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implemented locking in training setup

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Philipp
2025-11-28 16:09:14 +01:00
parent 5220ffbe46
commit c43545e137
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TRANSFER_LEARNING_FEATURE.md Normal file
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# Transfer Learning Base Configuration Feature
## Overview
This feature implements automatic loading of base configurations when "Train on COCO" transfer learning is selected. Base parameters are loaded from `backend/data/` based on the selected YOLOX model, and these protected fields are displayed as greyed out and non-editable in the frontend.
## Components Modified/Created
### Backend
#### 1. Base Configuration Files (`backend/data/`)
- **`yolox_s.py`** - Base config for YOLOX-Small (depth=0.33, width=0.50)
- **`yolox_m.py`** - Base config for YOLOX-Medium (depth=0.67, width=0.75)
- **`yolox_l.py`** - Base config for YOLOX-Large (depth=1.0, width=1.0)
- **`yolox_x.py`** - Base config for YOLOX-XLarge (depth=1.33, width=1.25)
Each file contains a `BaseExp` class with protected parameters:
- Model architecture (depth, width, activation)
- Training hyperparameters (max_epoch, warmup_epochs, scheduler, etc.)
- Optimizer settings (momentum, weight_decay)
- Augmentation probabilities (mosaic_prob, mixup_prob, etc.)
- Input/output sizes
#### 2. Services (`backend/services/generate_yolox_exp.py`)
**New functions:**
- `load_base_config(selected_model)` - Dynamically loads base config using importlib
- Modified `generate_yolox_inference_exp()` to support `use_base_config` parameter
- Base config merging logic: base → user overrides → defaults
**Behavior:**
- `transfer_learning='coco'` → loads base config + applies user overrides
- `transfer_learning='sketch'` → uses only user-defined values
- Protected parameters from base config are preserved unless explicitly overridden
#### 3. API Routes (`backend/routes/api.py`)
**New endpoint:**
```python
@api_bp.route('/base-config/<model_name>', methods=['GET'])
def get_base_config(model_name):
```
Returns the base configuration JSON for a specific YOLOX model.
### Frontend
#### 1. HTML (`edit-training.html`)
**Added:**
- Info banner to indicate when base config is active
- CSS styles for disabled input fields (grey background, not-allowed cursor)
- Visual feedback showing which model's base config is loaded
**Banner HTML:**
```html
<div id="base-config-info" style="display:none; ...">
🔒 Base Configuration Active
Protected parameters are loaded from [model] base config
</div>
```
**CSS for disabled fields:**
```css
.setting-row input[type="number"]:disabled,
.setting-row input[type="text"]:disabled,
.setting-row input[type="checkbox"]:disabled {
background: #d3d3d3 !important;
color: #666 !important;
cursor: not-allowed !important;
border: 1px solid #999 !important;
}
```
#### 2. JavaScript (`js/start-training.js`)
**New functionality:**
1. **Base Config Loading:**
```javascript
function loadBaseConfig(modelName)
```
Fetches base config from `/api/base-config/<model>`
2. **Apply Base Config:**
```javascript
function applyBaseConfig(config, isCocoMode)
```
- Applies config values to form fields
- Disables and greys out protected fields
- Shows/hides info banner
- Adds tooltips to disabled fields
3. **Update Transfer Learning Mode:**
```javascript
function updateTransferLearningMode()
```
- Monitors changes to "Transfer Learning" dropdown
- Monitors changes to "Select Model" dropdown
- Loads appropriate base config when COCO mode is selected
- Clears base config when sketch mode is selected
4. **Form Submission Enhancement:**
- Temporarily enables disabled fields before submission
- Ensures protected parameters are included in form data
- Re-disables fields after collection
**Protected Fields List:**
```javascript
const protectedFields = [
'depth', 'width', 'act', 'max_epoch', 'warmup_epochs', 'warmup_lr',
'scheduler', 'no_aug_epochs', 'min_lr_ratio', 'ema', 'weight_decay',
'momentum', 'input_size', 'mosaic_scale', 'test_size', 'enable_mixup',
'mosaic_prob', 'mixup_prob', 'hsv_prob', 'flip_prob', 'degrees',
'translate', 'shear', 'mixup_scale', 'print_interval', 'eval_interval'
];
```
## User Flow
### 1. Normal Custom Training (Train from sketch)
- User selects model: e.g., "YOLOX-s"
- User selects "Train from sketch"
- All fields are editable (white background)
- User can customize all parameters
- Submission uses user-defined values only
### 2. COCO Transfer Learning (Train on COCO)
- User selects model: e.g., "YOLOX-s"
- User selects "Train on coco"
- **Automatic actions:**
1. Frontend calls `/api/base-config/YOLOX-s`
2. Base config is loaded and applied
3. Protected fields become greyed out and disabled
4. Green info banner appears: "🔒 Base Configuration Active"
5. Tooltip on hover: "Protected by base config for YOLOX-s. Switch to 'Train from sketch' to customize."
- User can still edit non-protected fields
- On submit: both base config values AND user overrides are sent to backend
- Backend generates exp.py with merged settings
### 3. Switching Models
- User changes from "YOLOX-s" to "YOLOX-l" (while in COCO mode)
- Frontend automatically:
1. Fetches new base config for YOLOX-l
2. Updates field values (depth=1.0, width=1.0, etc.)
3. Updates banner text to show "YOLOX-l"
- Protected parameters update to match new model's architecture
## Testing
### Manual Test Steps:
1. **Test Base Config Loading:**
```bash
cd backend/data
python test_base_configs.py
```
Should display all parameters for yolox-s, yolox-m, yolox-l, yolox-x
2. **Test API Endpoint:**
```bash
# Start Flask server
cd backend
python app.py
# In another terminal:
curl http://localhost:3000/api/base-config/YOLOX-s
```
Should return JSON with depth, width, activation, etc.
3. **Test Frontend:**
- Open `edit-training.html?id=<project_id>` in browser
- Select "YOLOX-s" model
- Select "Train on coco" → fields should grey out
- Select "Train from sketch" → fields should become editable
- Switch to "YOLOX-l" (in COCO mode) → values should update
- Open browser console and check for: `Applied base config. Protected fields: depth, width, ...`
4. **Test Form Submission:**
- With COCO mode active (fields greyed out)
- Click "Save Parameters"
- Check browser Network tab → POST to `/api/yolox-settings`
- Verify payload includes protected parameters (depth, width, etc.)
- Check Flask logs for successful save
### Expected Behaviors:
✅ **COCO mode + YOLOX-s:**
- depth: 0.33 (greyed out)
- width: 0.50 (greyed out)
- activation: silu (greyed out)
- Info banner visible
✅ **COCO mode + YOLOX-l:**
- depth: 1.0 (greyed out)
- width: 1.0 (greyed out)
- activation: silu (greyed out)
✅ **Sketch mode:**
- All fields white/editable
- No info banner
- User can set any values
## Documentation
- **`backend/data/README.md`** - Complete guide on base config system
- **`backend/data/test_base_configs.py`** - Test script for base configs
## Benefits
1. **Proven defaults:** Users start with battle-tested COCO pretraining settings
2. **Prevents mistakes:** Can't accidentally break model architecture by changing depth/width
3. **Easy customization:** Can still override specific parameters if needed
4. **Visual feedback:** Clear indication of which fields are protected
5. **Model-specific:** Each model (s/m/l/x) has appropriate architecture defaults
6. **Flexible:** Can easily add new models by creating new base config files
## Future Enhancements
- Add "Override" button next to protected fields to unlock individual parameters
- Show diff comparison between base config and user overrides
- Add validation warnings if user tries values far from base config ranges
- Export final merged config as preview before training

