删除rmbg调试日志
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@ -82,7 +82,6 @@ class RmbgService:
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self.model = self.model.to(self.device)
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if torch.cuda.is_available():
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self.model = self.model.half() # 转换为半精度
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logger.info("模型已使用半精度（FP16）加载，显存占用减少约50%")
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except Exception as e:
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# 如果半精度加载失败，降级到全精度
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logger.warning(f"半精度加载失败，使用全精度: {str(e)}")
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@ -99,7 +98,6 @@ class RmbgService:
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# 设置显存分配器，减少碎片化
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os.environ.setdefault('PYTORCH_CUDA_ALLOC_CONF', 'expandable_segments:True')
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torch.cuda.empty_cache()
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logger.info(f"模型加载完成，当前显存占用: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
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def _process_image_sync(self, image):
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"""同步处理图像，移除背景（单张）"""
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@ -242,12 +240,9 @@ class RmbgService:
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self.queue_running = True
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self.queue_task = asyncio.create_task(self._queue_processor())
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logger.info("队列批处理机制已启动")
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async def _queue_processor(self):
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"""后台队列批处理任务（核心逻辑）"""
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logger.info("队列批处理任务开始运行")
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while self.queue_running:
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try:
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# 收集一批请求
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@ -451,13 +446,6 @@ class RmbgService:
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batch_size = settings.batch_size
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loop = asyncio.get_event_loop()
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# 性能统计变量
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download_time = 0.0
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gpu_inference_time = 0.0
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save_time = 0.0
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batch_count = 0
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batch_sizes = []
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stats_printed = False
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# 流水线队列：收集已下载的图片
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download_queue = asyncio.Queue()
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@ -465,51 +453,6 @@ class RmbgService:
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download_done_count = 0
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download_error_count = 0
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def print_stats():
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"""输出性能统计信息"""
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nonlocal stats_printed
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if stats_printed:
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return
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stats_printed = True
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total_time = time.time() - batch_start_time
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other_time = total_time - download_time - gpu_inference_time - save_time
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logger.info("=" * 60)
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logger.info("📊 批处理性能统计（流水线模式）")
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logger.info("=" * 60)
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logger.info(f"图片总数: {total}")
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logger.info(f"成功数量: {success_count}")
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logger.info(f"失败数量: {error_count}")
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logger.info(f"批处理次数: {batch_count}")
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logger.info(f"每批图片数: {batch_sizes}")
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logger.info("-" * 60)
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logger.info("⏱️ 各阶段耗时:")
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if total_time > 0:
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download_pct = (download_time / total_time) * 100
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gpu_pct = (gpu_inference_time / total_time) * 100
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save_pct = (save_time / total_time) * 100
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other_pct = (other_time / total_time) * 100
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logger.info(f" 1. 下载图片: {download_time:.3f}s ({download_pct:.1f}%)")
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logger.info(f" 2. GPU推理: {gpu_inference_time:.3f}s ({gpu_pct:.1f}%)")
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logger.info(f" 3. 保存图片: {save_time:.3f}s ({save_pct:.1f}%)")
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logger.info(f" 4. 其他开销: {other_time:.3f}s ({other_pct:.1f}%)")
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else:
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logger.info(f" 1. 下载图片: {download_time:.3f}s")
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logger.info(f" 2. GPU推理: {gpu_inference_time:.3f}s")
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logger.info(f" 3. 保存图片: {save_time:.3f}s")
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logger.info(f" 4. 其他开销: {other_time:.3f}s")
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logger.info("-" * 60)
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logger.info(f"📈 总耗时: {total_time:.3f}s")
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if total > 0:
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avg_per_image = (total_time / total) * 1000
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logger.info(f"📈 平均每张: {avg_per_image:.1f}ms")
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if batch_count > 0:
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avg_batch_time = gpu_inference_time / batch_count
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logger.info(f"📈 每批平均耗时: {avg_batch_time:.3f}s")
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async def download_image_async(index, url):
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"""异步下载图片并放入队列"""
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@ -543,7 +486,6 @@ class RmbgService:
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download_complete.set()
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# 启动所有下载任务（并行下载）
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download_start_time = time.time()
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download_tasks = [
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asyncio.create_task(download_image_async(i, url))
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for i, url in enumerate(urls, 1)
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@ -558,7 +500,6 @@ class RmbgService:
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async def process_pending_batch(force=False):
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"""处理待处理的批次"""
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nonlocal pending_batch, completed_order, success_count, error_count
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nonlocal gpu_inference_time, save_time, batch_count, batch_sizes
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if not pending_batch:
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return
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@ -605,11 +546,7 @@ class RmbgService:
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# 尝试一次性处理所有图片
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images_with_info = [(img, size, idx) for img, size, idx, _ in valid_items]
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gpu_start_time = time.time()
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batch_results = await self.process_batch_images(images_with_info)
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gpu_inference_time += time.time() - gpu_start_time
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batch_count += 1
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batch_sizes.append(len(images_with_info))
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# 并行保存
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save_tasks = []
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@ -623,9 +560,7 @@ class RmbgService:
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)
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save_tasks.append((index, save_task))
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save_start_time = time.time()
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save_results = await asyncio.gather(*[task for _, task in save_tasks], return_exceptions=True)
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save_time += time.time() - save_start_time
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for (index, _), image_url in zip(save_tasks, save_results):
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if isinstance(image_url, Exception):
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@ -668,11 +603,7 @@ class RmbgService:
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images_with_info = [(img, size, idx) for img, size, idx, _ in batch_items]
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gpu_start_time = time.time()
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batch_results = await self.process_batch_images(images_with_info)
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gpu_inference_time += time.time() - gpu_start_time
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batch_count += 1
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batch_sizes.append(len(images_with_info))
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# 并行保存
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save_tasks = []
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@ -686,9 +617,7 @@ class RmbgService:
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)
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save_tasks.append((index, save_task))
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save_start_time = time.time()
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save_results = await asyncio.gather(*[task for _, task in save_tasks], return_exceptions=True)
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save_time += time.time() - save_start_time
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for (index, _), image_url in zip(save_tasks, save_results):
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if isinstance(image_url, Exception):
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@ -850,15 +779,11 @@ class RmbgService:
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# 等待所有下载任务完成
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await asyncio.gather(*download_tasks, return_exceptions=True)
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download_time = time.time() - download_start_time
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# 确保所有结果都已处理
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if pending_batch:
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async for result in process_pending_batch(force=True):
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yield result
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# 输出性能统计信息
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print_stats()
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def is_valid_url(self, url):
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"""验证URL是否有效"""
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@ -900,7 +825,6 @@ class RmbgService:
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await self.queue_task
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except asyncio.CancelledError:
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pass
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logger.info("队列批处理机制已停止")
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# 处理队列中剩余的请求
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remaining_items = []
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