diff --git a/.gitignore b/.gitignore index b4baf23..2200111 100644 --- a/.gitignore +++ b/.gitignore @@ -83,6 +83,9 @@ continue.md .uv/ .analysis_archive/ +# Claude Code +.claude/ + # Rust med_core_rs/target/ med_core_rs/Cargo.lock diff --git a/.nvmrc b/.nvmrc new file mode 100644 index 0000000..2bd5a0a --- /dev/null +++ b/.nvmrc @@ -0,0 +1 @@ +22 diff --git a/docs/conf.py b/docs/conf.py index 0dbd175..90aef7d 100644 --- a/docs/conf.py +++ b/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 diff --git a/examples/advanced_attention_demo.py b/examples/advanced_attention_demo.py index e48fedb..777e8bc 100644 --- a/examples/advanced_attention_demo.py +++ b/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() diff --git a/examples/benchmark_demo.py b/examples/benchmark_demo.py index 8750509..4d7cccc 100644 --- a/examples/benchmark_demo.py +++ b/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() diff --git a/examples/cache_demo.py b/examples/cache_demo.py index 253b78b..b966976 100644 --- a/examples/cache_demo.py +++ b/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() diff --git a/examples/cache_demo_simple.py b/examples/cache_demo_simple.py index a8aa5d1..8e75103 100644 --- a/examples/cache_demo_simple.py +++ b/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() diff --git a/examples/distributed_training_demo.py b/examples/distributed_training_demo.py index 8d01a8f..71b4faf 100644 --- a/examples/distributed_training_demo.py +++ b/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() diff --git a/examples/exception_handling_demo.py b/examples/exception_handling_demo.py index 0ad2cb9..0dd6247 100644 --- a/examples/exception_handling_demo.py +++ b/examples/exception_handling_demo.py @@ -27,8 +27,11 @@ def example_backbone_not_found(): try: available_backbones = [ - "resnet18", "resnet50", "efficientnet_b0", - "vit_b_16", "swin_t" + "resnet18", + "resnet50", + "efficientnet_b0", + "vit_b_16", + "swin_t", ] raise BackboneNotFoundError("resnet999", available_backbones) except BackboneNotFoundError as e: @@ -46,7 +49,7 @@ def example_dimension_mismatch(): expected=(32, 3, 224, 224), actual=(32, 3, 256, 256), tensor_name="input_images", - suggestion="Resize images to 224x224 or update model input size" + suggestion="Resize images to 224x224 or update model input size", ) except DimensionMismatchError as e: print(format_error_report(e)) @@ -59,9 +62,7 @@ def example_missing_column(): print("=" * 60) try: - available_columns = [ - "patient_id", "age", "gender", "image_path", "diagnosis" - ] + available_columns = ["patient_id", "age", "gender", "image_path", "diagnosis"] raise MissingColumnError("bmi", available_columns) except MissingColumnError as e: print(format_error_report(e)) @@ -78,7 +79,7 @@ def example_training_error(): "Loss became NaN", epoch=15, step=2500, - suggestion="Reduce learning rate or enable gradient clipping" + suggestion="Reduce learning rate or enable gradient clipping", ) except TrainingError as e: print(format_error_report(e)) @@ -95,9 +96,9 @@ def example_incompatible_config(): "Attention supervision requires CBAM attention mechanism", conflicting_options=[ "training.use_attention_supervision=True", - "model.vision.attention_type='se'" + "model.vision.attention_type='se'", ], - suggestion="Set model.vision.attention_type='cbam'" + suggestion="Set model.vision.attention_type='cbam'", ) except IncompatibleConfigError as e: print(format_error_report(e)) @@ -111,8 +112,7 @@ def example_missing_dependency(): try: raise MissingDependencyError( - "torch", - install_cmd="pip install torch torchvision" + "torch", install_cmd="pip install torch torchvision" ) except MissingDependencyError as e: print(format_error_report(e)) @@ -153,7 +153,7 @@ def example_configuration_error(): "Invalid batch size", config_path="training.batch_size", invalid_value=-1, - suggestion="Batch size must be a positive integer (e.g., 16, 32, 64)" + suggestion="Batch size must be a positive integer (e.g., 16, 32, 64)", ) except ConfigurationError as e: print(format_error_report(e)) diff --git a/examples/gradient_checkpointing_demo.py b/examples/gradient_checkpointing_demo.py index 870008f..a4a03ac 100644 --- a/examples/gradient_checkpointing_demo.py +++ b/examples/gradient_checkpointing_demo.py @@ -65,8 +65,10 @@ def train_without_checkpointing(): if device.type == "cuda": memory_allocated = torch.cuda.memory_allocated() / 1024**2 memory_reserved = torch.cuda.memory_reserved() / 1024**2 - print(f"Batch {batch_idx + 1}: Loss={loss.item():.4f}, " - f"Memory: {memory_allocated:.2f}MB / {memory_reserved:.2f}MB") + print( + f"Batch {batch_idx + 1}: Loss={loss.item():.4f}, " + f"Memory: {memory_allocated:.2f}MB / {memory_reserved:.2f}MB" + ) else: print(f"Batch {batch_idx + 1}: Loss={loss.item():.4f}") @@ -133,8 +135,10 @@ def train_with_checkpointing(): if device.type == "cuda": memory_allocated = torch.cuda.memory_allocated() / 1024**2 memory_reserved = torch.cuda.memory_reserved() / 1024**2 - print(f"Batch {batch_idx + 1}: Loss={loss.item():.4f}, " - f"Memory: {memory_allocated:.2f}MB / {memory_reserved:.2f}MB") + print( + f"Batch {batch_idx + 1}: Loss={loss.item():.4f}, " + f"Memory: {memory_allocated:.2f}MB / {memory_reserved:.2f}MB" + ) else: print(f"Batch {batch_idx + 1}: Loss={loss.item():.4f}") diff --git a/examples/hyperparameter_tuning_demo.py b/examples/hyperparameter_tuning_demo.py index cf6ea45..1b06b32 100644 --- a/examples/hyperparameter_tuning_demo.py +++ b/examples/hyperparameter_tuning_demo.py @@ -39,7 +39,9 @@ def train_and_evaluate(params): train_dataset = TensorDataset(X_train, y_train) val_dataset = TensorDataset(X_val, y_val) - train_loader = DataLoader(train_dataset, batch_size=params["batch_size"], shuffle=True) + train_loader = DataLoader( + train_dataset, batch_size=params["batch_size"], shuffle=True + ) val_loader = DataLoader(val_dataset, batch_size=params["batch_size"]) # 创建模型 @@ -47,11 +49,17 @@ def train_and_evaluate(params): # 优化器 if params["optimizer"] == "adam": - optimizer = optim.Adam(model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"]) + optimizer = optim.Adam( + model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"] + ) elif params["optimizer"] == "sgd": - optimizer = optim.SGD(model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"]) + optimizer = optim.SGD( + model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"] + ) else: - optimizer = optim.AdamW(model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"]) + optimizer = optim.AdamW( + model.parameters(), lr=params["lr"], weight_decay=params["weight_decay"] + ) criterion = nn.CrossEntropyLoss() @@ -92,7 +100,9 @@ def objective(trial): params = { "lr": trial.suggest_float("lr", 1e-5, 1e-1, log=True), "batch_size": trial.suggest_int("batch_size", 16, 128, step=16), - "optimizer": trial.suggest_categorical("optimizer", ["adam", "sgd", "adamw"]), + "optimizer": trial.suggest_categorical( + "optimizer", ["adam", "sgd", "adamw"] + ), "weight_decay": trial.suggest_float("weight_decay", 1e-6, 1e-2, log=True), "dropout": trial.suggest_float("dropout", 0.0, 0.5, step=0.1), } diff --git a/examples/logging_demo.py b/examples/logging_demo.py index 6249596..f0771b3 100644 --- a/examples/logging_demo.py +++ b/examples/logging_demo.py @@ -77,7 +77,9 @@ def example_performance_tracking(): time.sleep(0.05) # Simulate inference # Track with additional context - with PerformanceLogger("batch_processing", logger=logger, log_args=True, batch_size=32): + with PerformanceLogger( + "batch_processing", logger=logger, log_args=True, batch_size=32 + ): time.sleep(0.02) print() @@ -163,12 +165,15 @@ def example_json_logging(): with LogContext(user_id="user123", session_id="sess456"): logger.info("User logged in") logger.info("Processing request", extra={"request_id": "req789"}) - logger.warning("Rate limit approaching", extra={"requests": 95, "limit": 100}) + logger.warning( + "Rate limit approaching", extra={"requests": 95, "limit": 100} + ) # Read and display JSON logs print("\nJSON Log Output:") with open(log_file) as f: import json + for line in f: data = json.loads(line) print(f" {json.dumps(data, indent=2)}") diff --git a/examples/model_export_demo.py b/examples/model_export_demo.py index e42ac36..37de172 100644 --- a/examples/model_export_demo.py +++ b/examples/model_export_demo.py @@ -259,7 +259,9 @@ def demo_inference(): with torch.no_grad(): torchscript_output = loaded_model(x) print(f" 输出形状: {torchscript_output.shape}") - print(f" 与 PyTorch 的差异: {(pytorch_output - torchscript_output).abs().max():.6f}") + print( + f" 与 PyTorch 的差异: {(pytorch_output - torchscript_output).abs().max():.6f}" + ) # 3. ONNX 推理 print("\n3. ONNX 推理:") @@ -271,7 +273,9 @@ def demo_inference(): ort_output = ort_session.run(None, ort_inputs)[0] print(f" 输出形状: {ort_output.shape}") - print(f" 与 PyTorch 的差异: {abs(pytorch_output.numpy() - ort_output).max():.6f}") + print( + f" 与 PyTorch 的差异: {abs(pytorch_output.numpy() - ort_output).max():.6f}" + ) except ImportError: print(" ⚠ ONNXRuntime 未安装，跳过 ONNX 推理") @@ -354,6 +358,7 @@ def main(): except Exception as e: print(f"\n❌ 错误: {e}") import traceback + traceback.print_exc() diff --git a/examples/smurf_usage.py b/examples/smurf_usage.py index 0ff5add..7da82c7 100644 --- a/examples/smurf_usage.py +++ b/examples/smurf_usage.py @@ -17,14 +17,11 @@ def example_basic_usage(): print("=" * 60) # Create model - model = smurf_small( - num_classes=4, - fusion_strategy='fused_attention' - ) + model = smurf_small(num_classes=4, fusion_strategy="fused_attention") # Prepare dummy data ct_scan = torch.randn(2, 1, 64, 128, 128) # [B, C, D, H, W] - pathology = torch.randn(2, 3, 224, 224) # [B, C, H, W] + pathology = torch.randn(2, 3, 224, 224) # [B, C, H, W] # Forward pass model.eval() @@ -45,10 +42,7 @@ def example_with_features(): print("=" * 60) # Create model - model = smurf_small( - num_classes=4, - fusion_strategy='fused_attention' - ) + model = smurf_small(num_classes=4, fusion_strategy="fused_attention") # Prepare data ct_scan = torch.randn(2, 1, 64, 128, 128) @@ -73,10 +67,7 @@ def example_mil(): print("=" * 60) # Create model with MIL - model = smurf_with_mil_small( - num_classes=4, - fusion_strategy='fused_attention' - ) + model = smurf_with_mil_small(num_classes=4, fusion_strategy="fused_attention") # Prepare data with multiple pathology patches ct_scan = torch.randn(2, 1, 64, 128, 128) @@ -95,7 +86,7 @@ def example_mil(): print(f"Output logits: {logits.shape}") # Show attention weights for first sample - attention = features['attention_weights'][0].squeeze() + attention = features["attention_weights"][0].squeeze() print("\nAttention weights for first sample:") for i, weight in enumerate(attention): print(f" Patch {i}: {weight.item():.4f}") @@ -112,7 +103,7 @@ def example_different_fusion_strategies(): ct_scan = torch.randn(2, 1, 64, 128, 128) pathology = torch.randn(2, 3, 224, 224) - strategies = ['concat', 'kronecker', 'fused_attention'] + strategies = ["concat", "kronecker", "fused_attention"] for strategy in strategies: model = smurf_small(num_classes=4, fusion_strategy=strategy) @@ -134,7 +125,7 @@ def example_training_setup(): print("=" * 60) # Create model - model = smurf_small(num_classes=4, fusion_strategy='fused_attention') + model = smurf_small(num_classes=4, fusion_strategy="fused_attention") # Setup optimizer optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.01) diff --git a/examples/train_demo.py b/examples/train_demo.py index 1a960a7..26f3e18 100644 --- a/examples/train_demo.py +++ b/examples/train_demo.py @@ -178,18 +178,10 @@ def main(): model=ModelConfig( num_classes=2, vision=VisionConfig( - backbone="resnet18", - pretrained=True, - attention_type="cbam" + backbone="resnet18", pretrained=True, attention_type="cbam" ), - tabular=TabularConfig( - hidden_dims=[32, 16], - output_dim=16 - ), - fusion=FusionConfig( - fusion_type="gated", - hidden_dim=32 - ) + tabular=TabularConfig(hidden_dims=[32, 16], output_dim=16), + fusion=FusionConfig(fusion_type="gated", hidden_dim=32), ), training=TrainingConfig( num_epochs=5, # Short run for demo @@ -197,7 +189,7 @@ def main(): stage1_epochs=1, stage2_epochs=2, stage3_epochs=2, - ) + ), ) # 3. Data Loading @@ -209,12 +201,11 @@ def main(): target_column=config.data.target_column, numerical_features=config.data.numerical_features, categorical_features=config.data.categorical_features, - handle_missing="fill_mean" + handle_missing="fill_mean", ) train_ds, val_ds, test_ds = split_dataset( - full_dataset, - train_ratio=0.7, val_ratio=0.15, test_ratio=0.15 + full_dataset, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15 ) # Add transforms @@ -227,7 +218,7 @@ def main(): val_dataset=val_ds, test_dataset=test_ds, batch_size=config.data.batch_size, - num_workers=config.data.num_workers + num_workers=config.data.num_workers, ) # 4. Model Setup @@ -236,33 +227,32 @@ def main(): backbone_name=config.model.vision.backbone, pretrained=config.model.vision.pretrained, feature_dim=config.model.vision.feature_dim, - attention_type=config.model.vision.attention_type + attention_type=config.model.vision.attention_type, ) tabular_backbone = create_tabular_backbone( input_dim=train_ds.get_tabular_dim(), output_dim=config.model.tabular.output_dim, - hidden_dims=config.model.tabular.hidden_dims + hidden_dims=config.model.tabular.hidden_dims, ) fusion_module = create_fusion_module( fusion_type=config.model.fusion.fusion_type, vision_dim=config.model.vision.feature_dim, tabular_dim=config.model.tabular.output_dim, - output_dim=config.model.fusion.hidden_dim + output_dim=config.model.fusion.hidden_dim, ) model = MultiModalFusionModel( vision_backbone=vision_backbone, tabular_backbone=tabular_backbone, fusion_module=fusion_module, - num_classes=config.model.num_classes + num_classes=config.model.num_classes, ) # 5. Training Setup optimizer = optim.AdamW( - model.parameters(), - lr=config.training.optimizer.learning_rate + model.parameters(), lr=config.training.optimizer.learning_rate ) scheduler = optim.lr_scheduler.CosineAnnealingLR( optimizer, T_max=config.training.num_epochs @@ -274,7 +264,7 @@ def main(): train_loader=dataloaders["train"], val_loader=dataloaders["val"], optimizer=optimizer, - scheduler=scheduler + scheduler=scheduler, ) # 6. Train @@ -306,7 +296,7 @@ def main(): metrics=metrics, output_dir=config.logging.output_dir, experiment_name=config.experiment_name, - config=config.to_dict() + config=config.to_dict(), ) logger.info(f"Demo complete! Report generated at {report_path}") diff --git a/med_core/cli/evaluate.py b/med_core/cli/evaluate.py index d8c8fe1..68b5fcd 100644 --- a/med_core/cli/evaluate.py +++ b/med_core/cli/evaluate.py @@ -138,7 +138,7 @@ def evaluate() -> None: logger.info(f"Loading weights from {args.checkpoint}") try: - checkpoint = torch.load(args.checkpoint, map_location=device) + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=True) model.load_state_dict(checkpoint["model_state_dict"]) model.to(device) model.eval() diff --git a/med_core/datasets/medical.py b/med_core/datasets/medical.py index 2bf7446..fbc42ea 100644 --- a/med_core/datasets/medical.py +++ b/med_core/datasets/medical.py @@ -190,6 +190,19 @@ def from_csv( else: img_path = image_dir / img_name + # Resolve to absolute path and validate it's within image_dir + try: + img_path = img_path.resolve() + # Ensure the resolved path is within image_dir (prevent path traversal) + if not Path(img_name).is_absolute(): + image_dir_resolved = image_dir.resolve() + if not str(img_path).startswith(str(image_dir_resolved)): + logger.warning(f"Path traversal attempt detected: {img_name}") + continue + except (OSError, RuntimeError) as e: + logger.warning(f"Invalid path {img_name}: {e}") + continue + if img_path.exists(): image_paths.append(img_path) valid_indices.append(idx) diff --git a/med_core/fusion/strategies.py b/med_core/fusion/strategies.py index c4d5f5c..1e4a0ab 100644 --- a/med_core/fusion/strategies.py +++ b/med_core/fusion/strategies.py @@ -149,8 +149,9 @@ def forward( # Apply dropout fused = self.dropout(fused) - # Store gate values for analysis - self._last_gate_values = z.detach() + # Store gate values for analysis (only in eval mode to prevent memory leak) + if not self.training: + self._last_gate_values = z.detach() aux_outputs = { "gate_values": z, @@ -260,8 +261,9 @@ def forward( # Extract CLS token as fused representation fused = tokens[:, 0, :] # (B, D) - # Store attention weights - self._last_attention_weights = attn_weights.detach() + # Store attention weights (only in eval mode to prevent memory leak) + if not self.training: + self._last_attention_weights = attn_weights.detach() aux_outputs = {"attention_weights": attn_weights} @@ -366,9 +368,10 @@ def forward( concat = torch.cat([v2t, t2v], dim=1) # (B, 2D) fused = self.output_proj(concat) # (B, D) - # Store attention weights - self._v2t_attn = v2t_attn.detach() - self._t2v_attn = t2v_attn.detach() + # Store attention weights (only in eval mode to prevent memory leak) + if not self.training: + self._v2t_attn = v2t_attn.detach() + self._t2v_attn = t2v_attn.detach() aux_outputs = { "vision_to_tabular_attn": v2t_attn, diff --git a/med_core/shared/model_utils/metrics.py b/med_core/shared/model_utils/metrics.py index df8b744..ad5ea2e 100644 --- a/med_core/shared/model_utils/metrics.py +++ b/med_core/shared/model_utils/metrics.py @@ -292,7 +292,8 @@ def calculate_confidence_intervals( try: auc_scores.append(roc_auc_score(y_true_boot, y_prob_boot)) accuracy_scores.append(accuracy_score(y_true_boot, y_pred_boot)) - except Exception: + except Exception as e: + logger.debug(f"Bootstrap iteration failed: {e}") continue ci_results = {} diff --git a/med_core/shared/model_utils/training.py b/med_core/shared/model_utils/training.py index 74a7794..c518739 100644 --- a/med_core/shared/model_utils/training.py +++ b/med_core/shared/model_utils/training.py @@ -217,7 +217,7 @@ def load_checkpoint( if not path.exists(): raise FileNotFoundError(f"Checkpoint not found: {path}") - checkpoint = torch.load(path, map_location=device) + checkpoint = torch.load(path, map_location=device, weights_only=True) model.load_state_dict(checkpoint["model_state_dict"]) if optimizer and "optimizer_state_dict" in checkpoint: diff --git a/med_core/trainers/base.py b/med_core/trainers/base.py index 85d8ea3..fcfac34 100644 --- a/med_core/trainers/base.py +++ b/med_core/trainers/base.py @@ -303,7 +303,7 @@ def _handle_checkpointing(self, val_metrics: dict[str, float]): self._save_checkpoint("last.pth", val_metrics) def _save_checkpoint(self, filename: str, metrics: dict[str, float]): - """Save model checkpoint.""" + """Save model checkpoint with atomic write to prevent corruption.""" checkpoint = { "epoch": self.current_epoch, "global_step": self.global_step, @@ -314,7 +314,12 @@ def _save_checkpoint(self, filename: str, metrics: dict[str, float]): } path = self.checkpoint_dir / filename - torch.save(checkpoint, path) + temp_path = path.with_suffix(".tmp") + + # Write to temporary file first + torch.save(checkpoint, temp_path) + # Atomic rename + temp_path.replace(path) def load_checkpoint(self, path: str | Path): """Load model checkpoint.""" @@ -322,7 +327,7 @@ def load_checkpoint(self, path: str | Path): if not path.exists(): raise FileNotFoundError(f"Checkpoint not found: {path}") - checkpoint = torch.load(path, map_location=self.device) + checkpoint = torch.load(path, map_location=self.device, weights_only=True) self.model.load_state_dict(checkpoint["model_state_dict"]) self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) self.current_epoch = checkpoint["epoch"] diff --git a/med_core/utils/checkpoint.py b/med_core/utils/checkpoint.py index 720029c..923a938 100644 --- a/med_core/utils/checkpoint.py +++ b/med_core/utils/checkpoint.py @@ -74,7 +74,7 @@ def load_checkpoint( if not filepath.exists(): raise FileNotFoundError(f"Checkpoint not found: {filepath}") - checkpoint = torch.load(filepath, map_location=device) + checkpoint = torch.load(filepath, map_location=device, weights_only=True) model.load_state_dict(checkpoint["model_state_dict"]) diff --git a/med_core/utils/distributed.py b/med_core/utils/distributed.py index 8cae850..0a4912e 100644 --- a/med_core/utils/distributed.py +++ b/med_core/utils/distributed.py @@ -450,7 +450,7 @@ def load_checkpoint( """ # 加载检查点 map_location = {"cuda:0": f"cuda:{get_rank()}"} - checkpoint = torch.load(filepath, map_location=map_location) + checkpoint = torch.load(filepath, map_location=map_location, weights_only=True) # 加载模型状态 if isinstance(model, (DDPWrapper, FSDPWrapper)): diff --git a/med_core/web/checkpoint_manager.py b/med_core/web/checkpoint_manager.py index 39a98ed..2d85896 100644 --- a/med_core/web/checkpoint_manager.py +++ b/med_core/web/checkpoint_manager.py @@ -284,7 +284,7 @@ def _load_models(self, checkpoint_path: Path) -> dict[str, Any]: # 只加载权重字典，不加载模型结构 # 模型结构需要在恢复时重新构建 - state_dict = torch.load(model_file, map_location="cpu") + state_dict = torch.load(model_file, map_location="cpu", weights_only=True) models[f"{node_id}_{model_type}"] = state_dict logger.debug(f"Model weights loaded: {model_file}") diff --git a/med_core/web/node_executors.py b/med_core/web/node_executors.py index 1d0f310..08b801e 100644 --- a/med_core/web/node_executors.py +++ b/med_core/web/node_executors.py @@ -47,6 +47,7 @@ async def execute( Returns: 执行结果字典 """ + # TODO: 子类需要实现具体的执行逻辑 raise NotImplementedError diff --git a/med_core/web/resource_monitor.py b/med_core/web/resource_monitor.py index 1c2ca08..5d09fb3 100644 --- a/med_core/web/resource_monitor.py +++ b/med_core/web/resource_monitor.py @@ -95,8 +95,8 @@ def __del__(self): if self.gpu_available and PYNVML_AVAILABLE: try: pynvml.nvmlShutdown() - except Exception: - pass + except Exception as e: + logger.debug(f"Failed to shutdown NVML during cleanup: {e}") async def start(self) -> None: """启动监控""" @@ -191,7 +191,8 @@ def _get_gpu_info(self) -> list[dict[str, Any]]: temperature = pynvml.nvmlDeviceGetTemperature( handle, pynvml.NVML_TEMPERATURE_GPU ) - except Exception: + except Exception as e: + logger.debug(f"Failed to get GPU temperature: {e}") temperature = None # 功率 @@ -202,7 +203,8 @@ def _get_gpu_info(self) -> list[dict[str, Any]]: power_limit = ( pynvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000.0 ) - except Exception: + except Exception as e: + logger.debug(f"Failed to get GPU power info: {e}") power_usage = None power_limit = None diff --git a/med_core/web/routers/workflow.py b/med_core/web/routers/workflow.py index ca891a0..3d6a101 100644 --- a/med_core/web/routers/workflow.py +++ b/med_core/web/routers/workflow.py @@ -251,13 +251,13 @@ async def progress_callback(node_id: str, status: NodeStatus, progress: float): logger.error(f"WebSocket error for workflow {workflow_id}: {e}") try: await websocket.send_json({"type": "error", "error": str(e)}) - except Exception: - pass + except Exception as send_error: + logger.debug(f"Failed to send error message: {send_error}") finally: try: await websocket.close() - except Exception: - pass + except Exception as close_error: + logger.debug(f"Failed to close websocket: {close_error}") @router.get("/") diff --git a/med_core_rs/analyze_bottleneck.py b/med_core_rs/analyze_bottleneck.py index fb6b408..b40008d 100644 --- a/med_core_rs/analyze_bottleneck.py +++ b/med_core_rs/analyze_bottleneck.py @@ -2,10 +2,11 @@ """ 深度性能分析：找出瓶颈 """ + import os import sys -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'target/release')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "target/release")) import time @@ -13,9 +14,9 @@ import med_core_rs -print("="*70) +print("=" * 70) print("🔍 深度性能分析：数据拷贝 vs 计算时间") -print("="*70) +print("=" * 70) # Test 1: 测量数据拷贝开销 print("\n[测试 1] 数据拷贝开销分析") @@ -46,9 +47,11 @@ numpy_time = time.perf_counter() - start copy_overhead = copy_time / rust_time * 100 - print(f"批量 {n:4d}: 拷贝 {copy_time*1000:6.2f}ms ({copy_overhead:5.1f}%), " - f"Rust {rust_time*1000:6.2f}ms, NumPy {numpy_time*1000:6.2f}ms, " - f"加速 {numpy_time/rust_time:.2f}x") + print( + f"批量 {n:4d}: 拷贝 {copy_time * 1000:6.2f}ms ({copy_overhead:5.1f}%), " + f"Rust {rust_time * 1000:6.2f}ms, NumPy {numpy_time * 1000:6.2f}ms, " + f"加速 {numpy_time / rust_time:.2f}x" + ) # Test 2: 测量不同操作的时间分布 print("\n[测试 2] Rust 函数内部时间分布估算") @@ -77,11 +80,17 @@ _ = (img - vmin) / (vmax - vmin) compute_time = time.perf_counter() - start -print(f"总时间: {total_time*1000:6.2f} ms (100%)") -print(f"数据传输: {transfer_time*1000:6.2f} ms ({transfer_time/total_time*100:5.1f}%)") -print(f"纯计算: {compute_time*1000:6.2f} ms ({compute_time/total_time*100:5.1f}%)") -print(f"其他开销: {(total_time-transfer_time-compute_time)*1000:6.2f} ms " - f"({(total_time-transfer_time-compute_time)/total_time*100:5.1f}%)") +print(f"总时间: {total_time * 1000:6.2f} ms (100%)") +print( + f"数据传输: {transfer_time * 1000:6.2f} ms ({transfer_time / total_time * 100:5.1f}%)" +) +print( + f"纯计算: {compute_time * 1000:6.2f} ms ({compute_time / total_time * 100:5.1f}%)" +) +print( + f"其他开销: {(total_time - transfer_time - compute_time) * 1000:6.2f} ms " + f"({(total_time - transfer_time - compute_time) / total_time * 100:5.1f}%)" +) # Test 3: 不同方法的性能对比 print("\n[测试 3] 不同归一化方法的性能") @@ -117,8 +126,10 @@ numpy_time = time.perf_counter() - start speedup = numpy_time / rust_time - print(f"{method:12s}: Rust {rust_time*1000:6.2f}ms, " - f"NumPy {numpy_time*1000:6.2f}ms, 加速 {speedup:.2f}x") + print( + f"{method:12s}: Rust {rust_time * 1000:6.2f}ms, " + f"NumPy {numpy_time * 1000:6.2f}ms, 加速 {speedup:.2f}x" + ) # Test 4: 内存布局影响 print("\n[测试 4] 内存布局对性能的影响") @@ -134,11 +145,13 @@ start = time.perf_counter() result = med_core_rs.normalize_intensity_batch(images, method="minmax") elapsed = time.perf_counter() - start - print(f"{name:10s}: {elapsed*1000:6.2f} ms, 连续性: {images.flags['C_CONTIGUOUS']}") + print( + f"{name:10s}: {elapsed * 1000:6.2f} ms, 连续性: {images.flags['C_CONTIGUOUS']}" + ) -print("\n" + "="*70) +print("\n" + "=" * 70) print("💡 分析结论") -print("="*70) +print("=" * 70) print(""" 1. 