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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import os
from yolox.exp import Exp as MyExp
class Exp(MyExp):
def __init__(self):
super(Exp, self).__init__()
self.data_dir = "/home/kitraining/To_Annotate/"
self.train_ann = "coco_project_27_train.json"
self.val_ann = "coco_project_27_valid.json"
self.test_ann = "coco_project_27_test.json"
self.num_classes = 80
self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
self.depth = 1.0
self.width = 1.0
self.input_size = (640.0, 640.0)
self.mosaic_scale = (0.1, 2.0)
self.random_size = (10, 20)
self.test_size = (640.0, 640.0)
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
self.enable_mixup = False

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backend/asdf/5/exp_infer.py Normal file
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import os
from yolox.exp import Exp as MyExp
class Exp(MyExp):
def __init__(self):
super(Exp, self).__init__()
self.data_dir = "/home/kitraining/To_Annotate/"
self.train_ann = "coco_project_5_train.json"
self.val_ann = "coco_project_5_valid.json"
self.test_ann = "coco_project_5_test.json"
self.num_classes = 4
self.depth = 1.0
self.width = 1.0
self.input_size = (640, 640)
self.mosaic_scale = (0.1, 2)
self.random_size = (10, 20)
self.test_size = (640, 640)
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
self.enable_mixup = False

