" cylinder_2=cylinder_R,\n",
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" base_folder=\"Output\",\n",
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" CBT=CBT)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9723e65b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using GPU 1: NVIDIA A40\n",
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"📂 總共偵測到 784 個病人資料夾，準備開始處理...\n",
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"\n",
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"=========================================\n",
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"🚀 === Running 1.3.6.1.4.1.9328.50.4.0001_L1 ===\n",
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"=========================================\n",
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"👉 執行 CBT 軌跡最佳化...\n",
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"Superior 終板傾斜角度: 6.64°\n",
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"斜率: 0.1164, R²: 0.7599\n",
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"=== 最佳化模式：最佳化位置、角度、直徑和長度 ===\n",
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"\n",
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"=== 左側 ===\n",
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"Stopping search: maximum iterations reached --> 100\n",
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"[LEFT] overlap: 15.3%\n",
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"[LEFT] Position: [ 27.47724944 40.25868267 37.64458752 100.44943459 58.1295552 ]\n",
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"[LEFT] Diameter: 5.0 mm (raw: 4.98)\n",
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"[LEFT] Length: 50 mm (raw: 49.97)\n",
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"Stopping search: maximum iterations reached --> 100\n",
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"[LEFT][retry 1] ⚠️ not ok | loss improved=-15892000.0000, overlap=8.4%\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[2], line 62\u001b[0m\n\u001b[1;32m 59\u001b[0m image_shape \u001b[38;5;241m=\u001b[39m binary_array\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 61\u001b[0m grid \u001b[38;5;241m=\u001b[39m create_coordinate_grid(image_shape, device)\n\u001b[0;32m---> 62\u001b[0m best_pos_l, best_loss_l, best_pos_r, best_loss_r, total_time \u001b[38;5;241m=\u001b[39m \u001b[43mrun_pso_torch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage1_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mIMAGE1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage2_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mIMAGE2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage3_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mIMAGE3\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mswarm_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m70\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mspacing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mCBT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimize_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgrid\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m cylinder_L, cylinder_R \u001b[38;5;241m=\u001b[39m save_cyl(best_pos_l, best_pos_r, spacing, IMAGE3, output_base\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOutput\u001b[39m\u001b[38;5;124m'\u001b[39m, CBT\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 78\u001b[0m cyl_and_CT(best_pos_l[\u001b[38;5;241m5\u001b[39m], best_pos_r[\u001b[38;5;241m5\u001b[39m], best_pos_l[\u001b[38;5;241m6\u001b[39m], best_pos_r[\u001b[38;5;241m6\u001b[39m], IMAGE3, cylinder_L, cylinder_R, base_folder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOutput\u001b[39m\u001b[38;5;124m'\u001b[39m, CBT\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
