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improve schema

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This commit is contained in:
russian_proger
2026-04-04 22:57:41 +03:00
parent b2cc714d79
commit ec8b3ae20e
9 changed files with 199 additions and 146 deletions
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3
models/SiaN/.gitignore vendored
View File

@@ -1,2 +1,3 @@
runs
*.gen.py
*.gen.py
*.img

20
models/SiaN/_schema.md
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@@ -1,4 +1,8 @@
# _schema.md
# SiaN Schema
Notebook structure definition for SiaN model.
---
## Format
@@ -10,14 +14,18 @@
# markdown
"""Description"""
# inline:
<custom code>
# shell:
<shell commands>
```
## Directives
- `# code:` - include file from src/
- `# markdown` - description block
- `# inline:` - custom code cell
| Directive | Description |
|----------------|------------------------------------|
| `# code:` | Include file from src/ |
| `# markdown` | Description block |
| `# shell:` | Shell script cell |
---
`build.py` generates notebook from `_schema.py`.

77
models/SiaN/_schema.py
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@@ -20,28 +20,87 @@ from tqdm import tqdm
# code: ./src/utils.py
# markdown
"""# SiaN Model"""
"""# Configuration
Global settings for:
- Data paths and image parameters
- Training hyperparameters
- Model architecture options
Contains the `config` dictionary used across all modules."""
# code: ./src/dataloader.py
# markdown
"""Dataset for Google/Yandex image pairs with homography augmentation."""
"""## Dataset
Google/Yandex image pair loader with homography augmentation.
**Features:**
- Loads paired images from dual camera sources
- Applies random homography transformations
- Supports configurable train/val split
**Returns:**
- Batch dict with `google_img`, `yandex_img`, `homography_params`"""
# code: ./src/model.py
# markdown
"""HomographyCNN6 predicts 6 params: rx, ry, rz, tx, ty, scale."""
"""## Model
`HomographyCNN6` — CNN architecture for homography estimation.
**Output:** 6 parameters
- `rx, ry, rz` — rotation angles (radians)
- `tx, ty` — translation offsets
- `scale` — isotropic scale factor
**Architecture:**
- Dual-branch CNN (Google + Yandex images)
- Shared backbone (configurable: resnet18/34/50)
- Fusion head with dropout regularization"""
# code: ./src/train.py
# markdown
"""HomographyTrainer manages training loop with validation."""
"""## Training
`HomographyTrainer` — training loop with validation and checkpointing.
**Features:**
- Epoch-based training with tqdm progress bar
- Adam optimizer with configurable LR
- Validation after each epoch
- Best model auto-save
- Periodic checkpoints (every N epochs via `save_every_n_epochs`)
**Checkpoint saving:**
- `best_model.pt` — lowest validation loss
- `checkpoint_epoch_N.pt` — periodic saves"""
# code: ./src/analyze.py
# markdown
"""Visualization and analysis of predictions."""
"""## Analysis
Visualization and evaluation tools:
- Training metrics plots (loss curves)
- Prediction visualization on sample images
- Error analysis and statistics"""
# code: ./src/main.py
# markdown
"""Run main() to execute the full pipeline."""