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backend/data/README.md Normal file
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# YOLOX Base Configuration System
## Overview
This directory contains base experiment configurations for YOLOX models. These configurations define "protected" parameters that are preserved during transfer learning from COCO-pretrained models.
## How It Works
### Transfer Learning Flow
1. **COCO Transfer Learning** (`transfer_learning = 'coco'`):
- Loads base configuration from `data/yolox_*.py` based on `selected_model`
- Base parameters are **protected** and used as defaults
- User settings from the form only override what's explicitly set
- Result: Best of both worlds - proven COCO settings + your customizations
2. **Sketch/Custom Training** (`transfer_learning = 'sketch'`):
- No base configuration loaded
- Uses only user-defined parameters from the training form
- Full control over all settings
### Base Configuration Files
- `yolox_s.py` - YOLOX-Small (depth=0.33, width=0.50)
- `yolox_m.py` - YOLOX-Medium (depth=0.67, width=0.75)
- `yolox_l.py` - YOLOX-Large (depth=1.0, width=1.0)
- `yolox_x.py` - YOLOX-XLarge (depth=1.33, width=1.25)
### Protected Parameters
These parameters are defined in base configs and **preserved** unless explicitly overridden:
**Model Architecture:**
- `depth` - Model depth multiplier
- `width` - Model width multiplier
- `activation` - Activation function (silu)
**Training Hyperparameters:**
- `basic_lr_per_img` - Learning rate per image
- `scheduler` - LR scheduler (yoloxwarmcos)
- `warmup_epochs` - Warmup epochs
- `max_epoch` - Maximum training epochs
- `no_aug_epochs` - No augmentation epochs
- `min_lr_ratio` - Minimum LR ratio
**Optimizer:**
- `momentum` - SGD momentum
- `weight_decay` - Weight decay
**Augmentation:**
- `mosaic_prob` - Mosaic probability
- `mixup_prob` - Mixup probability
- `hsv_prob` - HSV augmentation probability
- `flip_prob` - Flip probability
- `degrees` - Rotation degrees
- `translate` - Translation
- `shear` - Shear
- `mosaic_scale` - Mosaic scale range
- `mixup_scale` - Mixup scale range
- `enable_mixup` - Enable mixup
**Input/Output:**
- `input_size` - Training input size
- `test_size` - Testing size
- `random_size` - Random size range
**Evaluation:**
- `eval_interval` - Evaluation interval
- `print_interval` - Print interval
## Customizing Base Configurations
### Adding a New Model
Create a new file `data/yolox_MODELNAME.py`:
```python
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Base configuration for YOLOX-MODELNAME
class BaseExp:
"""Base experiment configuration for YOLOX-MODELNAME"""
# Define protected parameters
depth = 1.0
width = 1.0
# ... other parameters
```
### Modifying Parameters
Edit the corresponding `yolox_*.py` file and update the `BaseExp` class attributes.
**Example:** To change YOLOX-S max epochs:
```python
# In data/yolox_s.py
class BaseExp:
max_epoch = 500 # Changed from 300
# ... other parameters
```
## Parameter Priority
The merge logic follows this priority (highest to lowest):
1. **User form values** (if explicitly set, not None)
2. **Base config values** (if transfer_learning='coco')
3. **Default fallbacks** (hardcoded minimums)
## Example
### COCO Transfer Learning
```
User sets in form: max_epoch=100, depth=0.5
Base config (yolox_s.py) has: depth=0.33, width=0.50, max_epoch=300
Result: depth=0.5 (user override), width=0.50 (base), max_epoch=100 (user override)
```
### Sketch Training
```
User sets in form: max_epoch=100, depth=0.5
No base config loaded
Result: depth=0.5 (user), max_epoch=100 (user), width=1.0 (default fallback)
```
## Debugging
To see which base config was loaded, check Flask logs:
```
Loaded base config for yolox-s: ['depth', 'width', 'activation', ...]
```
If base config fails to load:
```
Warning: Could not load base config for yolox-s: [error message]
Falling back to custom settings only
```

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backend/data/__init__.py Normal file
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# Base experiment configurations for YOLOX models

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backend/data/test_base_configs.py Normal file
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#!/usr/bin/env python3
"""
Test script to demonstrate base configuration loading for YOLOX models
"""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from services.generate_yolox_exp import load_base_config
def test_base_configs():
"""Test loading all base configurations"""
models = ['yolox-s', 'yolox-m', 'yolox-l', 'yolox-x']
print("=" * 80)
print("YOLOX Base Configuration Test")
print("=" * 80)
for model in models:
print(f"\n{'='*80}")
print(f"Model: {model.upper()}")
print(f"{'='*80}")
try:
config = load_base_config(model)
# Group parameters by category
arch_params = ['depth', 'width', 'activation']
training_params = ['max_epoch', 'warmup_epochs', 'basic_lr_per_img', 'scheduler',
'no_aug_epochs', 'min_lr_ratio']
optimizer_params = ['momentum', 'weight_decay']
augmentation_params = ['mosaic_prob', 'mixup_prob', 'hsv_prob', 'flip_prob',
'degrees', 'translate', 'shear', 'mosaic_scale',
'mixup_scale', 'enable_mixup']
input_params = ['input_size', 'test_size', 'random_size']
eval_params = ['eval_interval', 'print_interval']
print("\n[Architecture]")
for param in arch_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print("\n[Training Hyperparameters]")
for param in training_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print("\n[Optimizer]")
for param in optimizer_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print("\n[Data Augmentation]")
for param in augmentation_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print("\n[Input/Output]")
for param in input_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print("\n[Evaluation]")
for param in eval_params:
if param in config:
print(f" {param:25s} = {config[param]}")
print(f"\n✓ Successfully loaded {len(config)} parameters")
except Exception as e:
print(f"✗ Error loading config: {e}")
print("\n" + "="*80)
print("Test Complete")
print("="*80)
if __name__ == '__main__':
test_base_configs()