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"File \u001b[0;32m/mnt/pool0/home/cyrou/CBT/CBT_project/core/optimizer.py:194\u001b[0m, in \u001b[0;36mrun_pso_torch\u001b[0;34m(label_str, image1_path, image2_path, image3_path, folder, swarm_size, max_iter, spacing, CBT, device, optimize_size, grid)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;66;03m# 左側 retry：loss 要 <=0 且 overlap >= 0.5 才算過關\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m (best_loss_l \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m best_overlap_l \u001b[38;5;241m<\u001b[39m OVERLAP_THRESH) \u001b[38;5;129;01mand\u001b[39;00m retries \u001b[38;5;241m<\u001b[39m max_retries:\n\u001b[0;32m--> 194\u001b[0m position_l, loss_l \u001b[38;5;241m=\u001b[39m \u001b[43mpso\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlb_l\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mub_l\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswarmsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswarm_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_iter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 195\u001b[0m overlap_l, diameter_l, length_l \u001b[38;5;241m=\u001b[39m eval_overlap_from_position(\n\u001b[1;32m 196\u001b[0m position_l, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m\"\u001b[39m, optimize_size, spine_tensor, image_shape, spacing\n\u001b[1;32m 197\u001b[0m )\n\u001b[1;32m 199\u001b[0m \u001b[38;5;66;03m# 只要找到更好的 loss（或你想用 loss+overlap 綜合排序也行）就更新 best\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;66;03m# 安全版本：優先選「合格解」；沒有合格解時才用 loss 最小的當備案\u001b[39;00m\n",
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"File \u001b[0;32m~/.conda/envs/envpy/lib/python3.13/site-packages/pyswarm/pso.py:145\u001b[0m, in \u001b[0;36mpso\u001b[0;34m(func, lb, ub, ieqcons, f_ieqcons, args, kwargs, swarmsize, omega, phip, phig, maxiter, minstep, minfunc, debug)\u001b[0m\n\u001b[1;32m 143\u001b[0m x[i, mark1] \u001b[38;5;241m=\u001b[39m lb[mark1]\n\u001b[1;32m 144\u001b[0m x[i, mark2] \u001b[38;5;241m=\u001b[39m ub[mark2]\n\u001b[0;32m--> 145\u001b[0m fx \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;66;03m# Compare particle's best position (if constraints are satisfied)\u001b[39;00m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fx\u001b[38;5;241m<\u001b[39mfp[i] \u001b[38;5;129;01mand\u001b[39;00m is_feasible(x[i, :]):\n",
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"File \u001b[0;32m~/.conda/envs/envpy/lib/python3.13/site-packages/pyswarm/pso.py:74\u001b[0m, in \u001b[0;36mpso.<locals>.<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 71\u001b[0m vlow \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mvhigh\n\u001b[1;32m 73\u001b[0m \u001b[38;5;66;03m# Check for constraint function(s) #########################################\u001b[39;00m\n\u001b[0;32m---> 74\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m f_ieqcons \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ieqcons):\n",
|
||
"File \u001b[0;32m/mnt/pool0/home/cyrou/CBT/CBT_project/core/objective.py:126\u001b[0m, in \u001b[0;36mobjective_function\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# 將連續值轉換為離散值\u001b[39;00m\n\u001b[1;32m 124\u001b[0m diameter_discrete, length_discrete \u001b[38;5;241m=\u001b[39m snap_to_discrete_values(diameter_raw, length_raw)\n\u001b[0;32m--> 126\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mcylinder_circle_line_intersection_loss_deductions_torch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiameter_discrete\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mlength_discrete\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage2_shape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mcortical_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43mspine_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[43m \u001b[49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