数据拷贝开销占总时间的 20-30% 2. Python-Rust 边界开销约 10-15% diff --git a/med_core_rs/benchmark_3d.py b/med_core_rs/benchmark_3d.py index a66fadb..7a7cce7 100644 --- a/med_core_rs/benchmark_3d.py +++ b/med_core_rs/benchmark_3d.py @@ -2,10 +2,11 @@ """ Detailed performance analysis for 3D volume processing """ + import os import sys -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'target/release')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "target/release")) import time @@ -13,9 +14,10 @@ import med_core_rs -print("="*70) +print("=" * 70) print("📊 3D 体积处理性能深度分析") -print("="*70) +print("=" * 70) + def benchmark(func, *args, n_runs=10, warmup=2): """运行基准测试""" @@ -32,6 +34,7 @@ def benchmark(func, *args, n_runs=10, warmup=2): return np.mean(times), np.std(times), result + # Test different batch sizes print("\n[测试 1] 不同批量大小的性能") print("-" * 70) @@ -43,8 +46,7 @@ def benchmark(func, *args, n_runs=10, warmup=2): # Rust rust_mean, rust_std, _ = benchmark( - lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), - volumes + lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), volumes ) # NumPy @@ -60,8 +62,10 @@ def numpy_batch(vols): numpy_mean, numpy_std, _ = benchmark(numpy_batch, volumes) speedup = numpy_mean / rust_mean - print(f"{batch_size:<10} {rust_mean*1000:>10.2f}±{rust_std*1000:.2f} " - f"{numpy_mean*1000:>10.2f}±{numpy_std*1000:.2f} {speedup:>8.2f}x") + print( + f"{batch_size:<10} {rust_mean * 1000:>10.2f}±{rust_std * 1000:.2f} " + f"{numpy_mean * 1000:>10.2f}±{numpy_std * 1000:.2f} {speedup:>8.2f}x" + ) # Test different volume sizes print("\n[测试 2] 不同体积大小的性能 (batch_size=8)") @@ -75,9 +79,7 @@ def numpy_batch(vols): # Rust rust_mean, rust_std, _ = benchmark( - lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), - volumes, - n_runs=5 + lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), volumes, n_runs=5 ) # NumPy @@ -94,8 +96,10 @@ def numpy_batch(vols): speedup = numpy_mean / rust_mean size_str = f"{size[0]}x{size[1]}x{size[2]}" - print(f"{size_str:<15} {rust_mean*1000:>10.2f}±{rust_std*1000:.2f} " - f"{numpy_mean*1000:>10.2f}±{numpy_std*1000:.2f} {speedup:>8.2f}x") + print( + f"{size_str:<15} {rust_mean * 1000:>10.2f}±{rust_std * 1000:.2f} " + f"{numpy_mean * 1000:>10.2f}±{numpy_std * 1000:.2f} {speedup:>8.2f}x" + ) # Test single volume vs batch print("\n[测试 3] 单体积 vs 批量处理") @@ -105,17 +109,14 @@ def numpy_batch(vols): volumes_batch = np.stack([volume] * 16) # Single volume (Rust) -single_rust_mean, _, _ = benchmark( - lambda v: med_core_rs.normalize_3d_minmax(v), - volume -) +single_rust_mean, _, _ = benchmark(lambda v: med_core_rs.normalize_3d_minmax(v), volume) # Batch (Rust) batch_rust_mean, _, _ = benchmark( - lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), - volumes_batch + lambda v: med_core_rs.normalize_3d_batch(v, method="minmax"), volumes_batch ) + # Single volume (NumPy) def numpy_single(vol): vmin, vmax = vol.min(), vol.max() @@ -123,16 +124,19 @@ def numpy_single(vol): return (vol - vmin) / (vmax - vmin) return np.zeros_like(vol) + single_numpy_mean, _, _ = benchmark(numpy_single, volume) -print(f"单体积 Rust: {single_rust_mean*1000:.2f} ms") -print(f"单体积 NumPy: {single_numpy_mean*1000:.2f} ms") -print(f"批量 Rust (16): {batch_rust_mean*1000:.2f} ms ({batch_rust_mean*1000/16:.2f} ms/体积)") -print(f"批量效率: {single_rust_mean*16/batch_rust_mean:.2f}x") +print(f"单体积 Rust: {single_rust_mean * 1000:.2f} ms") +print(f"单体积 NumPy: {single_numpy_mean * 1000:.2f} ms") +print( + f"批量 Rust (16): {batch_rust_mean * 1000:.2f} ms ({batch_rust_mean * 1000 / 16:.2f} ms/体积)" +) +print(f"批量效率: {single_rust_mean * 16 / batch_rust_mean:.2f}x") -print("\n" + "="*70) +print("\n" + "=" * 70) print("💡 分析结论") -print("="*70) +print("=" * 70) print(""" 1. NumPy 在小批量时非常快，因为它的 min/max 操作高度优化 2. Rust 的并行开销在小数据量时不值得 diff --git a/med_core_rs/benchmark_comparison.py b/med_core_rs/benchmark_comparison.py index 84fd670..2b06ad2 100644 --- a/med_core_rs/benchmark_comparison.py +++ b/med_core_rs/benchmark_comparison.py @@ -21,6 +21,7 @@ from med_core_rs import ( normalize_intensity_percentile as rust_percentile, ) + RUST_AVAILABLE = True except ImportError: print("Warning: Rust module not available. Install with: maturin develop --release") @@ -51,9 +52,9 @@ def benchmark_function(func: Callable, *args, iterations: int = 100) -> float: def compare_normalization(): """Compare normalization performance.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("NORMALIZATION BENCHMARK") - print("="*70) + print("=" * 70) sizes = [256, 512, 1024] @@ -65,14 +66,14 @@ def compare_normalization(): py_mean, py_std = benchmark_function( python_normalize, image, "minmax", iterations=50 ) - print(f" Python MinMax: {py_mean*1000:.2f} ± {py_std*1000:.2f} ms") + print(f" Python MinMax: {py_mean * 1000:.2f} ± {py_std * 1000:.2f} ms") if RUST_AVAILABLE: # Rust MinMax - rust_mean, rust_std = benchmark_function( - rust_minmax, image, iterations=50 + rust_mean, rust_std = benchmark_function(rust_minmax, image, iterations=50) + print( + f" Rust MinMax: {rust_mean * 1000:.2f} ± {rust_std * 1000:.2f} ms" ) - print(f" Rust MinMax: {rust_mean*1000:.2f} ± {rust_std*1000:.2f} ms") speedup = py_mean / rust_mean print(f" 🚀 Speedup: {speedup:.2f}x") @@ -82,23 +83,25 @@ def compare_normalization(): py_mean, py_std = benchmark_function( python_normalize, image, "percentile", iterations=50 ) - print(f" Python Percentile: {py_mean*1000:.2f} ± {py_std*1000:.2f} ms") + print(f" Python Percentile: {py_mean * 1000:.2f} ± {py_std * 1000:.2f} ms") if RUST_AVAILABLE: # Rust Percentile rust_mean, rust_std = benchmark_function( rust_percentile, image, 1.0, 99.0, iterations=50 ) - print(f" Rust Percentile: {rust_mean*1000:.2f} ± {rust_std*1000:.2f} ms") + print( + f" Rust Percentile: {rust_mean * 1000:.2f} ± {rust_std * 1000:.2f} ms" + ) speedup = py_mean / rust_mean print(f" 🚀 Speedup: {speedup:.2f}x") def compare_batch_processing(): """Compare batch processing performance.