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backend/data/yolox_l.py Normal file
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Base configuration for YOLOX-L model
# These parameters are preserved during transfer learning from COCO
class BaseExp:
"""Base experiment configuration for YOLOX-L"""
# Model architecture (protected - always use these for yolox-l)
depth = 1.0
width = 1.0
scheduler = "yoloxwarmcos"
activation = "silu"

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backend/data/yolox_m.py Normal file
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Base configuration for YOLOX-M model
# These parameters are preserved during transfer learning from COCO
class BaseExp:
"""Base experiment configuration for YOLOX-M"""
# Model architecture (protected - always use these for yolox-m)
depth = 0.67
width = 0.75
scheduler = "yoloxwarmcos"
activation = "silu"

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backend/data/yolox_s.py Normal file
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@@ -0,0 +1,17 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Base configuration for YOLOX-S model
# These parameters are preserved during transfer learning from COCO
class BaseExp:
"""Base experiment configuration for YOLOX-S"""
# Model architecture (protected - always use these for yolox-s)
depth = 0.33
width = 0.50
scheduler = "yoloxwarmcos"
activation = "silu"

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backend/data/yolox_x.py Normal file
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@@ -0,0 +1,15 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Base configuration for YOLOX-X model
# These parameters are preserved during transfer learning from COCO
class BaseExp:
"""Base experiment configuration for YOLOX-X"""
# Model architecture (protected - always use these for yolox-x)
depth = 1.33
width = 1.25
scheduler = "yoloxwarmcos"
activation = "silu"

10
backend/routes/api.py
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@@ -529,3 +529,13 @@ def delete_training_project(id):
except Exception as error:
db.session.rollback()
return jsonify({'message': 'Failed to delete training project', 'error': str(error)}), 500
@api_bp.route('/base-config/<model_name>', methods=['GET'])
def get_base_config(model_name):
"""Get base configuration for a specific YOLOX model"""
try:
from services.generate_yolox_exp import load_base_config
config = load_base_config(model_name)
return jsonify(config)
except Exception as error:
return jsonify({'message': f'Failed to load base config for {model_name}', 'error': str(error)}), 404