|
||
"File \u001b[0;32m/mnt/pool0/home/cyrou/CBT/CBT_project/core/objective.py:54\u001b[0m, in \u001b[0;36mcylinder_circle_line_intersection_loss_deductions_torch\u001b[0;34m(diameter, length, params, image_shape, cortical_tensor, spine_tensor, spacing, device)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03mComputes the loss for a given set of cylinder params in PyTorch, \u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124;03mreturning a Python float for PSO consumption.\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 52\u001b[0m position_z, position_y, position_x, azimuth, altitude \u001b[38;5;241m=\u001b[39m params\n\u001b[0;32m---> 54\u001b[0m cyl_fwd \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_cylinder_n_torch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiameter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mlength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_z\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mazimuth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43maltitude\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage_shape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m cyl_opp \u001b[38;5;241m=\u001b[39m generate_cylinder_o_torch(\n\u001b[1;32m 69\u001b[0m diameter,\n\u001b[1;32m 70\u001b[0m length,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 79\u001b[0m grid\n\u001b[1;32m 80\u001b[0m )\n\u001b[1;32m 83\u001b[0m \u001b[38;5;66;03m# We call the center_line_intersections in Torch mode\u001b[39;00m\n",
|
||
"File \u001b[0;32m/mnt/pool0/home/cyrou/CBT/CBT_project/core/cylinder.py:83\u001b[0m, in \u001b[0;36mgenerate_cylinder_n_torch\u001b[0;34m(diameter, length, position_z, position_y, position_x, azimuth, altitude, shape, spacing, device, grid)\u001b[0m\n\u001b[1;32m 75\u001b[0m x_rot \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 76\u001b[0m x_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(azimuth_rad_t) \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(altitude_rad_t)\n\u001b[1;32m 77\u001b[0m \u001b[38;5;241m+\u001b[39m y_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39msin(azimuth_rad_t) \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(altitude_rad_t)\n\u001b[1;32m 78\u001b[0m \u001b[38;5;241m-\u001b[39m z_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39msin(altitude_rad_t)\n\u001b[1;32m 79\u001b[0m )\n\u001b[1;32m 80\u001b[0m y_rot \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mx_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39msin(azimuth_rad_t) \u001b[38;5;241m+\u001b[39m y_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(azimuth_rad_t)\n\u001b[1;32m 81\u001b[0m z_rot \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 82\u001b[0m x_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(azimuth_rad_t) \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39msin(altitude_rad_t)\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;241m+\u001b[39m y_t \u001b[38;5;241m*\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mazimuth_rad_t\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39msin(altitude_rad_t)\n\u001b[1;32m 84\u001b[0m \u001b[38;5;241m+\u001b[39m z_t \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mcos(altitude_rad_t)\n\u001b[1;32m 85\u001b[0m )\n\u001b[1;32m 87\u001b[0m \u001b[38;5;66;03m# Handle spacing\u001b[39;00m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m# You can expand or generalize for more spacing options\u001b[39;00m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m spacing \u001b[38;5;241m==\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m]:\n",
|
||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import traceback\n",
|
||
