duxingchen/WQ_GUI
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix(step8): 修复外部模型字典透传断链 + 规范化 loaded_model_data 防 Ridge subscriptable 崩溃

Browse Source
This commit is contained in:
DXC
2026-06-08 11:36:36 +08:00
parent 2b76d7908f
commit e3debbcb15
5 changed files with 189 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files

95
src/core/prediction/inference_batch.py
View File

@ -41,10 +41,17 @@ class WaterQualityInference:
print(f"警告: 模型目录不存在: {artifacts_dir}，将在需要时创建")
self.best_model_info = None
self.loaded_model_data = None
self.external_model = external_model
self.external_model_path = external_model_path
# 规范化 loaded_model_data：始终为 dict，确保 ['model'] 访问不崩溃
if external_model is not None:
# 外部传入的是裸模型对象 → 包装为 dict，统一后续 .get('model') 访问
self.loaded_model_data = {'model': external_model, 'preprocess_method': 'None'}
print(f" 外部模型已规范化: type={type(external_model).__name__}")
else:
self.loaded_model_data = None
def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
"""
加载sampling生成的CSV数据（兼容 WQI 增强版 CSV）
@ -751,8 +758,7 @@ class WaterQualityInference:
print("\n步骤1: 加载模型")
print("-" * 40)
if self.external_model is not None:
# 外部预训练模型已注入，直接使用，跳过磁盘加载
self.loaded_model_data = self.external_model
# 已在 __init__ 中规范化，无需重复赋值
print(f" 使用外部预训练模型: type={type(self.external_model).__name__}")
elif model_file_path:
self.load_specific_model(model_file_path)
@ -802,8 +808,8 @@ class WaterQualityInference:
info = {
"status": "model_loaded",
"preprocess_method": self.loaded_model_data['preprocess_method'],
"model_name": self.loaded_model_data['model_name'],
"preprocess_method": self.loaded_model_data.get('preprocess_method', 'Unknown'),
"model_name": self.loaded_model_data.get('model_name', type(self.external_model).__name__ if self.external_model else 'Unknown'),
"model_type": str(type(self.loaded_model_data['model'])),
"metadata": self.loaded_model_data.get('metadata', {})
}
@ -877,7 +883,8 @@ class WaterQualityInference:
prediction_column: str = 'prediction',
output_format: str = 'csv',
external_model=None,
external_model_path=None):
external_model_path=None,
external_models_dict=None):
"""
使用多个子文件夹中的模型进行批量推理
@ -893,45 +900,61 @@ class WaterQualityInference:
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
# 优先级：外部预训练模型 > 从磁盘加载
if external_model is not None:
effective_model = external_model
model_desc = (
f"外部导入模型 ({external_model_path or 'unknown'}), "
f"type={type(external_model).__name__}"
)
print(f"\n使用外部预训练模型: {model_desc}")
else:
effective_model = None
# 查找所有子文件夹
subdirs = [d for d in models_root.iterdir() if d.is_dir()]
if not subdirs:
print(f"在目录 {models_root_dir} 中未找到子文件夹")
return
print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
print(f"输出格式: {output_format.upper()}")
all_results = {}
for subdir in subdirs:
# 优先级 1：_external_models_dict 非空 → 直接用字典的 keys 作为 targets，不扫描磁盘
print(f"[BatchInference] 终于收到字典啦！包含模型: {list(external_models_dict.keys()) if external_models_dict else 'None'}")
if external_models_dict is not None and len(external_models_dict) > 0:
targets = list(external_models_dict.keys())
print(f"\n使用外部导入模型字典（{len(targets)} 个模型）")
print(f"检测到外部导入模型，将预测以下参数: {targets}")
elif external_model is not None:
print(f"\n使用外部预训练模型: {external_model_path or 'unknown'}")
subdirs = [d for d in models_root.iterdir() if d.is_dir()]
if not subdirs:
print(f"在目录 {models_root_dir} 中未找到子文件夹")
return {}
print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
targets = [d.name for d in subdirs]
else:
subdirs = [d for d in models_root.iterdir() if d.is_dir()]
if not subdirs:
print(f"在目录 {models_root_dir} 中未找到子文件夹")
return {}
print(f"找到 {len(subdirs)} 个模型子文件夹进行批量推理")
targets = [d.name for d in subdirs]
print(f"输出格式: {output_format.upper()}")
for subdir_name in targets:
try:
subdir_name = subdir.name
print(f"\n{'='*60}")
print(f"处理模型文件夹: {subdir_name}")
print(f"处理模型: {subdir_name}")
print(f"{'='*60}")
# 创建推理实例：外部模型优先注入，跳过磁盘查找
# 优先级：字典中该 target 的模型 > 共享单模型 > 磁盘加载
effective_model = None
if external_models_dict and subdir_name in external_models_dict:
effective_model = external_models_dict[subdir_name]
print(f" → 使用字典中模型: {type(effective_model).__name__}")
elif external_model is not None:
effective_model = external_model
print(f" → 使用共享外部模型: {type(effective_model).__name__}")
# artifacts_dir：字典模式优先用 placeholder "./"，否则用真实子目录
artifacts_dir = (
str(models_root / subdir_name)
if (models_root / subdir_name).is_dir()
else str(models_root)
)
if effective_model is not None:
model_inferencer = WaterQualityInference(
str(subdir),
artifacts_dir,
external_model=effective_model,
external_model_path=external_model_path,
external_model_path=external_model_path or "",
)
else:
model_inferencer = WaterQualityInference(str(subdir))
model_inferencer = WaterQualityInference(artifacts_dir)
# 根据输出格式设置文件扩展名
file_ext = f".{output_format}"
@ -960,10 +983,10 @@ class WaterQualityInference:
}
}
print(f"子文件夹 {subdir_name} 处理完成")
print(f"模型 {subdir_name} 处理完成")
except Exception as e:
print(f"处理子文件夹 {subdir_name} 失败: {e}")
print(f"处理模型 {subdir_name} 失败: {e}")
all_results[subdir_name] = {
'status': 'error',
'error': str(e)