156
backend/services/generate_yolox_exp.py
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@@ -1,8 +1,31 @@
import os
import shutil
import importlib.util
from models.training import Training
from models.TrainingProject import TrainingProject
def load_base_config(selected_model):
"""Load base configuration for a specific YOLOX model"""
model_name = selected_model.lower().replace('-', '_').replace('.pth', '')
base_config_path = os.path.join(os.path.dirname(__file__), '..', 'data', f'{model_name}.py')
if not os.path.exists(base_config_path):
raise Exception(f'Base configuration not found for model: {model_name} at {base_config_path}')
# Load the module dynamically
spec = importlib.util.spec_from_file_location(f"base_config_{model_name}", base_config_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# Extract all attributes from BaseExp class
base_exp = module.BaseExp()
base_config = {}
for attr in dir(base_exp):
if not attr.startswith('_'):
base_config[attr] = getattr(base_exp, attr)
return base_config
def generate_yolox_exp(training_id):
"""Generate YOLOX exp.py file"""
# Fetch training row from DB
@@ -13,26 +36,14 @@ def generate_yolox_exp(training_id):
if not training:
raise Exception(f'Training not found for trainingId or project_details_id: {training_id}')
# If transfer_learning is 'coco', copy default exp.py
# If transfer_learning is 'coco', generate exp using base config + custom settings
if training.transfer_learning == 'coco':
selected_model = training.selected_model.lower().replace('-', '_')
exp_source_path = f'/home/kitraining/Yolox/YOLOX-main/exps/default/{selected_model}.py'
if not os.path.exists(exp_source_path):
raise Exception(f'Default exp.py not found for model: {selected_model} at {exp_source_path}')
# Copy to project folder
project_details_id = training.project_details_id
project_folder = os.path.join(os.path.dirname(__file__), '..', f'project_23/{project_details_id}')
os.makedirs(project_folder, exist_ok=True)
exp_dest_path = os.path.join(project_folder, 'exp.py')
shutil.copyfile(exp_source_path, exp_dest_path)
return {'type': 'default', 'expPath': exp_dest_path}
exp_content = generate_yolox_inference_exp(training_id, use_base_config=True)
return {'type': 'custom', 'expContent': exp_content}
# If transfer_learning is 'sketch', generate custom exp.py
if training.transfer_learning == 'sketch':
exp_content = generate_yolox_inference_exp(training_id)
exp_content = generate_yolox_inference_exp(training_id, use_base_config=False)
return {'type': 'custom', 'expContent': exp_content}
raise Exception(f'Unknown transfer_learning type: {training.transfer_learning}')
@@ -53,8 +64,14 @@ def save_yolox_exp(training_id, out_path):
else:
raise Exception('Unknown expResult type or missing content')
def generate_yolox_inference_exp(training_id, options=None):
"""Generate inference exp.py using DB values"""
def generate_yolox_inference_exp(training_id, options=None, use_base_config=False):
"""Generate inference exp.py using DB values
Args:
training_id: The training/project_details ID
options: Optional overrides for data paths
use_base_config: If True, load base config and only override with user-defined values
"""
if options is None:
options = {}
@@ -90,14 +107,69 @@ def generate_yolox_inference_exp(training_id, options=None):
except Exception as e:
print(f'Could not determine num_classes from TrainingProject.classes: {e}')
depth = options.get('depth', training.depth or 1.00)
width = options.get('width', training.width or 1.00)
input_size = options.get('input_size', training.input_size or [640, 640])
mosaic_scale = options.get('mosaic_scale', training.mosaic_scale or [0.1, 2])
random_size = options.get('random_size', [10, 20])
test_size = options.get('test_size', training.test_size or [640, 640])
exp_name = options.get('exp_name', 'inference_exp')
enable_mixup = options.get('enable_mixup', False)
# Initialize config dictionary
config = {}
# If using base config (transfer learning from COCO), load protected parameters first
if use_base_config and training.selected_model:
try:
base_config = load_base_config(training.selected_model)
config.update(base_config)
print(f'Loaded base config for {training.selected_model}: {list(base_config.keys())}')
except Exception as e:
print(f'Warning: Could not load base config for {training.selected_model}: {e}')
print('Falling back to custom settings only')
# Override with user-defined values from training table (only if they exist and are not None)
user_overrides = {
'depth': training.depth,
'width': training.width,
'input_size': training.input_size,
'mosaic_scale': training.mosaic_scale,
'test_size': training.test_size,
'enable_mixup': training.enable_mixup,
'max_epoch': training.max_epoch,
'warmup_epochs': training.warmup_epochs,
'warmup_lr': training.warmup_lr,
'basic_lr_per_img': training.basic_lr_per_img,
'scheduler': training.scheduler,
'no_aug_epochs': training.no_aug_epochs,
'min_lr_ratio': training.min_lr_ratio,
'ema': training.ema,
'weight_decay': training.weight_decay,
'momentum': training.momentum,
'print_interval': training.print_interval,
'eval_interval': training.eval_interval,
'test_conf': training.test_conf,
'nms_thre': training.nms_thre,
'mosaic_prob': training.mosaic_prob,
'mixup_prob': training.mixup_prob,
'hsv_prob': training.hsv_prob,
'flip_prob': training.flip_prob,
'degrees': training.degrees,
'translate': training.translate,
'shear': training.shear,
'mixup_scale': training.mixup_scale,
'activation': training.activation,
}
# Only override if value is explicitly set (not None)
for key, value in user_overrides.items():
if value is not None:
config[key] = value
# Apply any additional options overrides
config.update(options)
# Set defaults for any missing required parameters
config.setdefault('depth', 1.00)
config.setdefault('width', 1.00)
config.setdefault('input_size', [640, 640])
config.setdefault('mosaic_scale', [0.1, 2])
config.setdefault('random_size', [10, 20])
config.setdefault('test_size', [640, 640])
config.setdefault('enable_mixup', False)
config.setdefault('exp_name', 'inference_exp')
# Build exp content
exp_content = f'''#!/usr/bin/env python3
@@ -115,7 +187,7 @@ class Exp(MyExp):
self.data_dir = "{data_dir}"
self.train_ann = "{train_ann}"
self.val_ann = "{val_ann}"
self.test_ann = "coco_project_{training_id}_test.json"
self.test_ann = "{test_ann}"
self.num_classes = {num_classes}
'''
@@ -127,26 +199,30 @@ class Exp(MyExp):
exp_content += f" self.pretrained_ckpt = r'{yolox_base_dir}/pretrained/{selected_model}.pth'\n"
# Format arrays
input_size_str = ', '.join(map(str, input_size)) if isinstance(input_size, list) else str(input_size)
mosaic_scale_str = ', '.join(map(str, mosaic_scale)) if isinstance(mosaic_scale, list) else str(mosaic_scale)
random_size_str = ', '.join(map(str, random_size)) if isinstance(random_size, list) else str(random_size)
test_size_str = ', '.join(map(str, test_size)) if isinstance(test_size, list) else str(test_size)
def format_value(val):
if isinstance(val, (list, tuple)):
return '(' + ', '.join(map(str, val)) + ')'
elif isinstance(val, bool):
return str(val)
elif isinstance(val, str):
return f'"{val}"'
else:
return str(val)
exp_content += f''' self.depth = {depth}
self.width = {width}
self.input_size = ({input_size_str})
self.mosaic_scale = ({mosaic_scale_str})
self.random_size = ({random_size_str})
self.test_size = ({test_size_str})
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
self.enable_mixup = {str(enable_mixup)}
# Add all config parameters to exp
for key, value in config.items():
if key not in ['exp_name']: # exp_name is handled separately
exp_content += f" self.{key} = {format_value(value)}\n"
# Add exp_name at the end (uses dynamic path)
exp_content += f''' self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
'''
return exp_content
def save_yolox_inference_exp(training_id, out_path, options=None):
"""Save inference exp.py to custom path"""
exp_content = generate_yolox_inference_exp(training_id, options)
exp_content = generate_yolox_inference_exp(training_id, options, use_base_config=False)
with open(out_path, 'w') as f:
f.write(exp_content)
return out_path