"""## Main Pipeline
# inline:
if __name__ == "__main__":
main()
Executes the full training workflow:
1. Load dataset info
2. Create data loaders
3. Initialize model
4. Train with validation
5. Analyze and export results
**Outputs:**
- Model checkpoints in `runs/checkpoints/`
- TensorBoard logs in `runs/`
- Analysis plots"""
# # shell:
# !zip artefacts.zip runs/gan_training/checkpoints/best_model.pt

39
models/SiaN/build.py
View File

@@ -42,23 +42,34 @@ def parse_schema(schema_path):
i += 1
continue
if stripped.startswith('# inline:'):
if stripped.startswith('# inline:') or stripped.startswith('# # shell:'):
directive = 'inline' if stripped.startswith('# inline:') else 'shell'
i += 1
code_lines = []
while i < len(lines):
stripped = lines[i].strip()
if stripped.startswith('#'):
if stripped.startswith('# code:') or stripped.startswith('# inline:') or stripped == '# markdown' or stripped.startswith('# ==='):
break
i += 1
continue
if directive == 'shell':
if stripped.startswith('# ') or stripped.startswith('# # '):
line_content = lines[i].strip()
if line_content.startswith('# # '):
line_content = line_content[3:].lstrip()
else:
line_content = line_content[2:]
code_lines.append(line_content)
i += 1
continue
if stripped.startswith('#'):
if stripped.startswith('# code:') or stripped.startswith('# inline:') or stripped.startswith('# # shell:') or stripped == '# markdown' or stripped.startswith('# ==='):
break
i += 1
continue
if stripped == '':
i += 1
continue
code_lines.append(lines[i])
i += 1
if code_lines:
items.append(('inline', '\n'.join(code_lines).rstrip()))
items.append((directive, '\n'.join(code_lines).rstrip()))
continue
if stripped == '# markdown':
@@ -180,6 +191,20 @@ def build_notebook(schema_path, output_path=None):
"source": [line + "\n" for line in lines]
}
cells.append(cell)
elif item_type == 'shell':
lines = item_content.split('\n')
while lines and lines[-1].strip() == '':
lines.pop()
if lines:
lines.append('')
cell = {
"cell_type": "code",
"execution_count": None,
"metadata": {},
"outputs": [],
"source": [line + "\n" for line in lines]
}
cells.append(cell)
elif item_type == 'code':
try:
content = read_src_file(item_content, schema_dir)

107
models/SiaN/notebook.gen.ipynb
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@@ -42,6 +42,7 @@
" \"dropout_rate\": 0.3,\n",
" \"backbone\": \"resnet18\",\n",
" \"output_dir\": r\"C:\\Users\\admin\\Projects\\autopilot\\models\\SiaN\\runs\",\n",
" \"save_every_n_epochs\": 15,\n",
"}\n",
"\n",
"\n",
@@ -88,7 +89,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": "# SiaN Model\n"
"source": "# Configuration\n\nGlobal settings for:\n- Data paths and image parameters\n- Training hyperparameters\n- Model architecture options\n"
},
{
"cell_type": "code",
@@ -185,7 +186,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": "Dataset for Google/Yandex image pairs with homography augmentation.\n"
"source": "## Dataset\n\nGoogle/Yandex image pair loader with homography augmentation.\n\n**Features:**\n- Loads paired images from dual camera sources\n- Applies random homography transformations\n- Supports configurable train/val split\n\n**Returns:**\n"
},
{
"cell_type": "code",
@@ -237,7 +238,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": "HomographyCNN6 predicts 6 params: rx, ry, rz, tx, ty, scale.\n"
"source": "## Model\n\n`HomographyCNN6` — CNN architecture for homography estimation.\n\n**Output:** 6 parameters\n- `rx, ry, rz` — rotation angles (radians)\n- `tx, ty` — translation offsets\n- `scale` — isotropic scale factor\n\n**Architecture:**\n- Dual-branch CNN (Google + Yandex images)\n- Shared backbone (configurable: resnet18/34/50)\n"
},
{
"cell_type": "code",
@@ -310,6 +311,10 @@
" self.save_checkpoint(epoch, is_best=True)\n",
" print(f\"Best model saved (val loss: {val_metrics['loss']:.4f})\")\n",
"\n",
" if epoch % config[\"save_every_n_epochs\"] == 0:\n",
" self.save_checkpoint(epoch, is_best=False)\n",
" print(f\"Checkpoint saved at epoch {epoch}\")\n",
"\n",
" self.writer.close()\n",
"\n",
" def save_checkpoint(self, epoch, is_best=False):\n",
@@ -325,7 +330,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": "HomographyTrainer manages training loop with validation.\n"
"source": "## Training\n\n`HomographyTrainer` — training loop with validation and checkpointing.\n\n**Features:**\n- Epoch-based training with tqdm progress bar\n- Adam optimizer with configurable LR\n- Validation after each epoch\n- Best model auto-save\n- Periodic checkpoints (every N epochs via `save_every_n_epochs`)\n\n**Checkpoint saving:**\n- `best_model.pt` — lowest validation loss\n"
},
{
"cell_type": "code",
@@ -388,7 +393,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": "Visualization and analysis of predictions.\n"
"source": "## Analysis\n\nVisualization and evaluation tools:\n\n- Training metrics plots (loss curves)\n- Prediction visualization on sample images\n"
},
{
"cell_type": "code",
@@ -403,75 +408,52 @@
"logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(message)s\")\n",
"logger = logging.getLogger(__name__)\n",
"\n",
"logger.info(\"=\" * 50)\n",
"logger.info(\"SiaN Training Pipeline\")\n",
"logger.info(\"=\" * 50)\n",
"\n",
"def create_dataset():\n",
" logger.info(\"Creating data loaders...\")\n",
" train_loader, val_loader = create_data_loaders(\n",
" root_dir=config[\"data_dir\"],\n",
" batch_size=config[\"batch_size\"],\n",
" train_split=config[\"train_split\"],\n",
" num_workers=config[\"num_workers\"],\n",
" image_size=config[\"image_size\"],\n",
" )\n",
" logger.info(f\"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}\")\n",
" return train_loader, val_loader\n",
"dataset_info = get_dataset_info()\n",
"logger.info(f\"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}\")\n",
"\n",
"train_loader, val_loader = create_data_loaders(\n",
" root_dir=config[\"data_dir\"],\n",
" batch_size=config[\"batch_size\"],\n",
" train_split=config[\"train_split\"],\n",
" num_workers=config[\"num_workers\"],\n",
" image_size=config[\"image_size\"],\n",
")\n",
"logger.info(f\"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}\")\n",
"\n",
"def create_model():\n",
" logger.info(\"Creating model...\")\n",
" model = HomographyCNN6(\n",
" input_channels=3,\n",
" backbone_name=config[\"backbone\"],\n",
" pretrained=True,\n",
" dropout_rate=config[\"dropout_rate\"]\n",
" )\n",
" logger.info(f\"Model created with {count_parameters(model):,} parameters\")\n",
" return model\n",
"model = HomographyCNN6(\n",
" input_channels=3,\n",
" backbone_name=config[\"backbone\"],\n",
" pretrained=True,\n",
" dropout_rate=config[\"dropout_rate\"]\n",
")\n",
"logger.info(f\"Model created with {count_parameters(model):,} parameters\")\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"logger.info(f\"Using device: {device}\")\n",
"\n",
"def train_model(model, train_loader, val_loader):\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" logger.info(f\"Using device: {device}\")\n",
" \n",
" trainer = HomographyTrainer(model, train_loader, val_loader, device)\n",
" logger.info(\"Starting training...\")\n",
" trainer.train(config[\"epochs\"])\n",
" logger.info(\"Training completed\")\n",
" return trainer\n",
"trainer = HomographyTrainer(model, train_loader, val_loader, device)\n",
"logger.info(\"Starting training...\")\n",
"trainer.train(config[\"epochs\"])\n",
"logger.info(\"Training completed\")\n",
"\n",
"logger.info(\"Analyzing model...\")\n",
"results = analyze_training(trainer)\n",
"logger.info(f\"Analysis complete: best_val_loss={results['best_val_loss']:.4f}\")\n",
"\n",
"def analyze_model(trainer):\n",
" logger.info(\"Analyzing model...\")\n",
" results = analyze_training(trainer)\n",
" logger.info(f\"Analysis complete: best_val_loss={results['best_val_loss']:.4f}\")\n",
" return results\n",
"\n",
"\n",
"def main():\n",
" logger.info(\"=\" * 50)\n",
" logger.info(\"SiaN Training Pipeline\")\n",
" logger.info(\"=\" * 50)\n",
" \n",
" dataset_info = get_dataset_info()\n",
" logger.info(f\"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}\")\n",
" \n",
" train_loader, val_loader = create_dataset()\n",
" model = create_model()\n",
" trainer = train_model(model, train_loader, val_loader)\n",
" results = analyze_model(trainer)\n",
" \n",
" logger.info(\"=\" * 50)\n",
" logger.info(\"Pipeline completed successfully\")\n",
" logger.info(\"=\" * 50)\n",
" \n",
" return trainer, results\n",
"logger.info(\"=\" * 50)\n",
"logger.info(\"Pipeline completed successfully\")\n",
"logger.info(\"=\" * 50)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Run main() to execute the full pipeline.\n"
"source": "## Main Pipeline\n\nExecutes the full training workflow:\n1. Load dataset info\n2. Create data loaders\n3. Initialize model\n4. Train with validation\n5. Analyze and export results\n\n**Outputs:**\n- Model checkpoints in `runs/checkpoints/`\n- TensorBoard logs in `runs/`\n"
},
{
"cell_type": "code",
@@ -479,8 +461,7 @@
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" main()\n",
"!zip artefacts.zip runs/gan_training/checkpoints/best_model.pt\n",
"\n"
]
}

2
models/SiaN/src/dataloader.py
View File

@@ -78,7 +78,7 @@ def create_data_loaders(root_dir, batch_size=32, train_split=0.8, num_workers=0,
split = int(train_split * len(indices))
train_ds = Subset(aug_ds if augment_train else full_ds, indices[:split])
val_ds = Subset(full_ds, indices[split:])
val_ds = Subset(aug_ds, indices[split:])
return (DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True),
DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True))

92
models/SiaN/src/main.py
View File

@@ -1,4 +1,5 @@
import logging
import os
import torch
@@ -12,69 +13,42 @@ from .utils import config
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(message)s")
logger = logging.getLogger(__name__)
logger.info("=" * 50)
logger.info("SiaN Training Pipeline")
logger.info("=" * 50)
def create_dataset():
logger.info("Creating data loaders...")