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("BATCH PROCESSING BENCHMARK") - print("="*70) + print("=" * 70) batch_sizes = [10, 50, 100] img_size = 512 @@ -109,22 +112,21 @@ def compare_batch_processing(): # Python sequential processing def python_batch_process(imgs): - return np.array([ - python_normalize(img, "percentile") - for img in imgs - ]) + return np.array([python_normalize(img, "percentile") for img in imgs]) py_mean, py_std = benchmark_function( python_batch_process, images, iterations=20 ) - print(f" Python (sequential): {py_mean*1000:.2f} ± {py_std*1000:.2f} ms") + print(f" Python (sequential): {py_mean * 1000:.2f} ± {py_std * 1000:.2f} ms") if RUST_AVAILABLE: # Rust parallel processing rust_mean, rust_std = benchmark_function( rust_batch, images, "percentile", 1.0, 99.0, iterations=20 ) - print(f" Rust (parallel): {rust_mean*1000:.2f} ± {rust_std*1000:.2f} ms") + print( + f" Rust (parallel): {rust_mean * 1000:.2f} ± {rust_std * 1000:.2f} ms" + ) speedup = py_mean / rust_mean print(f" 🚀 Speedup: {speedup:.2f}x") @@ -134,9 +136,9 @@ def python_batch_process(imgs): def verify_correctness(): """Verify that Rust and Python implementations produce similar results.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("CORRECTNESS VERIFICATION") - print("="*70) + print("=" * 70) if not RUST_AVAILABLE: print("⚠️ Rust module not available, skipping verification") @@ -162,9 +164,9 @@ def verify_correctness(): def main(): - print("\n" + "="*70) + print("\n" + "=" * 70) print("MedCore Rust Acceleration Benchmark") - print("="*70) + print("=" * 70) if not RUST_AVAILABLE: print("\n⚠️ Rust module not found!") @@ -177,9 +179,9 @@ def main(): compare_normalization() compare_batch_processing() - print("\n" + "="*70) + print("\n" + "=" * 70) print("Benchmark Complete!") - print("="*70) + print("=" * 70) if __name__ == "__main__": diff --git a/med_core_rs/benchmark_standalone.py b/med_core_rs/benchmark_standalone.py index 6d836a4..2ded949 100644 --- a/med_core_rs/benchmark_standalone.py +++ b/med_core_rs/benchmark_standalone.py @@ -21,6 +21,7 @@ from med_core_rs import ( normalize_intensity_percentile as rust_percentile, ) + RUST_AVAILABLE = True except ImportError: print("⚠️ Rust 模块未找到") @@ -37,7 +38,9 @@ def numpy_normalize_minmax(image: np.ndarray) -> np.ndarray: return np.zeros_like(image) -def numpy_normalize_percentile(image: np.ndarray, p_low: float = 1.0, p_high: float = 99.0) -> np.ndarray: +def numpy_normalize_percentile( + image: np.ndarray, p_low: float = 1.0, p_high: float = 99.0 +) -> np.ndarray: """NumPy Percentile normalization""" low = np.percentile(image, p_low) high = np.percentile(image, p_high) @@ -46,7 +49,9 @@ def numpy_normalize_percentile(image: np.ndarray, p_low: float = 1.0, p_high: fl return np.zeros_like(image) -def benchmark_function(func: Callable, *args, iterations: int = 50, warmup: int = 5) -> tuple[float, float]: +def benchmark_function( + func: Callable, *args, iterations: int = 50, warmup: int = 5 +) -> tuple[float, float]: """Benchmark a function and return (mean_time, std_time)""" # Warmup for _ in range(warmup): @@ -65,9 +70,9 @@ def benchmark_function(func: Callable, *args, iterations: int = 50, warmup: int def compare_single_image(): """对比单图像处理性能""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("📊 单图像处理性能对比") - print("="*70) + print("=" * 70) sizes = [256, 512, 1024] @@ -78,31 +83,33 @@ def compare_single_image(): # MinMax print("\n MinMax 归一化:") numpy_mean, numpy_std = benchmark_function(numpy_normalize_minmax, image) - print(f" NumPy: {numpy_mean*1000:6.2f} ± {numpy_std*1000:4.2f} ms") + print(f" NumPy: {numpy_mean * 1000:6.2f} ± {numpy_std * 1000:4.2f} ms") if RUST_AVAILABLE: rust_mean, rust_std = benchmark_function(rust_minmax, image) - print(f" Rust: {rust_mean*1000:6.2f} ± {rust_std*1000:4.2f} ms") + print(f" Rust: {rust_mean * 1000:6.2f} ± {rust_std * 1000:4.2f} ms") speedup = numpy_mean / rust_mean print(f" 🚀 加速: {speedup:.2f}x") # Percentile print("\n Percentile 归一化:") - numpy_mean, numpy_std = benchmark_function(numpy_normalize_percentile, image, 1.0, 99.0) - print(f" NumPy: {numpy_mean*1000:6.2f} ± {numpy_std*1000:4.2f} ms") + numpy_mean, numpy_std = benchmark_function( + numpy_normalize_percentile, image, 1.0, 99.0 + ) + print(f" NumPy: {numpy_mean * 1000:6.2f} ± {numpy_std * 1000:4.2f} ms") if RUST_AVAILABLE: rust_mean, rust_std = benchmark_function(rust_percentile, image, 1.0, 99.0) - print(f" Rust: {rust_mean*1000:6.2f} ± {rust_std*1000:4.2f} ms") + print(f" Rust: {rust_mean * 1000:6.2f} ± {rust_std * 1000:4.2f} ms") speedup = numpy_mean / rust_mean print(f" 🚀 加速: {speedup:.2f}x") def compare_batch_processing(): """对比批量处理性能""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("📊 批量处理性能对比") - print("="*70) + print("=" * 70) batch_sizes = [10, 50, 100] img_size = 512 @@ -115,25 +122,31 @@ def compare_batch_processing(): def numpy_batch_process(imgs): return np.array([numpy_normalize_percentile(img) for img in imgs]) - numpy_mean, numpy_std = benchmark_function(numpy_batch_process, images, iterations=20) - print(f" NumPy (顺序): {numpy_mean*1000:7.2f} ± {numpy_std*1000:5.2f} ms") - print(f" {batch_size/numpy_mean:6.1f} 张/秒") + numpy_mean, numpy_std = benchmark_function( + numpy_batch_process, images, iterations=20 + ) + print( + f" NumPy (顺序): {numpy_mean * 1000:7.2f} ± {numpy_std * 1000:5.2f} ms" + ) + print(f" {batch_size / numpy_mean:6.1f} 张/秒") if RUST_AVAILABLE: rust_mean, rust_std = benchmark_function( rust_batch, images, "percentile", 1.0, 99.0, iterations=20 ) - print(f" Rust (并行): {rust_mean*1000:7.2f} ± {rust_std*1000:5.2f} ms") - print(f" {batch_size/rust_mean:6.1f} 张/秒") + print( + f" Rust (并行): {rust_mean * 1000:7.2f} ± {rust_std * 1000:5.2f} ms" + ) + print(f" {batch_size / rust_mean:6.1f} 张/秒") speedup = numpy_mean / rust_mean print(f" 🚀 加速: {speedup:.2f}x") def verify_correctness(): """验证 Rust 和 NumPy 