"from datetime import datetime\n",
|
||
"from core.optimizer import run_pso_torch\n",
|
||
"from visualization.res_cyl_nifti import save_cyl\n",
|
||
"from visualization.res_cyl_to_CT import cyl_and_CT\n",
|
||
"\n",
|
||
"device = get_device(1)\n",
|
||
"\n",
|
||
"# 只需要傳入 base_dir, patient_id, level 即可自動組裝路徑\n",
|
||
"def build_paths(base_dir: str, patient_id: str, level: str):\n",
|
||
" case_dir = os.path.join(base_dir, patient_id)\n",
|
||
" img1 = os.path.join(case_dir, f\"{level}_cortical.nii.gz\")\n",
|
||
" img2 = os.path.join(case_dir, f\"{level}_binary2.nii.gz\")\n",
|
||
" img3 = os.path.join(case_dir, f\"{level}_roi2.nii.gz\")\n",
|
||
" return img1, img2, img3\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" base_dir = \"/home/cyrou/CBT/Seg/Resample/standardized/\" # 你的 case 資料夾根目錄\n",
|
||
" spacing = [0.5, 0.5, 0.5]\n",
|
||
" levels = [\"L1\", \"L2\", \"L3\", \"L4\", \"L5\"] # 鎖定要跑的脊椎節段\n",
|
||
" date = datetime.now().strftime(\"%Y%m%d\")\n",
|
||
" \n",
|
||
" failed = []\n",
|
||
" skipped = []\n",
|
||
" succeeded = []\n",
|
||
"\n",
|
||
" # 1. 動態獲取 base_dir 下的所有項目，並篩選出「資料夾」\n",
|
||
" # 使用 sorted 讓它照字母/數字順序跑，強迫症看了比較舒服\n",
|
||
" all_patients = sorted([d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d))])\n",
|
||
" \n",
|
||
" print(f\"📂 總共偵測到 {len(all_patients)} 個病人資料夾，準備開始處理...\")\n",
|
||
"\n",
|
||
" # 2. 外層迴圈：遍歷每一個病人資料夾\n",
|
||
" for patient_id in all_patients:\n",
|
||
" \n",
|
||
" # 3. 內層迴圈：遍歷 L1 到 L5\n",
|
||
" for level in levels:\n",
|
||
" label_str = f\"{patient_id}_{level}\"\n",
|
||
" IMAGE1, IMAGE2, IMAGE3 = build_paths(base_dir, patient_id, level)\n",
|
||
"\n",
|
||
" # 檢查這個 level 的檔案是否齊全\n",
|
||
" missing = [p for p in [IMAGE1, IMAGE2, IMAGE3] if not os.path.exists(p)]\n",
|
||
" if missing:\n",
|
||
" # 缺檔直接跳過，不印出太多落落長的警告，保持終端機乾淨\n",
|
||
" print(f\"[SKIP] ⏭️ {label_str} (缺少 {len(missing)} 個檔案)\")\n",
|
||
" skipped.append((label_str, missing))\n",
|
||
" continue\n",
|
||
"\n",
|
||
" print(f\"\\n=========================================\")\n",
|
||
" print(f\"🚀 === Running {label_str} ===\")\n",
|
||
" print(f\"=========================================\")\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # --- 第一階段：CBT = True ---\n",
|
||
" print(f\"👉 執行 CBT 軌跡最佳化...\")\n",
|
||
" binary_image = sitk.ReadImage(IMAGE2)\n",
|
||
" binary_array = sitk.GetArrayFromImage(binary_image)\n",
|
||
" image_shape = binary_array.shape\n",
|
||
"\n",
|
||
" grid = create_coordinate_grid(image_shape, device)\n",
|
||
" best_pos_l, best_loss_l, best_pos_r, best_loss_r, total_time = run_pso_torch(\n",
|
||
" label_str,\n",
|
||
" image1_path=IMAGE1,\n",
|
||
" image2_path=IMAGE2,\n",
|
||
" image3_path=IMAGE3,\n",
|
||
" folder=date,\n",
|
||
" swarm_size=70,\n",
|
||
" max_iter=100,\n",
|
||
" spacing=spacing,\n",
|
||
" CBT=True,\n",
|
||
" device=device,\n",
|
||
" optimize_size=True,\n",
|
||
" grid=grid\n",
|
||
" )\n",
|
||
" \n",
|
||
" cylinder_L, cylinder_R = save_cyl(best_pos_l, best_pos_r, spacing, IMAGE3, output_base='Output', CBT=True)\n",
|
||
" cyl_and_CT(best_pos_l[5], best_pos_r[5], best_pos_l[6], best_pos_r[6], IMAGE3, cylinder_L, cylinder_R, base_folder='Output', CBT=True)\n",
|
||
" \n",
|
||
" # --- 第二階段：CBT = False (傳統軌跡 TT) ---\n",
|
||
" print(f\"👉 執行傳統軌跡 (TT) 最佳化...\")\n",
|
||
" best_pos_l_tt, best_loss_l_tt, best_pos_r_tt, best_loss_r_tt, total_time_tt = run_pso_torch(\n",
|
||
" label_str,\n",
|
||
" image1_path=IMAGE2, # 注意：你原本的扣這裡是 IMAGE2，如果有改過記得確認\n",
|
||
" image2_path=IMAGE2,\n",
|
||
" image3_path=IMAGE3,\n",
|
||
" folder=date,\n",
|
||
" swarm_size=70,\n",
|
||
" max_iter=100,\n",
|
||
" spacing=spacing,\n",
|
||
" CBT=False,\n",
|
||
" device=device,\n",
|
||
" optimize_size=True,\n",
|
||
" grid=grid\n",
|
||
" )\n",
|
||
" \n",
|
||
