374 lines
16 KiB
Python
Executable File
374 lines
16 KiB
Python
Executable File
import os
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import shutil
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import importlib.util
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from models.training import Training
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from models.TrainingProject import TrainingProject
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def load_base_config(selected_model):
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"""Load base configuration for a specific YOLOX model"""
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model_name = selected_model.lower().replace('-', '_').replace('.pth', '')
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base_config_path = os.path.join(os.path.dirname(__file__), '..', 'data', f'{model_name}.py')
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if not os.path.exists(base_config_path):
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raise Exception(f'Base configuration not found for model: {model_name} at {base_config_path}')
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# Load the module dynamically
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spec = importlib.util.spec_from_file_location(f"base_config_{model_name}", base_config_path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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# Extract all attributes from BaseExp class
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base_exp = module.BaseExp()
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base_config = {}
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for attr in dir(base_exp):
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if not attr.startswith('_'):
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base_config[attr] = getattr(base_exp, attr)
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return base_config
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def generate_yolox_exp(training_id):
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"""Generate YOLOX exp.py file"""
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# Fetch training row from DB
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training = Training.query.get(training_id)
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if not training:
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training = Training.query.filter_by(project_details_id=training_id).first()
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if not training:
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raise Exception(f'Training not found for trainingId or project_details_id: {training_id}')
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# If transfer_learning is 'coco', generate exp using base config + custom settings
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if training.transfer_learning == 'coco':
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exp_content = generate_yolox_inference_exp(training_id, use_base_config=True)
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return {'type': 'custom', 'expContent': exp_content}
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# If transfer_learning is 'sketch', generate custom exp.py
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if training.transfer_learning == 'sketch':
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exp_content = generate_yolox_inference_exp(training_id, use_base_config=False)
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return {'type': 'custom', 'expContent': exp_content}
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raise Exception(f'Unknown transfer_learning type: {training.transfer_learning}')
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def save_yolox_exp(training_id, out_path):
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"""Save YOLOX exp.py to specified path"""
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exp_result = generate_yolox_exp(training_id)
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if exp_result['type'] == 'custom' and 'expContent' in exp_result:
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with open(out_path, 'w') as f:
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f.write(exp_result['expContent'])
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return out_path
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elif exp_result['type'] == 'default' and 'expPath' in exp_result:
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# Optionally copy the file if outPath is different
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if exp_result['expPath'] != out_path:
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shutil.copyfile(exp_result['expPath'], out_path)
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return out_path
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else:
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raise Exception('Unknown expResult type or missing content')
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def generate_yolox_inference_exp(training_id, options=None, use_base_config=False):
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"""Generate inference exp.py using DB values
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Args:
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training_id: The training/project_details ID
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options: Optional overrides for data paths
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use_base_config: If True, load base config and only override with user-defined values
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"""
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if options is None:
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options = {}
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training = Training.query.get(training_id)
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if not training:
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training = Training.query.filter_by(project_details_id=training_id).first()
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if not training:
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raise Exception(f'Training not found for trainingId or project_details_id: {training_id}')
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# Always use the project_details_id for annotation file names and paths
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project_details_id = training.project_details_id
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# Get annotation file names from options or use defaults
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# Use training.id (not project_details_id) for consistency with generate_training_json
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train_ann = options.get('train_ann', f'coco_project_{training_id}_train.json')
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val_ann = options.get('val_ann', f'coco_project_{training_id}_valid.json')
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test_ann = options.get('test_ann', f'coco_project_{training_id}_test.json')
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# Get data_dir - this should point to where IMAGES are located (not annotations)
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# YOLOX will combine data_dir + file_name from COCO JSON to find images
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# The annotations are in a separate location (output folder)
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from services.settings_service import get_setting
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from models.TrainingProjectDetails import TrainingProjectDetails
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if 'data_dir' in options:
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data_dir = options['data_dir']
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else:
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# Use the yolox_data_dir setting - this is where training images are stored
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data_dir = get_setting('yolox_data_dir', '/home/kitraining/To_Annotate/')
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# Ensure it ends with a separator
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if not data_dir.endswith(os.sep) and not data_dir.endswith('/'):
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data_dir += os.sep
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# Get num_classes from ProjectClass table (3NF)
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num_classes = 80
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try:
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from models.ProjectClass import ProjectClass
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training_project = TrainingProject.query.get(project_details_id)
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if training_project:
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# Count classes from ProjectClass table
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class_count = ProjectClass.query.filter_by(project_id=training_project.project_id).count()