实现的正确性""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("✅ 正确性验证") - print("="*70) + print("=" * 70) if not RUST_AVAILABLE: print("⚠️ Rust 模块不可用，跳过验证") @@ -161,9 +174,9 @@ def verify_correctness(): def print_summary(): """打印总结""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("📈 性能总结") - print("="*70) + print("=" * 70) if not RUST_AVAILABLE: print("\n⚠️ Rust 模块不可用") @@ -186,9 +199,9 @@ def print_summary(): def main(): - print("\n" + "="*70) + print("\n" + "=" * 70) print("🦀 Rust vs NumPy 性能基准测试") - print("="*70) + print("=" * 70) if not RUST_AVAILABLE: print("\n⚠️ Rust 模块未安装！") @@ -200,9 +213,9 @@ def main(): compare_batch_processing() print_summary() - print("\n" + "="*70) + print("\n" + "=" * 70) print("✅ 基准测试完成！") - print("="*70 + "\n") + print("=" * 70 + "\n") if __name__ == "__main__": diff --git a/med_core_rs/example_integration.py b/med_core_rs/example_integration.py index 818fa91..b061680 100644 --- a/med_core_rs/example_integration.py +++ b/med_core_rs/example_integration.py @@ -16,6 +16,7 @@ normalize_intensity_minmax, normalize_intensity_percentile, ) + RUST_AVAILABLE = True print("✅ Rust acceleration available") except ImportError: @@ -71,14 +72,16 @@ def preprocess_single(self, image: np.ndarray) -> np.ndarray: # Crop to target size if self.output_size: if self.use_rust: - image = center_crop_rust(image, self.output_size[0], self.output_size[1]) + image = center_crop_rust( + image, self.output_size[0], self.output_size[1] + ) else: # Python fallback h, w = image.shape th, tw = self.output_size start_h = (h - th) // 2 start_w = (w - tw) // 2 - image = image[start_h:start_h+th, start_w:start_w+tw] + image = image[start_h : start_h + th, start_w : start_w + tw] return image @@ -102,17 +105,16 @@ def preprocess_batch(self, images: np.ndarray) -> np.ndarray: # Parallel normalization images = normalize_intensity_batch( - images, - method=self.normalize_method, - p_low=1.0, - p_high=99.0 + images, method=self.normalize_method, p_low=1.0, p_high=99.0 ) # Crop each image (TODO: batch crop in Rust) if self.output_size: processed = [] for img in images: - cropped = center_crop_rust(img, self.output_size[0], self.output_size[1]) + cropped = center_crop_rust( + img, self.output_size[0], self.output_size[1] + ) processed.append(cropped) images = np.array(processed) @@ -121,17 +123,16 @@ def preprocess_batch(self, images: np.ndarray) -> np.ndarray: def demo_single_image(): """Demo: Process a single image.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("DEMO 1: Single Image Processing") - print("="*70) + print("=" * 70) # Generate test image image = np.random.rand(1024, 1024).astype(np.float32) * 255 print(f"Input image shape: {image.shape}") preprocessor = RustAcceleratedPreprocessor( - normalize_method="percentile", - output_size=(224, 224) + normalize_method="percentile", output_size=(224, 224) ) start = time.perf_counter() @@ -139,15 +140,15 @@ def demo_single_image(): elapsed = time.perf_counter() - start print(f"Output image shape: {processed.shape}") - print(f"Processing time: {elapsed*1000:.2f} ms") + print(f"Processing time: {elapsed * 1000:.2f} ms") print(f"Value range: [{processed.min():.3f}, {processed.max():.3f}]") def demo_batch_processing(): """Demo: Process a batch of images.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("DEMO 2: Batch Processing") - print("="*70) + print("=" * 70) batch_size = 100 img_size = 512 @@ -157,8 +158,7 @@ def demo_batch_processing(): print(f"Input batch shape: {images.shape}") preprocessor = RustAcceleratedPreprocessor( - normalize_method="percentile", - output_size=(224, 224) + normalize_method="percentile", output_size=(224, 224) ) start = time.perf_counter() @@ -166,16 +166,16 @@ def demo_batch_processing(): elapsed = time.perf_counter() - start print(f"Output batch shape: {processed.shape}") - print(f"Total processing time: {elapsed*1000:.2f} ms") - print(f"Time per image: {elapsed/batch_size*1000:.2f} ms") - print(f"Throughput: {batch_size/elapsed:.1f} images/sec") + print(f"Total processing time: {elapsed * 1000:.2f} ms") + print(f"Time per image: {elapsed / batch_size * 1000:.2f} ms") + print(f"Throughput: {batch_size / elapsed:.1f} images/sec") def demo_integration_with_dataloader(): """Demo: Integration with PyTorch DataLoader.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("DEMO 3: Integration with DataLoader") - print("="*70) + print("=" * 70) try: import torch @@ -188,8 +188,7 @@ class MedicalDataset(Dataset): def __init__(self, num_samples=1000): self.num_samples = num_samples self.preprocessor = RustAcceleratedPreprocessor( - normalize_method="percentile", - output_size=(224, 224) + normalize_method="percentile", output_size=(224, 224) ) def __len__(self): @@ -227,21 +226,21 @@ def __getitem__(self, idx): elapsed = time.perf_counter() - start print(f"\nTotal time: {elapsed:.2f} sec") - print(f"Throughput: {len(dataset)/elapsed:.1f} samples/sec") + print(f"Throughput: {len(dataset) / elapsed:.1f} samples/sec") def main(): - print("\n" + "="*70) + print("\n" + "=" * 70) print("MedCore Rust Acceleration - Integration Examples") - print("="*70) + print("=" * 70) demo_single_image() demo_batch_processing() demo_integration_with_dataloader() - print("\n" + "="*70) + print("\n" + "=" * 70) print("✅ All demos completed!") - print("="*70) + print("=" * 70) print("\nNext steps:") print("1. Integrate into your training pipeline") print("2. Run benchmarks: python benchmark_comparison.py") diff --git a/med_core_rs/integration_example.py b/med_core_rs/integration_example.py index 1df7147..e466f8a 100644 --- a/med_core_rs/integration_example.py +++ b/med_core_rs/integration_example.py @@ -4,10 +4,11 @@ 这个示例展示了如何在真实的训练代码中集成 Rust 加速模块。 """ + import os import sys -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'target/release')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "target/release")) import time @@ -17,6 +18,7 @@ try: import torch from torch.utils.data import DataLoader + HAS_TORCH = True except ImportError: HAS_TORCH = False @@ -24,14 +26,15 @@ import med_core_rs -print("="*70) +print("=" * 70) print("🚀 MedFusion + Rust 加速集成示例") -print("="*70) +print("=" * 70) # ============================================================================ # 1. 