chore: project optimization and code quality improvements

.gitignore

@@ -83,6 +83,9 @@ continue.md
 .uv/
 .analysis_archive/
 
+# Claude Code
+.claude/
+
 # Rust
 med_core_rs/target/
 med_core_rs/Cargo.lock

.nvmrc

@@ -0,0 +1 @@
+22

docs/conf.py

@@ -12,25 +12,25 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'MedFusion'
-copyright = '2024-2026, MedFusion Team'
-author = 'MedFusion Team'
-release = '0.2.0'
+project = "MedFusion"
+copyright = "2024-2026, MedFusion Team"
+author = "MedFusion Team"
+release = "0.2.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.githubpages',
-    'myst_parser',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
+    "sphinx.ext.coverage",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.githubpages",
+    "myst_parser",
 ]
 
 # Napoleon settings
@@ -51,14 +51,14 @@
 
 # Autodoc settings
 autodoc_default_options = {
-    'members': True,
-    'member-order': 'bysource',
-    'special-members': '__init__',
-    'undoc-members': True,
-    'exclude-members': '__weakref__'
+    "members": True,
+    "member-order": "bysource",
+    "special-members": "__init__",
+    "undoc-members": True,
+    "exclude-members": "__weakref__",
 }
-autodoc_typehints = 'description'
-autodoc_typehints_description_target = 'documented'
+autodoc_typehints = "description"
+autodoc_typehints_description_target = "documented"
 
 # Autosummary settings
 autosummary_generate = True
@@ -80,14 +80,14 @@
     "tasklist",
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'furo'
-html_static_path = ['_static']
+html_theme = "furo"
+html_static_path = ["_static"]
 
 html_theme_options = {
     "light_css_variables": {
@@ -112,9 +112,9 @@
 
 # Intersphinx mapping
 intersphinx_mapping = {
-    'python': ('https://docs.python.org/3', None),
-    'torch': ('https://pytorch.org/docs/stable/', None),
-    'numpy': ('https://numpy.org/doc/stable/', None),
+    "python": ("https://docs.python.org/3", None),
+    "torch": ("https://pytorch.org/docs/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable/", None),
 }
 
 # Todo extension

examples/advanced_attention_demo.py

@@ -181,7 +181,7 @@ def demo_factory_function():
         print(f"  • {attn_type:12s}: {x.shape} -> {out.shape}")
 
     print("\n使用示例:")
-    code = '''
+    code = """
 from med_core.attention_supervision import create_attention_module
 
 # 创建注意力模块
@@ -202,7 +202,7 @@ def forward(self, x):
         x = self.conv(x)
         x = self.attention(x)  # 应用注意力
         return x
-'''
+"""
     print(code)
 
 
@@ -267,7 +267,7 @@ def demo_integration_example():
     print("=" * 60)
 
     print("\n完整的模型集成示例:")
-    code = '''
+    code = """
 import torch.nn as nn
 from med_core.attention_supervision import (
     SEAttention,
@@ -349,7 +349,7 @@ def compute_loss(self, x, y):
 loss, loss_dict = model.compute_loss(x, y)
 print(f"Total loss: {loss.item():.4f}")
 print(f"Loss components: {loss_dict}")
-'''
+"""
     print(code)
 
 
@@ -403,6 +403,7 @@ def main():
     except Exception as e:
         print(f"\n❌ 错误: {e}")
         import traceback
+
         traceback.print_exc()
 
 

examples/benchmark_demo.py

@@ -35,7 +35,7 @@ def fast_function():
         slow_function()
     slow_time = time.time() - start
     print(f"   耗时: {slow_time:.3f}s")
-    print(f"   吞吐量: {100/slow_time:.1f} ops/s")
+    print(f"   吞吐量: {100 / slow_time:.1f} ops/s")
 