" cylinder_L_tt, cylinder_R_tt = save_cyl(best_pos_l_tt, best_pos_r_tt, spacing, IMAGE3, output_base='Output', CBT=False)\n",
|
||
" cyl_and_CT(best_pos_l_tt[5], best_pos_r_tt[5], best_pos_l_tt[6], best_pos_r_tt[6], IMAGE3, cylinder_L_tt, cylinder_R_tt, base_folder='Output', CBT=False)\n",
|
||
"\n",
|
||
" # 記錄成功 (把兩次的耗時加起來)\n",
|
||
" succeeded.append((label_str, best_loss_l, best_loss_r, total_time + total_time_tt))\n",
|
||
" print(f\"[OK] ✅ {label_str} 完成！總耗時={(total_time + total_time_tt):.2f}s\")\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"[FAIL] ❌ {label_str} 發生錯誤: {e}\")\n",
|
||
" traceback.print_exc() # 保留剛學到的神技，印出詳細報錯\n",
|
||
" failed.append((label_str, str(e)))\n",
|
||
"\n",
|
||
" # 4. 最終報告\n",
|
||
" print(\"\\n\" + \"=\"*40)\n",
|
||
" print(\"🏆 ==== 全自動批次處理總結 ====\")\n",
|
||
" print(\"=\"*40)\n",
|
||
" print(f\"✅ Succeeded (成功): {len(succeeded)} 節段\")\n",
|
||
" print(f\"⏭️ Skipped (缺檔跳過): {len(skipped)} 節段\")\n",
|
||
" print(f\"❌ Failed (執行錯誤): {len(failed)} 節段\")\n",
|
||
" \n",
|
||
" if failed:\n",
|
||
" print(\"\\n⚠️ 失敗清單:\")\n",
|
||
" for fail_label, err_msg in failed:\n",
|
||
" print(f\" - {fail_label}: {err_msg}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "bcf55fef",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"usage: ipykernel_launcher.py [-h] --gpu GPU [--total_gpus TOTAL_GPUS]\n",
|
||
"ipykernel_launcher.py: error: the following arguments are required: --gpu\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "SystemExit",
|
||
"evalue": "2",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"An exception has occurred, use %tb to see the full traceback.\n",
|
||
"\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/cyrou/.conda/envs/envpy/lib/python3.13/site-packages/IPython/core/interactiveshell.py:3585: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
|
||
" warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import traceback\n",
|
||
"import argparse\n",
|
||
"import torch\n",
|
||
"from datetime import datetime\n",
|
||
"\n",
|
||
"# 請確保你有 import run_pso_torch, save_cyl, cyl_and_CT 等自訂函數\n",
|
||
"\n",
|
||
"def build_paths(base_dir: str, patient_id: str, level: str):\n",
|
||
" case_dir = os.path.join(base_dir, patient_id)\n",
|
||
" img1 = os.path.join(case_dir, f\"{level}_cortical.nii.gz\")\n",
|
||
" img2 = os.path.join(case_dir, f\"{level}_binary2.nii.gz\")\n",
|
||
" img3 = os.path.join(case_dir, f\"{level}_roi2.nii.gz\")\n",
|
||
" return img1, img2, img3\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" # === 1. 設定命令列引數 (GPU 拆分設定) ===\n",
|
||
" parser = argparse.ArgumentParser(description=\"Multi-GPU CBT Batch Processing\")\n",
|
||
" parser.add_argument('--gpu', type=int, required=True, help='指定這支程式要用的 GPU ID (0, 1, 2, 3)')\n",
|
||
" parser.add_argument('--total_gpus', type=int, default=4, help='總共開啟的 GPU 數量')\n",
|
||
" args = parser.parse_args()\n",
|
||
"\n",
|
||
" # === 2. 綁定 GPU ===\n",
|
||
" if torch.cuda.is_available():\n",
|
||
" device = torch.device(f'cuda:{args.gpu}')\n",
|
||
" torch.cuda.set_device(device)\n",
|
||
" else:\n",
|
||
" device = torch.device('cpu')\n",
|
||
" print(\"⚠️ 找不到 CUDA，將使用 CPU\")\n",
|
||
"\n",
|
||
" print(f\"\\n🚀 啟動批次任務 | 分配至 GPU: [{args.gpu}] | 總共 {args.total_gpus} 個節點協同運算\")\n",
|
||
"\n",
|
||
" # === 3. 基本參數 ===\n",
|
||
" base_dir = \"Seg/Resample/standardized\" \n",
|
||
" spacing = [0.5, 0.5, 0.5]\n",
|
||
" levels = [\"L1\", \"L2\", \"L3\", \"L4\", \"L5\"] \n",
|
||
" date = datetime.now().strftime(\"%Y%m%d\")\n",
|
||
" \n",
|
||
" failed = []\n",
|
||
" skipped = []\n",
|
||
" succeeded = []\n",
|
||
"\n",
|
||
" # 動態獲取所有病人資料夾\n",
|
||
" all_patients = sorted([d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d))])\n",
|
||
" \n",
|
||
" # === 4. 餘數分工：只挑選屬於這張 GPU 的病人 ===\n",
|
||
" my_patients = [p for i, p in enumerate(all_patients) if i % args.total_gpus == args.gpu]\n",