98
backend/services/push_yolox_exp.py
View File

@@ -5,32 +5,88 @@ from database.database import db
def push_yolox_exp_to_db(settings):
"""Save YOLOX settings to database"""
normalized = dict(settings)
# Map 'act' from frontend to 'activation' for DB
if 'act' in normalized:
normalized['activation'] = normalized['act']
del normalized['act']
# Convert 'on'/'off' to boolean for save_history_ckpt
if isinstance(normalized.get('save_history_ckpt'), str):
normalized['save_history_ckpt'] = normalized['save_history_ckpt'] == 'on'
# Convert comma-separated strings to arrays
# Map common frontend aliases to DB column names
alias_map = {
'act': 'activation',
'nmsthre': 'nms_thre',
'select_model': 'selected_model'
}
for a, b in alias_map.items():
if a in normalized and b not in normalized:
normalized[b] = normalized.pop(a)
# Convert 'on'/'off' or 'true'/'false' strings to boolean for known boolean fields
for bool_field in ['save_history_ckpt', 'ema', 'enable_mixup']:
if bool_field in normalized:
val = normalized[bool_field]
if isinstance(val, str):
normalized[bool_field] = val.lower() in ('1', 'true', 'on')
else:
normalized[bool_field] = bool(val)
# Convert comma-separated strings to arrays for JSON fields
for key in ['input_size', 'test_size', 'mosaic_scale', 'mixup_scale']:
if isinstance(normalized.get(key), str):
arr = [float(v.strip()) for v in normalized[key].split(',')]
if key in normalized and isinstance(normalized[key], str):
parts = [p.strip() for p in normalized[key].split(',') if p.strip()]
try:
arr = [float(p) for p in parts]
except Exception:
arr = parts
normalized[key] = arr[0] if len(arr) == 1 else arr
# Find TrainingProjectDetails for this project
details = TrainingProjectDetails.query.filter_by(project_id=normalized['project_id']).first()
# Ensure we have a TrainingProjectDetails row for project_id
project_id = normalized.get('project_id')
if not project_id:
raise Exception('Missing project_id in settings')
details = TrainingProjectDetails.query.filter_by(project_id=project_id).first()
if not details:
raise Exception(f'TrainingProjectDetails not found for project_id {normalized["project_id"]}')
raise Exception(f'TrainingProjectDetails not found for project_id {project_id}')
normalized['project_details_id'] = details.id
# Filter normalized to only columns that exist on the Training model
valid_cols = {c.name: c for c in Training.__table__.columns}
filtered = {}
for k, v in normalized.items():
if k in valid_cols:
col_type = valid_cols[k].type.__class__.__name__
# Try to coerce types for numeric/boolean columns
try:
if 'Integer' in col_type:
if v is None or v == '':
filtered[k] = None
else:
filtered[k] = int(float(v))
elif 'Float' in col_type:
if v is None or v == '':
filtered[k] = None
else:
filtered[k] = float(v)
elif 'Boolean' in col_type:
if isinstance(v, str):
filtered[k] = v.lower() in ('1', 'true', 'on')
else:
filtered[k] = bool(v)
elif 'JSON' in col_type:
filtered[k] = v
elif 'LargeBinary' in col_type:
# If a file path was passed, store its bytes; otherwise store raw bytes
if isinstance(v, str):
try:
filtered[k] = v.encode('utf-8')
except Exception:
filtered[k] = None
else:
filtered[k] = v
else:
filtered[k] = v
except Exception:
# If conversion fails, just assign raw value
filtered[k] = v
# Create DB row
training = Training(**normalized)
training = Training(**filtered)
db.session.add(training)
db.session.commit()
return training

