File size: 2,644 Bytes
5b86813 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | ## Changes: Remove tqdm, Add Custom Pip-Style Progress Bar
### Summary
Removed dependency on `tqdm` library and implemented a custom pip-style training progress bar using only standard Python libraries (`sys`, `time`, `threading`).
### Files Modified
- [train_road_anomaly_model.py](train_road_anomaly_model.py#L1-L30)
### Changes Made
#### 1. Removed Imports (Line 1-30)
**Before:**
```python
from rich.progress import (...)
from tqdm import tqdm
```
**After:**
```python
import threading
import time
```
#### 2. Custom Progress Bar Implementation (Line 386-462)
Replaced tqdm-based progress rendering with custom `_render_progress()` function that:
- **Displays real-time progress** with visual bar:
```
training: 45.0%|█████████░░░░░░░░░░░░| 45/100 [00:30<00:40, 1.37 epoch/s] loss=0.3421 24.50img/s
```
- **Components**:
- Percentage: `45.0%`
- Visual progress bar: `█████████░░░░░░░░░░░░` (30 char width)
- Completed/Total: `45/100`
- Time: `[elapsed<remaining, rate]`
- Training stats: `loss` and `speed` (img/s)
- **Features**:
- ETA calculation based on per-epoch timing
- 500ms update interval (smooth without excessive output)
- Time formatting: `MM:SS` or `HH:MM:SS`
- Final progress line includes newline for clean finish
### Benefits
✅ **Zero external dependencies** - no tqdm, rich, or other progress bar libraries
✅ **Lightweight** - uses only standard library
✅ **Pip-style format** - familiar to Python developers
✅ **Real-time stats** - displays loss and speed alongside progress
✅ **ETA calculation** - estimates remaining time
✅ **Threading-safe** - background thread updates don't block training
### Example Output
```
training: 0.0%|░░░░░░░░░░░░░░░░░░░░░░░░░░░░| 0/100 [00:00<00:40, 0.00 epoch/s] loss=0.5000 24.50img/s
training: 10.0%|███░░░░░░░░░░░░░░░░░░░░░░░░░| 10/100 [00:10<00:35, 1.00 epoch/s] loss=0.4700 24.50img/s
training: 20.0%|██████░░░░░░░░░░░░░░░░░░░░░░| 20/100 [00:20<00:30, 1.00 epoch/s] loss=0.4400 24.50img/s
...
training:100.0%|██████████████████████████████| 100/100 [00:50<00:00, 2.00 epoch/s] loss=0.2000 24.50img/s
```
### Usage
No changes needed - progress bar appears automatically during training via background thread.
### Testing
Demo script created: `demo_progress_bar.py` demonstrates the progress bar output format.
|