22
src/core/steps/prediction_step.py
View File

@ -118,6 +118,8 @@ class PredictionStep:
print("\n" + "=" * 80)
print("步骤8: 预测水质参数")
print("=" * 80)
print(f"[PredictionStep] 准备执行预测，字典状态: {'Yes' if _external_models_dict else 'No'}"
f", 单模型状态: {'Yes' if _external_model else 'No'}")
step_start_time = time.time()
@ -153,8 +155,10 @@ class PredictionStep:
else:
print(f"检测到部分预测结果文件，缺少: {missing_targets}，将继续生成...")
all_results = {}
if _external_models_dict:
# 外部模型字典优先：每个 {subdir_name: model_obj} 对应一个水质参数，
# 外部模型字典优先：直接用字典的 keys 作为 targets 列表，
# 手动为每个模型创建 inference 实例并调用 inference_pipeline。
print(f"\n使用外部导入模型字典（{len(_external_models_dict)} 个模型）...")
for target_name, model_obj in _external_models_dict.items():
@ -172,11 +176,18 @@ class PredictionStep:
prediction_column=prediction_column,
)
prediction_files[target_name] = str(output_file)
all_results[target_name] = {
"status": "success",
"output_file": str(output_file),
"sample_count": len(predictions),
}
print(f" ✓ {target_name}: {len(predictions)} 个预测值")
except Exception as e:
print(f" ✗ {target_name}: 失败 — {type(e).__name__}: {e}")
prediction_files[target_name] = None
all_results[target_name] = {"status": "error", "error": str(e)}
else:
# 字典为空或不存在：回退到扫描 models_dir 子目录的传统逻辑
inferencer = WaterQualityInference(
models_dir,
external_model=_external_model,
@ -191,10 +202,13 @@ class PredictionStep:
output_format="csv",
external_model=_external_model,
external_model_path=_external_model_path,
external_models_dict=_external_models_dict,
)
for target_name, result in all_results.items():
if result.get("status") == "success":
prediction_files[target_name] = result["output_file"]
# batch_inference_multi_models 已确保返回字典，永不返回 None
if all_results:
for target_name, result in all_results.items():
if result.get("status") == "success":
prediction_files[target_name] = result["output_file"]
print(f"预测完成，结果保存在: {ml_prediction_dir}")