train_loader, val_loader = create_data_loaders(
root_dir=config["data_dir"],
batch_size=config["batch_size"],
train_split=config["train_split"],
num_workers=config["num_workers"],
image_size=config["image_size"],
)
logger.info(f"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}")
return train_loader, val_loader
dataset_info = get_dataset_info()
logger.info(f"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}")
train_loader, val_loader = create_data_loaders(
root_dir=config["data_dir"],
batch_size=config["batch_size"],
train_split=config["train_split"],
num_workers=config["num_workers"],
image_size=config["image_size"],
)
logger.info(f"Data loaders created: train={len(train_loader.dataset)}, val={len(val_loader.dataset)}")
def create_model():
logger.info("Creating model...")
model = HomographyCNN6(
input_channels=3,
backbone_name=config["backbone"],
pretrained=True,
dropout_rate=config["dropout_rate"]
)
logger.info(f"Model created with {count_parameters(model):,} parameters")
return model
model = HomographyCNN6(
input_channels=3,
backbone_name=config["backbone"],
pretrained=True,
dropout_rate=config["dropout_rate"]
)
logger.info(f"Model created with {count_parameters(model):,} parameters")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")
def train_model(model, train_loader, val_loader):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")
trainer = HomographyTrainer(model, train_loader, val_loader, device)
logger.info("Starting training...")
trainer.train(config["epochs"])
logger.info("Training completed")
return trainer
trainer = HomographyTrainer(model, train_loader, val_loader, device)
logger.info("Starting training...")
trainer.train(config["epochs"])
logger.info("Training completed")
logger.info("Analyzing model...")
results = analyze_training(trainer)
logger.info(f"Analysis complete: best_val_loss={results['best_val_loss']:.4f}")
def analyze_model(trainer):
logger.info("Analyzing model...")
results = analyze_training(trainer)
logger.info(f"Analysis complete: best_val_loss={results['best_val_loss']:.4f}")
return results
def main():
logger.info("=" * 50)
logger.info("SiaN Training Pipeline")
logger.info("=" * 50)
dataset_info = get_dataset_info()
logger.info(f"Dataset: {dataset_info['size']} samples, keys={dataset_info['sample_keys']}")
train_loader, val_loader = create_dataset()
model = create_model()
trainer = train_model(model, train_loader, val_loader)
results = analyze_model(trainer)
logger.info("=" * 50)
logger.info("Pipeline completed successfully")
logger.info("=" * 50)
return trainer, results
if __name__ == "__main__":
main()
logger.info("=" * 50)
logger.info("Pipeline completed successfully")
logger.info("=" * 50)

4
models/SiaN/src/train.py
View File

@@ -72,6 +72,10 @@ class HomographyTrainer:
self.save_checkpoint(epoch, is_best=True)
print(f"Best model saved (val loss: {val_metrics['loss']:.4f})")
if epoch % config["save_every_n_epochs"] == 0:
self.save_checkpoint(epoch, is_best=False)
print(f"Checkpoint saved at epoch {epoch}")
self.writer.close()
def save_checkpoint(self, epoch, is_best=False):

1
models/SiaN/src/utils.py
View File

@@ -12,6 +12,7 @@ config = {
"dropout_rate": 0.3,
"backbone": "resnet18",
"output_dir": r"C:\Users\admin\Projects\autopilot\models\SiaN\runs",
"save_every_n_epochs": 15,
}