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if class_count > 0:
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num_classes = class_count
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except Exception as e:
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print(f'Could not determine num_classes from ProjectClass: {e}')
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# Initialize config dictionary
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config = {}
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# If using base config (transfer learning from COCO), load protected parameters first
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if use_base_config and training.selected_model:
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try:
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base_config = load_base_config(training.selected_model)
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config.update(base_config)
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print(f'Loaded base config for {training.selected_model}: {list(base_config.keys())}')
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except Exception as e:
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print(f'Warning: Could not load base config for {training.selected_model}: {e}')
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print('Falling back to custom settings only')
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# Get size arrays from TrainingSize table (3NF)
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from models.TrainingSize import TrainingSize
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def get_size_array(training_id, size_type):
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"""Helper to get size array from TrainingSize table"""
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sizes = TrainingSize.query.filter_by(
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training_id=training_id,
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size_type=size_type
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).order_by(TrainingSize.value_order).all()
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return [s.value for s in sizes] if sizes else None
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input_size = get_size_array(training.id, 'input_size')
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test_size = get_size_array(training.id, 'test_size')
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mosaic_scale = get_size_array(training.id, 'mosaic_scale')
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mixup_scale = get_size_array(training.id, 'mixup_scale')
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# Override with user-defined values from training table (only if they exist and are not None)
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user_overrides = {
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'depth': training.depth,
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'width': training.width,
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'input_size': input_size,
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'mosaic_scale': mosaic_scale,
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'test_size': test_size,
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'enable_mixup': training.enable_mixup,
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'max_epoch': training.max_epoch,
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'warmup_epochs': training.warmup_epochs,
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'warmup_lr': training.warmup_lr,
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'basic_lr_per_img': training.basic_lr_per_img,
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'scheduler': training.scheduler,
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'no_aug_epochs': training.no_aug_epochs,
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'min_lr_ratio': training.min_lr_ratio,
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'ema': training.ema,
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'weight_decay': training.weight_decay,
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'momentum': training.momentum,
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'print_interval': training.print_interval,
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'eval_interval': training.eval_interval,
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'test_conf': training.test_conf,
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'nms_thre': training.nms_thre,
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'mosaic_prob': training.mosaic_prob,
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'mixup_prob': training.mixup_prob,
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'hsv_prob': training.hsv_prob,
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'flip_prob': training.flip_prob,
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'degrees': training.degrees,
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'translate': training.translate,
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'shear': training.shear,
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'mixup_scale': mixup_scale,
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'activation': training.activation,
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}
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# Only override if value is explicitly set (not None)
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for key, value in user_overrides.items():
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if value is not None:
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config[key] = value
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# Apply any additional options overrides
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config.update(options)
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# Set defaults for any missing required parameters
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config.setdefault('depth', 1.00)
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config.setdefault('width', 1.00)
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config.setdefault('input_size', [640, 640])
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config.setdefault('mosaic_scale', [0.1, 2])
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config.setdefault('random_size', [10, 20])
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config.setdefault('test_size', [640, 640])
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config.setdefault('enable_mixup', False)
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config.setdefault('exp_name', 'inference_exp')
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# Prepare data_dir for template - escape backslashes and remove trailing separator
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data_dir_clean = data_dir.rstrip('/\\')
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data_dir_escaped = data_dir_clean.replace('\\', '\\\\')
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# Calculate annotations directory (where JSON files are stored)
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# This is in the output folder, not with the images
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from models.TrainingProjectDetails import TrainingProjectDetails
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details = TrainingProjectDetails.query.get(project_details_id)
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if details:
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training_project = TrainingProject.query.get(details.project_id)
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project_name = training_project.title.replace(' ', '_') if training_project and training_project.title else f'project_{details.project_id}'
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else:
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project_name = f'project_{project_details_id}'
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training_folder_name = f"{training.exp_name or training.training_name or 'training'}_{training_id}"
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training_folder_name = training_folder_name.replace(' ', '_')
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output_base_path = get_setting('yolox_output_path', './backend')
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annotations_parent_dir = os.path.join(output_base_path, project_name, training_folder_name)
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annotations_parent_escaped = annotations_parent_dir.replace('\\', '\\\\')
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# Set output directory for checkpoints - models subdirectory
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models_dir = os.path.join(annotations_parent_dir, 'models')
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models_dir_escaped = models_dir.replace('\\', '\\\\')
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# Build exp content
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exp_content = f'''#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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# Copyright (c) Megvii, Inc. and its affiliates.