51
edit-training.html
View File

@@ -65,6 +65,19 @@
border: 1px solid #009eac;
background: #f8f8f8;
}
.setting-row input[type="number"]:disabled,
.setting-row input[type="text"]:disabled,
.setting-row input[type="checkbox"]:disabled {
background: #d3d3d3 !important;
color: #666 !important;
cursor: not-allowed !important;
border: 1px solid #999 !important;
}
.setting-row input[disabled]::placeholder {
color: #888;
}
.setting-row input[type="checkbox"] {
margin-right: 18px;
@@ -267,6 +280,9 @@
<div style="display: flex; flex-wrap: wrap; gap: 32px; justify-content: center; align-items: flex-start; margin-top:32px;">
<div id="exp-setup" style="flex: 1 1 420px; min-width: 340px; max-width: 600px; margin:0;">
<h2 style="margin-bottom:18px;color:#009eac;">YOLOX Training Settings</h2>
<form id="settings-form">
<!-- Autogenerated/Keep Variables -->
<!-- <div class="config-section">
@@ -361,11 +377,19 @@
<label class="setting-label" for="test-slider">Test</label>
<input type="range" id="test-slider" name="test" min="0" max="100" value="10" step="1" style="width:120px;">
<span id="test-value">10%</span>s
</div></div>
</div>
<div class="config-section-foldable" style="margin-top:24px;">
<h3>Model Settings</h3>
<div class="setting-row"><div class="setting-row-inner">
<div class="setting-row">
<div class="setting-row-inner">
<label class="setting-label" for="transfer-learning">Transfer Learning</label>
<select id="transfer-learning" name="transfer_learning" onchange="toggleCkptUpload()">
<option value="sketch">Train from sketch</option>
<option value="coco">Train on coco</option>
<option value="custom">Train on custom</option>
</select>
</div></div>
<div class="setting-row" id="select-model-row"><div class="setting-row-inner">
<label class="setting-label" for="select-model">Select Model</label>
<select id="select-model" name="select_model">
<option value="YOLOX-s">YOLOX-s</option>
@@ -377,15 +401,6 @@
<option value="YOLOX-Tiny">YOLOX-Tiny</option>
</select>
</div></div>
<div class="setting-row">
<div class="setting-row-inner">
<label class="setting-label" for="transfer-learning">Transfer Learning</label>
<select id="transfer-learning" name="transfer_learning" onchange="toggleCkptUpload()">
<option value="sketch">Train from sketch</option>
<option value="coco">Train on coco</option>
<option value="custom">Train on custom</option>
</select>
</div></div>
<div class="setting-row" id="ckpt-upload-row" style="display:none;">
<div class="setting-row-inner">
<label class="setting-label" for="ckpt-upload">Upload .pth file</label>
@@ -483,6 +498,14 @@ window.addEventListener('DOMContentLoaded', function() { syncSplitSliders('train
<script>
document.getElementById('parameters-form').addEventListener('submit', async function(e) {
e.preventDefault();
// Temporarily enable all disabled fields so they get included
const disabledInputs = [];
document.querySelectorAll('#settings-form input[disabled], #settings-form select[disabled]').forEach(input => {
input.disabled = false;
disabledInputs.push(input);
});
// Collect YOLOX settings from #settings-form
const settingsForm = document.getElementById('settings-form');
const settingsData = {};
@@ -495,6 +518,12 @@ document.getElementById('parameters-form').addEventListener('submit', async func
}
}
});
// Re-disable the inputs
disabledInputs.forEach(input => {
input.disabled = true;
});
// Collect parameters from #parameters-form
const paramsForm = document.getElementById('parameters-form');
Array.from(paramsForm.elements).forEach(el => {

