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Refactor logging and formatting

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Improved code readability
This commit is contained in:
Dariusz L
2025-06-27 05:32:11 +02:00
parent be4fae2964
commit 83ce890ef4
16 changed files with 241 additions and 363 deletions
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90
canvas_node.py
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@@ -27,33 +27,46 @@ sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'python
# Importuj logger
try:
from python.logger import logger, LogLevel, debug, info, warn, error, exception
# Konfiguracja loggera dla modułu canvas_node
logger.set_module_level('canvas_node', LogLevel.INFO) # Domyślnie INFO, można zmienić na DEBUG
# Włącz logowanie do pliku
logger.configure({
'log_to_file': True,
'log_dir': os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs')
})
# Funkcje pomocnicze dla modułu
log_debug = lambda *args, **kwargs: debug('canvas_node', *args, **kwargs)
log_info = lambda *args, **kwargs: info('canvas_node', *args, **kwargs)
log_warn = lambda *args, **kwargs: warn('canvas_node', *args, **kwargs)
log_error = lambda *args, **kwargs: error('canvas_node', *args, **kwargs)
log_exception = lambda *args: exception('canvas_node', *args)
log_info("Logger initialized for canvas_node")
except ImportError as e:
# Fallback jeśli logger nie jest dostępny
print(f"Warning: Logger module not available: {e}")
# Proste funkcje zastępcze
def log_debug(*args): print("[DEBUG]", *args)
def log_info(*args): print("[INFO]", *args)
def log_warn(*args): print("[WARN]", *args)
def log_error(*args): print("[ERROR]", *args)
def log_debug(*args):
print("[DEBUG]", *args)
def log_info(*args):
print("[INFO]", *args)
def log_warn(*args):
print("[WARN]", *args)
def log_error(*args):
print("[ERROR]", *args)
def log_exception(*args):
print("[ERROR]", *args)
traceback.print_exc()
@@ -95,7 +108,7 @@ class BiRefNet(torch.nn.Module):
class CanvasNode:
_canvas_data_storage = {}
_storage_lock = threading.Lock()
_canvas_cache = {
'image': None,
'mask': None,
@@ -104,7 +117,7 @@ class CanvasNode:
'persistent_cache': {},
'last_execution_id': None
}
# Simple in-memory storage for canvas data, keyed by prompt_id
# WebSocket-based storage for canvas data per node
_websocket_data = {}
@@ -247,11 +260,13 @@ class CanvasNode:
# Zmienna blokująca równoczesne wykonania
_processing_lock = threading.Lock()
def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None, input_image=None,
def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None,
input_image=None,
input_mask=None):
log_info(f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
log_info(
f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
try:
# Sprawdź czy już trwa przetwarzanie
if not self.__class__._processing_lock.acquire(blocking=False):
@@ -259,11 +274,12 @@ class CanvasNode:
# Return cached data if available to avoid breaking the flow
return self.get_cached_data()
log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
log_info(
f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
# Use node_id as the primary key, as unique_id is proving unreliable
storage_key = node_id
processed_image = None
processed_mask = None
@@ -296,7 +312,6 @@ class CanvasNode:
log_info("Using provided input_mask as fallback")
processed_mask = input_mask
# Fallback to default tensors if nothing is loaded
if processed_image is None:
log_warn(f"Processed image is still None, creating default blank image.")
@@ -305,22 +320,22 @@ class CanvasNode:
log_warn(f"Processed mask is still None, creating default blank mask.")
processed_mask = torch.zeros((1, 512, 512), dtype=torch.float32)
if not output_switch:
log_debug(f"Output switch is OFF, returning empty tuple")
return (None, None)
log_debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
log_debug(
f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
self.update_persistent_cache()
log_info(f"Successfully returning processed image and mask")
return (processed_image, processed_mask)
except Exception as e:
log_exception(f"Error in process_canvas_image: {str(e)}")
return (None, None)
finally:
# Zwolnij blokadę
if self.__class__._processing_lock.locked():
@@ -373,26 +388,26 @@ class CanvasNode:
try:
current_time = time.time()
cleanup_threshold = 300 # 5 minutes
nodes_to_remove = []
for node_id, data in cls._websocket_data.items():
# Remove invalid node IDs
if node_id < 0:
nodes_to_remove.append(node_id)
continue
# Remove old data
if current_time - data.get('timestamp', 0) > cleanup_threshold:
nodes_to_remove.append(node_id)
continue
for node_id in nodes_to_remove:
del cls._websocket_data[node_id]
log_debug(f"Cleaned up old WebSocket data for node {node_id}")
if nodes_to_remove:
log_info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
except Exception as e:
log_error(f"Error during WebSocket cleanup: {str(e)}")
@@ -402,7 +417,7 @@ class CanvasNode:
async def handle_canvas_websocket(request):
ws = web.WebSocketResponse()
await ws.prepare(request)
async for msg in ws:
if msg.type == web.WSMsgType.TEXT:
try:
@@ -411,17 +426,17 @@ class CanvasNode:
if not node_id:
await ws.send_json({'status': 'error', 'message': 'nodeId is required'})
continue
image_data = data.get('image')
mask_data = data.get('mask')
with cls._storage_lock:
cls._canvas_data_storage[node_id] = {
'image': image_data,
'mask': mask_data,
'timestamp': time.time()
}
log_info(f"Received canvas data for node {node_id} via WebSocket")
# Send acknowledgment back to the client
ack_payload = {
@@ -431,7 +446,7 @@ class CanvasNode:
}
await ws.send_json(ack_payload)
log_debug(f"Sent ACK for node {node_id}")
except Exception as e:
log_error(f"Error processing WebSocket message: {e}")
await ws.send_json({'status': 'error', 'message': str(e)})
@@ -679,10 +694,11 @@ class BiRefNetMatting:
# Zmienna blokująca równoczesne wywołania matting
_matting_lock = None
@PromptServer.instance.routes.post("/matting")
async def matting(request):
global _matting_lock
# Sprawdź czy już trwa przetwarzanie
if _matting_lock is not None:
log_warn("Matting already in progress, rejecting request")
@@ -690,10 +706,10 @@ async def matting(request):
"error": "Another matting operation is in progress",
"details": "Please wait for the current operation to complete"
}, status=429) # 429 Too Many Requests
# Ustaw blokadę
_matting_lock = True
try:
log_info("Received matting request")
data = await request.json()