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import os
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from yolox.exp import Exp as MyExp
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class Exp(MyExp):
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def __init__(self):
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super(Exp, self).__init__()
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self.data_dir = "{data_dir_escaped}" # Where images are located
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self.annotations_dir = "{annotations_parent_escaped}" # Where annotation JSONs are located
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self.output_dir = "{models_dir_escaped}" # Where checkpoints will be saved
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self.train_ann = "{train_ann}"
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self.val_ann = "{val_ann}"
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self.test_ann = "{test_ann}"
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self.num_classes = {num_classes}
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# Disable train2017 subdirectory - our images are directly in data_dir
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self.name = ""
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# Set data workers for training
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self.data_num_workers = 8
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'''
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# Set pretrained_ckpt if transfer_learning is 'coco'
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if training.transfer_learning and isinstance(training.transfer_learning, str) and training.transfer_learning.lower() == 'coco':
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yolox_base_dir = '/home/kitraining/Yolox/YOLOX-main'
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selected_model = training.selected_model.replace('.pth', '') if training.selected_model else ''
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if selected_model:
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exp_content += f" self.pretrained_ckpt = r'{yolox_base_dir}/pretrained/{selected_model}.pth'\n"
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# Format arrays and values for Python code generation
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# Integer-only parameters (sizes, epochs, intervals)
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integer_params = {
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'input_size', 'test_size', 'random_size', 'max_epoch', 'warmup_epochs',
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'no_aug_epochs', 'print_interval', 'eval_interval', 'multiscale_range',
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'data_num_workers', 'num_classes'
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}
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def format_value(val, param_name=''):
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if isinstance(val, (list, tuple)):
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# Check if this parameter should have integer values
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if param_name in integer_params:
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# Convert all values to integers
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formatted_items = [str(int(float(item))) if isinstance(item, (int, float)) else str(item) for item in val]
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else:
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# Keep as floats or original type
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formatted_items = []
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for item in val:
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if isinstance(item, float):
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formatted_items.append(str(item))
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elif isinstance(item, int):
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formatted_items.append(str(item))
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else:
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formatted_items.append(str(item))
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return '(' + ', '.join(formatted_items) + ')'
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elif isinstance(val, bool):
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return str(val)
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elif isinstance(val, str):
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return f'"{val}"'
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elif isinstance(val, int):
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return str(val)
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elif isinstance(val, float):
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return str(val)
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else:
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return str(val)
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# Add all config parameters to exp
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for key, value in config.items():
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if key not in ['exp_name']: # exp_name is handled separately
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exp_content += f" self.{key} = {format_value(value, key)}\n"
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# Add get_dataset override using name parameter for image directory
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exp_content += '''
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def get_dataset(self, cache=False, cache_type="ram"):
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"""Override to use name parameter for images directory"""
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from yolox.data import COCODataset
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# COCODataset constructs image paths as: os.path.join(data_dir, name, file_name)
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# YOLOX adds "annotations/" to data_dir automatically, so we pass annotations_dir directly
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# Use empty string for name since we have absolute paths in JSON
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return COCODataset(
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data_dir=self.annotations_dir,
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json_file=self.train_ann,
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name="",
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img_size=self.input_size,
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preproc=self.preproc if hasattr(self, 'preproc') else None,
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cache=cache,
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cache_type=cache_type,
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)
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def get_eval_dataset(self, **kwargs):
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"""Override eval dataset using name parameter"""
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from yolox.data import COCODataset, ValTransform
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testdev = kwargs.get("testdev", False)
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legacy = kwargs.get("legacy", False)
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return COCODataset(
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data_dir=self.annotations_dir,
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json_file=self.val_ann if not testdev else self.test_ann,
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name="",
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img_size=self.test_size,
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preproc=ValTransform(legacy=legacy), # Use proper validation transform
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)
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def get_eval_loader(self, batch_size, is_distributed, **kwargs):
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"""Standard YOLOX eval loader - matches official implementation"""
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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valdataset = self.get_eval_dataset(**kwargs)
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if is_distributed:
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batch_size = batch_size // dist.get_world_size()
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sampler = torch.utils.data.distributed.DistributedSampler(
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valdataset, shuffle=False
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)
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else:
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sampler = torch.utils.data.SequentialSampler(valdataset)
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dataloader_kwargs = {
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"num_workers": self.data_num_workers,
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"pin_memory": True,
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"sampler": sampler,
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}
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dataloader_kwargs["batch_size"] = batch_size
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val_loader = DataLoader(valdataset, **dataloader_kwargs)
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return val_loader
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'''
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# Add exp_name at the end (uses dynamic path)
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exp_content += f''' self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
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'''
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return exp_content
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def save_yolox_inference_exp(training_id, out_path, options=None):
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"""Save inference exp.py to custom path"""
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exp_content = generate_yolox_inference_exp(training_id, options, use_base_config=False)
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with open(out_path, 'w') as f:
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f.write(exp_content)
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return out_path
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