11
src/core/water_quality_inversion_pipeline_GUI.py
View File

@ -808,6 +808,13 @@ class WaterQualityInversionPipeline:
Returns:
预测结果文件路径字典（键为目标列名）
"""
_external_models_dict = kwargs.get('_external_models_dict')
_external_model = kwargs.get('_external_model')
_external_model_path = kwargs.get('_external_model_path')
_external_model_dir = kwargs.get('_external_model_dir')
print(f"[Pipeline] 收到字典: {'Yes' if _external_models_dict else 'No'}"
f", 收到单模型: {'Yes' if _external_model else 'No'}")
self._notify("started", "步骤8: 预测水质参数")
result = PredictionStep.predict_water_quality(
sampling_csv_path=sampling_csv_path,
@ -816,6 +823,10 @@ class WaterQualityInversionPipeline:
prediction_column=prediction_column,
output_dir=str(self.prediction_dir / "Machine_Learning_Prediction"),
_report_generator=self.report_generator,
_external_model=_external_model,
_external_model_path=_external_model_path,
_external_models_dict=_external_models_dict,
_external_model_dir=_external_model_dir,
)
self._record_step_time("步骤8: 预测水质参数", 0, 0)
self._notify("completed", f"预测完成，结果保存在: {self.prediction_dir}")

4
src/gui/core/worker_thread.py
View File

@ -333,6 +333,10 @@ class WorkerThread(QThread):
val = config.get(key)
if val is not None and val != "":
step_config[key] = val
if key == '_external_models_dict':
print(f"[Worker] 提取到的外部字典 Keys: {list(val.keys())}")
else:
print(f"[Worker] 透传 {key}: {val}")
step_config['skip_dependency_check'] = True