     # 测试快速实现
     print("\n2. 测试快速实现:")
@@ -44,7 +44,7 @@ def fast_function():
         fast_function()
     fast_time = time.time() - start
     print(f"   耗时: {fast_time:.3f}s")
-    print(f"   吞吐量: {100/fast_time:.1f} ops/s")
+    print(f"   吞吐量: {100 / fast_time:.1f} ops/s")
 
     # 比较
     speedup = slow_time / fast_time
@@ -65,7 +65,7 @@ def demo_benchmark_suite():
     print("  • 检测性能回归")
 
     print("\n使用示例:")
-    code = '''
+    code = """
 from med_core.utils.benchmark import BenchmarkSuite, PerformanceBenchmark
 
 # 1. 创建测试套件
@@ -86,7 +86,7 @@ def test_model_inference():
 
 # 5. 与基线比较
 suite.compare_with("baseline.json")
-'''
+"""
     print(code)
 
 
@@ -204,7 +204,7 @@ def demo_ci_integration():
     print("=" * 60)
 
     print("\nGitHub Actions 示例:")
-    yaml = '''
+    yaml = """
 name: Performance Benchmarks
 
 on: [push, pull_request]
@@ -239,7 +239,7 @@ def demo_ci_integration():
         with:
           name: benchmark-results
           path: benchmarks/
-'''
+"""
     print(yaml)
 
 
@@ -289,6 +289,7 @@ def main():
     except Exception as e:
         print(f"\n❌ 错误: {e}")
         import traceback
+
         traceback.print_exc()
 
 

examples/cache_demo.py

@@ -116,7 +116,7 @@ def demo_usage_example():
     print("实际使用示例")
     print("=" * 60)
 
-    code = '''
+    code = """
 # 1. 创建原始数据集
 from med_core.datasets import MedicalDataset
 
@@ -156,7 +156,7 @@ def demo_usage_example():
     if hasattr(cached_dataset, 'get_cache_stats'):
         stats = cached_dataset.get_cache_stats()
         print(f"缓存命中率: {stats['hit_rate']:.2%}")
-'''
+"""
 
     print("\n代码示例:")
     print(code)
@@ -193,6 +193,7 @@ def main():
     except Exception as e:
         print(f"\n错误: {e}")
         import traceback
+
         traceback.print_exc()
 
 

examples/cache_demo_simple.py

@@ -66,11 +66,11 @@ def demo_lru_cache():
 
     # 获取数据
     print("\n2. 从缓存获取数据:")
-    result1 = cache.get('image_001')
+    result1 = cache.get("image_001")
     print(f"   image_001: {result1} (命中)")
-    result2 = cache.get('image_002')
+    result2 = cache.get("image_002")
     print(f"   image_002: {result2} (命中)")
-    result3 = cache.get('image_999')
+    result3 = cache.get("image_999")
     print(f"   image_999: {result3} (未命中)")
 
     # 添加新数据（触发淘汰）
@@ -84,7 +84,7 @@ def demo_lru_cache():
     print("\n4. 缓存统计:")
     stats = cache.get_stats()
     for key, value in stats.items():
-        if key == 'hit_rate':
+        if key == "hit_rate":
             print(f"   {key}: {value:.2%}")
         else:
             print(f"   {key}: {value}")
@@ -126,7 +126,7 @@ def slow_load_data(idx):
     if time_with_cache > 0:
         speedup = time_no_cache / time_with_cache
         print(f"\n3. 加速比: {speedup:.1f}x")
-        print(f"   性能提升: {(1 - time_with_cache/time_no_cache) * 100:.1f}%")
+        print(f"   性能提升: {(1 - time_with_cache / time_no_cache) * 100:.1f}%")
 
 
 def demo_access_patterns():
@@ -185,7 +185,7 @@ def demo_cache_size_impact():
             cache.get(idx)
 
         stats = cache.get_stats()
-        hit_rate = stats['hit_rate']
+        hit_rate = stats["hit_rate"]
 
         if hit_rate < 0.3:
             desc = "太小，效果差"
@@ -264,6 +264,7 @@ def main():
     except Exception as e:
         print(f"\n❌ 错误: {e}")
         import traceback
+
         traceback.print_exc()
 