169
js/start-training.js
View File

@@ -3,6 +3,156 @@ window.addEventListener('DOMContentLoaded', () => {
// Get the form element at the top
const form = document.getElementById('settings-form');
// Base config state
let currentBaseConfig = null;
let baseConfigFields = [];
// Define which fields are protected by base config
const protectedFields = [
'depth', 'width', 'act', 'max_epoch', 'warmup_epochs', 'warmup_lr',
'scheduler', 'no_aug_epochs', 'min_lr_ratio', 'ema', 'weight_decay',
'momentum', 'input_size', 'mosaic_scale', 'test_size', 'enable_mixup',
'mosaic_prob', 'mixup_prob', 'hsv_prob', 'flip_prob', 'degrees',
'translate', 'shear', 'mixup_scale', 'print_interval', 'eval_interval'
];
// Map backend field names to frontend field names
const fieldNameMap = {
'activation': 'act', // Backend uses 'activation', frontend uses 'act'
'nms_thre': 'nmsthre'
};
// Function to load base config for selected model
function loadBaseConfig(modelName) {
if (!modelName) return Promise.resolve(null);
return fetch(`/api/base-config/${modelName}`)
.then(res => {
if (!res.ok) throw new Error('Base config not found');
return res.json();
})
.catch(err => {
console.warn(`Could not load base config for ${modelName}:`, err);
return null;
});
}
// Function to apply base config to form fields
function applyBaseConfig(config, isCocoMode) {
const infoBanner = document.getElementById('base-config-info');
const modelNameSpan = document.getElementById('base-config-model');
if (!config || !isCocoMode) {
// Hide info banner
if (infoBanner) infoBanner.style.display = 'none';
// Remove grey styling and enable all fields
protectedFields.forEach(fieldName => {
const input = form.querySelector(`[name="${fieldName}"]`);
if (input) {
input.disabled = false;
input.style.backgroundColor = '#f8f8f8';
input.style.color = '#333';
input.style.cursor = 'text';
input.title = '';
}
});
baseConfigFields = [];
return;
}
// Show info banner
if (infoBanner) {
infoBanner.style.display = 'block';
const modelName = form.querySelector('[name="select_model"]')?.value || 'selected model';
if (modelNameSpan) modelNameSpan.textContent = modelName;
}
// Apply base config values and grey out fields
baseConfigFields = [];
Object.entries(config).forEach(([key, value]) => {
// Map backend field name to frontend field name if needed
const frontendFieldName = fieldNameMap[key] || key;
if (protectedFields.includes(frontendFieldName)) {
const input = form.querySelector(`[name="${frontendFieldName}"]`);
if (input) {
baseConfigFields.push(frontendFieldName);
// Set value based on type
if (input.type === 'checkbox') {
input.checked = Boolean(value);
} else if (Array.isArray(value)) {
input.value = value.join(',');
} else {
input.value = value;
}
// Grey out and disable
input.disabled = true;
input.style.backgroundColor = '#d3d3d3';
input.style.color = '#666';
input.style.cursor = 'not-allowed';
// Add title tooltip
const modelName = form.querySelector('[name="select_model"]')?.value || 'selected model';
input.title = `Protected by base config for ${modelName}. Switch to "Train from sketch" to customize.`;
}
}
});
console.log(`Applied base config. Protected fields: ${baseConfigFields.join(', ')}`);
}
// Function to update form based on transfer learning mode
function updateTransferLearningMode() {
const transferLearning = document.getElementById('transfer-learning');
const selectModel = document.getElementById('select-model');
const selectModelRow = document.getElementById('select-model-row');
if (!transferLearning || !selectModel) return;
const isCocoMode = transferLearning.value === 'coco';
const isCustomMode = transferLearning.value === 'custom';
const isSketchMode = transferLearning.value === 'sketch';
const modelName = selectModel.value;
// Show/hide select model based on transfer learning mode
if (selectModelRow) {
if (isSketchMode) {
selectModelRow.style.display = 'none';
} else {
selectModelRow.style.display = '';
}
}
if (isCocoMode && modelName) {
// Load and apply base config
loadBaseConfig(modelName).then(config => {
currentBaseConfig = config;
applyBaseConfig(config, true);
});
} else {
// Clear base config
currentBaseConfig = null;
applyBaseConfig(null, false);
}
}
// Listen for changes to transfer learning dropdown
const transferLearningSelect = document.getElementById('transfer-learning');
if (transferLearningSelect) {
transferLearningSelect.addEventListener('change', updateTransferLearningMode);
}
// Listen for changes to model selection
const modelSelect = document.getElementById('select-model');
if (modelSelect) {
modelSelect.addEventListener('change', updateTransferLearningMode);
}
// Initial update on page load
setTimeout(updateTransferLearningMode, 100);
// Auto-set num_classes from training_project classes array
const urlParams = new URLSearchParams(window.location.search);
@@ -43,17 +193,26 @@ window.addEventListener('DOMContentLoaded', () => {
form.addEventListener('submit', function(e) {
console.log("Form submitted");
e.preventDefault();
// Temporarily enable disabled fields so they get included in FormData
const disabledInputs = [];
form.querySelectorAll('input[disabled], select[disabled]').forEach(input => {
input.disabled = false;
disabledInputs.push(input);
});
const formData = new FormData(form);
const settings = {};
let fileToUpload = null;
for (const [key, value] of formData.entries()) {
if (key === 'model_upload' && form.elements[key].files.length > 0) {
fileToUpload = form.elements[key].files[0];
continue;
}
if (key === 'ema' || key === 'enable_mixup') {
if (key === 'ema' || key === 'enable_mixup' || key === 'save_history_ckpt') {
settings[key] = form.elements[key].checked;
} else if (key === 'scale' || key === 'mosaic_scale') {
} else if (key === 'scale' || key === 'mosaic_scale' || key === 'mixup_scale' || key === 'input_size' || key === 'test_size') {
settings[key] = value.split(',').map(v => parseFloat(v.trim()));
} else if (!isNaN(value) && value !== '') {
settings[key] = parseFloat(value);
@@ -61,6 +220,12 @@ window.addEventListener('DOMContentLoaded', () => {
settings[key] = value;
}
}
// Re-disable the inputs
disabledInputs.forEach(input => {
input.disabled = true;
});
// Attach project id from URL
const urlParams = new URLSearchParams(window.location.search);
const projectId = urlParams.get('id');