97
src/gui/panels/step8_panel.py
View File

@ -10,8 +10,10 @@ from pathlib import Path
from PyQt5.QtWidgets import (
QWidget, QVBoxLayout, QGroupBox, QFormLayout,
QPushButton, QCheckBox, QComboBox, QLineEdit, QMessageBox,
QFileDialog, QRadioButton,
QFileDialog, QRadioButton, QListWidget, QAbstractItemView, QHBoxLayout,
QListWidgetItem,
)
from PyQt5.QtCore import Qt
from src.gui.components.custom_widgets import FileSelectWidget
from src.gui.styles import ModernStylesheet
@ -75,6 +77,50 @@ class Step8Panel(QWidget):
self.external_model_widget.setVisible(False)
layout.addWidget(self.external_model_widget)
# -------- 已扫描模型列表（条件显示） --------
self.model_list_group = QGroupBox("选择参与预测的模型")
self.model_list_group.setVisible(False)
model_list_layout = QVBoxLayout()
self.model_list = QListWidget()
self.model_list.setMaximumHeight(130)
self.model_list.setSelectionMode(QAbstractItemView.NoSelection)
self.model_list.setStyleSheet("""
QListWidget {
border: 1px solid #C0C0C0;
border-radius: 4px;
background-color: #FFFFFF;
font-size: 12px;
}
QListWidget::item {
padding: 4px 6px;
border-bottom: 1px solid #F0F0F0;
}
QListWidget::item:selected {
background-color: transparent;
}
""")
model_list_layout.addWidget(self.model_list)
btn_row = QHBoxLayout()
self.btn_select_all = QPushButton("全选")
self.btn_select_all.setMaximumWidth(80)
self.btn_select_all.setStyleSheet(ModernStylesheet.get_button_stylesheet('default'))
self.btn_select_all.clicked.connect(self._select_all_models)
self.btn_select_none = QPushButton("全不选")
self.btn_select_none.setMaximumWidth(80)
self.btn_select_none.setStyleSheet(ModernStylesheet.get_button_stylesheet('default'))
self.btn_select_none.clicked.connect(self._select_none_models)
btn_row.addWidget(self.btn_select_all)
btn_row.addWidget(self.btn_select_none)
btn_row.addStretch()
model_list_layout.addLayout(btn_row)
self.model_list_group.setLayout(model_list_layout)
layout.addWidget(self.model_list_group)
# -------- 采样光谱CSV文件（用于独立运行）--------
self.sampling_csv_file = FileSelectWidget(
"采样光谱CSV:",
@ -134,9 +180,11 @@ class Step8Panel(QWidget):
return
is_external = self.use_external_model.isChecked()
self.external_model_widget.setVisible(is_external)
self.model_list_group.setVisible(is_external)
if not is_external:
self.external_models_dict = {}
self.external_model_dir = ""
self._clear_model_list()
def _scan_external_model_dir(self):
"""浏览模型母文件夹，自动扫描子目录中的 .joblib 文件"""
@ -200,9 +248,11 @@ class Step8Panel(QWidget):
)
self.external_model_widget.set_path("")
self.external_models_dict = {}
self._clear_model_list()
return
self.external_models_dict = models_found
self._populate_model_list(models_found)
names = sorted(models_found.keys())
display = f"已识别到 {len(names)} 个模型: {', '.join(names)}"
self.external_model_widget.set_path(display)
@ -216,6 +266,40 @@ class Step8Panel(QWidget):
f"加载失败 {len(errors)} 个：\n{err_lines}",
)
def _populate_model_list(self, models_dict):
"""将扫描到的模型填充到 QListWidget，每个条目可勾选，默认全选"""
self.model_list.clear()
for name in sorted(models_dict.keys()):
item = QListWidgetItem(name)
item.setFlags(item.flags() | Qt.ItemIsUserCheckable)
item.setCheckState(Qt.Checked)
self.model_list.addItem(item)
def _clear_model_list(self):
"""清空模型列表"""
self.model_list.clear()
def _select_all_models(self):
"""全选：设置所有条目为 Checked"""
for i in range(self.model_list.count()):
self.model_list.item(i).setCheckState(Qt.Checked)
def _select_none_models(self):
"""全不选：设置所有条目为 Unchecked"""
for i in range(self.model_list.count()):
self.model_list.item(i).setCheckState(Qt.Unchecked)
def _get_checked_models_dict(self):
"""从列表中提取用户勾选的模型，组装成字典返回"""
result = {}
for i in range(self.model_list.count()):
item = self.model_list.item(i)
if item.checkState() == Qt.Checked:
name = item.text()
if name in self.external_models_dict:
result[name] = self.external_models_dict[name]
return result
def update_from_config(self, work_dir=None, pipeline=None):
"""从全局配置自动填充采样光谱和模型目录
@ -347,11 +431,20 @@ class Step8Panel(QWidget):
"请先点击「浏览...」按钮选择模型母文件夹！",
)
return
# 只传递用户勾选的模型
checked_dict = self._get_checked_models_dict()
if not checked_dict:
QMessageBox.warning(
self,
"未选择模型",
"请至少勾选一个模型参与预测！",
)
return
main_window = self.window()
if hasattr(main_window, 'run_single_step'):
config = {
'step8': self.get_config(),
'_external_models_dict': self.external_models_dict,
'_external_models_dict': checked_dict,
'_external_model_dir': self.external_model_dir,
}
main_window.run_single_step('step8', config)