 

examples/distributed_training_demo.py

@@ -57,8 +57,10 @@ def train_epoch(model, dataloader, criterion, optimizer, device, rank):
         total += target.size(0)
 
         if batch_idx % 10 == 0:
-            print(f"Rank {rank}: Batch {batch_idx}/{len(dataloader)}, "
-                  f"Loss: {loss.item():.4f}")
+            print(
+                f"Rank {rank}: Batch {batch_idx}/{len(dataloader)}, "
+                f"Loss: {loss.item():.4f}"
+            )
 
     avg_loss = total_loss / len(dataloader)
     accuracy = 100.0 * correct / total
@@ -135,8 +137,8 @@ def train_ddp(args):
                 optimizer,
                 epoch + 1,
                 f"outputs/checkpoint_ddp_epoch_{epoch + 1}.pt",
-                loss=avg_metrics['loss'].item(),
-                accuracy=avg_metrics['accuracy'].item(),
+                loss=avg_metrics["loss"].item(),
+                accuracy=avg_metrics["accuracy"].item(),
             )
 
     # 清理
@@ -219,8 +221,8 @@ def train_fsdp(args):
                 optimizer,
                 epoch + 1,
                 f"outputs/checkpoint_fsdp_epoch_{epoch + 1}.pt",
-                loss=avg_metrics['loss'].item(),
-                accuracy=avg_metrics['accuracy'].item(),
+                loss=avg_metrics["loss"].item(),
+                accuracy=avg_metrics["accuracy"].item(),
             )
 
     # 清理
@@ -273,33 +275,40 @@ def main():
     parser = argparse.ArgumentParser(description="分布式训练示例")
 
     # 训练参数
-    parser.add_argument("--strategy", type=str, default="ddp",
-                        choices=["ddp", "fsdp"],
-                        help="分布式策略")
-    parser.add_argument("--backend", type=str, default="nccl",
-                        choices=["nccl", "gloo", "mpi"],
-                        help="分布式后端")
-    parser.add_argument("--epochs", type=int, default=5,
-                        help="训练轮数")
-    parser.add_argument("--batch_size", type=int, default=32,
-                        help="批次大小")
-    parser.add_argument("--lr", type=float, default=0.001,
-                        help="学习率")
-    parser.add_argument("--num_samples", type=int, default=1000,
-                        help="样本数量")
-    parser.add_argument("--save_interval", type=int, default=2,
-                        help="保存间隔")
+    parser.add_argument(
+        "--strategy",
+        type=str,
+        default="ddp",
+        choices=["ddp", "fsdp"],
+        help="分布式策略",
+    )
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="nccl",
+        choices=["nccl", "gloo", "mpi"],
+        help="分布式后端",
+    )
+    parser.add_argument("--epochs", type=int, default=5, help="训练轮数")
+    parser.add_argument("--batch_size", type=int, default=32, help="批次大小")
+    parser.add_argument("--lr", type=float, default=0.001, help="学习率")
+    parser.add_argument("--num_samples", type=int, default=1000, help="样本数量")
+    parser.add_argument("--save_interval", type=int, default=2, help="保存间隔")
 
     # FSDP 参数
-    parser.add_argument("--sharding_strategy", type=str, default="FULL_SHARD",
-                        choices=["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD"],
-                        help="FSDP 分片策略")
-    parser.add_argument("--min_num_params", type=int, default=1000,
-                        help="自动包装的最小参数数量")
+    parser.add_argument(
+        "--sharding_strategy",
+        type=str,
+        default="FULL_SHARD",
+        choices=["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD"],
+        help="FSDP 分片策略",
+    )
+    parser.add_argument(
+        "--min_num_params", type=int, default=1000, help="自动包装的最小参数数量"
+    )
 
     # 其他
-    parser.add_argument("--demo", action="store_true",
-                        help="显示使用示例")
+    parser.add_argument("--demo", action="store_true", help="显示使用示例")
 
     args = parser.parse_args()