|
||
" \n",
|
||
" print(f\"📂 總資料庫有 {len(all_patients)} 個病人。\")\n",
|
||
" print(f\"🎯 本 GPU (ID: {args.gpu}) 被分配到 {len(my_patients)} 個病人，準備開始處理...\")\n",
|
||
"\n",
|
||
" for patient_id in my_patients:\n",
|
||
" for level in levels:\n",
|
||
" label_str = f\"{patient_id}_{level}\"\n",
|
||
" IMAGE1, IMAGE2, IMAGE3 = build_paths(base_dir, patient_id, level)\n",
|
||
"\n",
|
||
" missing = [p for p in [IMAGE1, IMAGE2, IMAGE3] if not os.path.exists(p)]\n",
|
||
" if missing:\n",
|
||
" print(f\"[SKIP] ⏭️ {label_str} (缺檔)\")\n",
|
||
" skipped.append((label_str, missing))\n",
|
||
" continue\n",
|
||
"\n",
|
||
" print(f\"\\n=========================================\")\n",
|
||
" print(f\"🔥 [GPU {args.gpu}] === Running {label_str} ===\")\n",
|
||
" print(f\"=========================================\")\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # --- CBT = True ---\n",
|
||
" print(f\"👉 [GPU {args.gpu}] 執行 CBT 軌跡最佳化...\")\n",
|
||
" binary_image = sitk.ReadImage(IMAGE2)\n",
|
||
" binary_array = sitk.GetArrayFromImage(binary_image)\n",
|
||
" image_shape = binary_array.shape\n",
|
||
"\n",
|
||
" grid = create_coordinate_grid(image_shape, device)\n",
|
||
" best_pos_l, best_loss_l, best_pos_r, best_loss_r, total_time = run_pso_torch(\n",
|
||
" label_str,\n",
|
||
" image1_path=IMAGE1,\n",
|
||
" image2_path=IMAGE2,\n",
|
||
" image3_path=IMAGE3,\n",
|
||
" folder=date, \n",
|
||
" swarm_size=70,\n",
|
||
" max_iter=100,\n",
|
||
" spacing=spacing,\n",
|
||
" CBT=True,\n",
|
||
" device=device,\n",
|
||
" optimize_size=True,\n",
|
||
" grid=grid # 記得把 device 傳進去\n",
|
||
" )\n",
|
||
" \n",
|
||
" cylinder_L, cylinder_R = save_cyl(best_pos_l, best_pos_r, spacing, IMAGE3, output_base='Output', CBT=True)\n",
|
||
" cyl_and_CT(best_pos_l[5], best_pos_r[5], best_pos_l[6], best_pos_r[6], IMAGE3, cylinder_L, cylinder_R, base_folder='Output', CBT=True)\n",
|
||
" \n",
|
||
" # --- CBT = False ---\n",
|
||
" print(f\"👉 [GPU {args.gpu}] 執行傳統軌跡 (TT) 最佳化...\")\n",
|
||
" best_pos_l_tt, best_loss_l_tt, best_pos_r_tt, best_loss_r_tt, total_time_tt = run_pso_torch(\n",
|
||
" label_str,\n",
|
||
" image1_path=IMAGE2, \n",
|
||
" image2_path=IMAGE2,\n",
|
||
" image3_path=IMAGE3,\n",
|
||
" folder=date, \n",
|
||
" swarm_size=70,\n",
|
||
" max_iter=100,\n",
|
||
" spacing=spacing,\n",
|
||
" CBT=False,\n",
|
||
" device=device,\n",
|
||
" optimize_size=True,\n",
|
||
" grid=grid # 記得把 device 傳進去\n",
|
||
" )\n",
|
||
" \n",
|
||
" cylinder_L_tt, cylinder_R_tt = save_cyl(best_pos_l_tt, best_pos_r_tt, spacing, IMAGE3, output_base='Output', CBT=False)\n",
|
||
" cyl_and_CT(best_pos_l_tt[5], best_pos_r_tt[5], best_pos_l_tt[6], best_pos_r_tt[6], IMAGE3, cylinder_L_tt, cylinder_R_tt, base_folder='Output', CBT=False)\n",
|
||
"\n",
|
||
" succeeded.append((label_str, best_loss_l, best_loss_r, total_time + total_time_tt))\n",
|
||
" print(f\"[OK] ✅ [GPU {args.gpu}] {label_str} 完成！總耗時={(total_time + total_time_tt):.2f}s\")\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"[FAIL] ❌ [GPU {args.gpu}] {label_str} 發生錯誤: {e}\")\n",
|
||
" traceback.print_exc()\n",
|
||
" failed.append((label_str, str(e)))\n",
|
||
"\n",
|
||
" # --- GPU 專屬的 Summary ---\n",
|
||
" print(\"\\n\" + \"=\"*40)\n",
|
||
" print(f\"🏆 ==== GPU {args.gpu} 處理總結 ====\")\n",
|
||
" print(\"=\"*40)\n",
|
||
" print(f\"✅ Succeeded (成功): {len(succeeded)} 節段\")\n",
|
||
" print(f\"⏭️ Skipped (缺檔跳過): {len(skipped)} 節段\")\n",
|
||
" print(f\"❌ Failed (執行錯誤): {len(failed)} 節段\")\n",
|
||
" \n",
|
||
" if failed:\n",
|
||
" print(f\"\\n⚠️ GPU {args.gpu} 失敗清單:\")\n",
|
||
" for fail_label, err_msg in failed:\n",
|
||
" print(f\" - {fail_label}: {err_msg}\")"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "envpy",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|