定义数据集 (模拟医学图像数据集) # ============================================================================ + class MedicalImageDataset: """模拟医学图像数据集""" @@ -49,10 +52,12 @@ def __getitem__(self, idx): label = idx % 2 # 二分类 return image, label + # ============================================================================ # 2. 定义 Collate 函数 (关键优化点) # ============================================================================ + def collate_fn_numpy(batch: list[tuple[np.ndarray, int]]): """传统的 NumPy collate 函数""" images, labels = zip(*batch) @@ -71,6 +76,7 @@ def collate_fn_numpy(batch: list[tuple[np.ndarray, int]]): else: return normalized, np.array(labels) + def collate_fn_rust(batch: list[tuple[np.ndarray, int]]): """🚀 使用 Rust 加速的 collate 函数""" images, labels = zip(*batch) @@ -78,10 +84,7 @@ def collate_fn_rust(batch: list[tuple[np.ndarray, int]]): # Rust Percentile 归一化 (4.8x 加速!) normalized = med_core_rs.normalize_intensity_batch( - images, - method="percentile", - p_low=1.0, - p_high=99.0 + images, method="percentile", p_low=1.0, p_high=99.0 ) if HAS_TORCH: @@ -89,15 +92,17 @@ def collate_fn_rust(batch: list[tuple[np.ndarray, int]]): else: return normalized, np.array(labels) + # ============================================================================ # 3. 性能对比测试 # ============================================================================ + def benchmark_dataloader(collate_fn, name: str, n_batches: int = 50): """测试 DataLoader 性能""" - print(f"\n{'='*70}") + print(f"\n{'=' * 70}") print(f"📊 测试: {name}") - print(f"{'='*70}") + print(f"{'=' * 70}") dataset = MedicalImageDataset(n_samples=1000) @@ -107,7 +112,7 @@ def benchmark_dataloader(collate_fn, name: str, n_batches: int = 50): batch_size=32, collate_fn=collate_fn, num_workers=0, # 单进程测试 - shuffle=False + shuffle=False, ) else: # 模拟 DataLoader @@ -119,7 +124,10 @@ def __init__(self, dataset, batch_size, collate_fn): def __iter__(self): for i in range(0, len(self.dataset), self.batch_size): - batch = [self.dataset[j] for j in range(i, min(i + self.batch_size, len(self.dataset)))] + batch = [ + self.dataset[j] + for j in range(i, min(i + self.batch_size, len(self.dataset))) + ] yield self.collate_fn(batch) dataloader = SimpleDataLoader(dataset, batch_size=32, collate_fn=collate_fn) @@ -149,46 +157,56 @@ def __iter__(self): total_time = time.time() - start_total print(f"总时间: {total_time:.2f} 秒") - print(f"平均每批: {np.mean(times)*1000:.2f} ms") + print(f"平均每批: {np.mean(times) * 1000:.2f} ms") print(f"吞吐量: {n_batches * 32 / total_time:.1f} 张/秒") return total_time, np.mean(times) + # ============================================================================ # 4. 运行对比测试 # ============================================================================ -print("\n" + "="*70) +print("\n" + "=" * 70) print("🔬 开始性能对比测试") -print("="*70) +print("=" * 70) # 测试 NumPy 版本 -numpy_total, numpy_avg = benchmark_dataloader(collate_fn_numpy, "NumPy Percentile", n_batches=50) +numpy_total, numpy_avg = benchmark_dataloader( + collate_fn_numpy, "NumPy Percentile", n_batches=50 +) # 测试 Rust 版本 -rust_total, rust_avg = benchmark_dataloader(collate_fn_rust, "Rust Percentile 🚀", n_batches=50) +rust_total, rust_avg = benchmark_dataloader( + collate_fn_rust, "Rust Percentile 🚀", n_batches=50 +) # 计算加速比 speedup = numpy_total / rust_total -print("\n" + "="*70) +print("\n" + "=" * 70) print("📊 性能对比结果") -print("="*70) +print("=" * 70) print(f"{'方法':<20} {'总时间':<15} {'平均每批':<15} {'吞吐量':<15}") -print("-"*70) -print(f"{'NumPy':<20} {numpy_total:>10.2f} s {numpy_avg*1000:>10.2f} ms {50*32/numpy_total:>10.1f} 张/秒") -print(f"{'Rust 🚀':<20} {rust_total:>10.2f} s {rust_avg*1000:>10.2f} ms {50*32/rust_total:>10.1f} 张/秒") -print("-"*70) +print("-" * 70) +print( + f"{'NumPy':<20} {numpy_total:>10.2f} s {numpy_avg * 1000:>10.2f} ms {50 * 32 / numpy_total:>10.1f} 张/秒" +) +print( + f"{'Rust 🚀':<20} {rust_total:>10.2f} s {rust_avg * 1000:>10.2f} ms {50 * 32 / rust_total:>10.1f} 张/秒" +) +print("-" * 70) print(f"{'加速比':<20} {speedup:>10.2f}x") -print("="*70) +print("=" * 70) # ============================================================================ # 5. 实际训练示例 # ============================================================================ -print("\n" + "="*70) +print("\n" + "=" * 70) print("🎓 实际训练流程示例") -print("="*70) +print("=" * 70) + def train_one_epoch_simulation(dataloader, name: str): """模拟训练一个 epoch""" @@ -213,44 +231,59 @@ def train_one_epoch_simulation(dataloader, name: str): elapsed = time.time() - start print(f" Epoch 完成: {elapsed:.2f} 秒") - print(f" 平均 loss: {total_loss / (i+1):.4f}") + print(f" 平均 loss: {total_loss / (i + 1):.4f}") return elapsed + # 创建数据集 dataset = MedicalImageDataset(n_samples=1000) # NumPy 版本 if HAS_TORCH: - dataloader_numpy = DataLoader(dataset, batch_size=32, collate_fn=collate_fn_numpy, num_workers=0) + dataloader_numpy = DataLoader( + dataset, batch_size=32, collate_fn=collate_fn_numpy, num_workers=0 + ) else: + class SimpleDataLoader: def __init__(self, dataset, batch_size, collate_fn): self.dataset = dataset self.batch_size = batch_size self.collate_fn = collate_fn + def __iter__(self): for i in range(0, len(self.dataset), self.batch_size): - batch = [self.dataset[j] for j in range(i, min(i + self.batch_size, len(self.dataset)))] + batch = [ + self.dataset[j] + for j in range(i, min(i + self.batch_size, len(self.dataset))) + ] yield self.collate_fn(batch) - dataloader_numpy = SimpleDataLoader(dataset, batch_size=32, collate_fn=collate_fn_numpy) + + dataloader_numpy = SimpleDataLoader( + dataset, batch_size=32, collate_fn=collate_fn_numpy + ) numpy_epoch_time = train_one_epoch_simulation(dataloader_numpy, "NumPy") # Rust 版本 if HAS_TORCH: - dataloader_rust = DataLoader(dataset, batch_size=32, collate_fn=collate_fn_rust, num_workers=0) + dataloader_rust = DataLoader( + dataset, batch_size=32, collate_fn=collate_fn_rust, num_workers=0 + ) else: - dataloader_rust = SimpleDataLoader(dataset, batch_size=32, collate_fn=collate_fn_rust) + dataloader_rust = SimpleDataLoader( + dataset, batch_size=32, collate_fn=collate_fn_rust + ) rust_epoch_time = train_one_epoch_simulation(dataloader_rust, "Rust 🚀") # 计算训练加速 train_speedup = numpy_epoch_time / rust_epoch_time -print("\n" + "="*70) +print("\n" + "=" * 70) print("🎯 训练性能提升") -print("="*70) +print("=" * 70) print(f"NumPy epoch 时间: {numpy_epoch_time:.2f} 秒") print(f"Rust epoch 时间: {rust_epoch_time:.2f} 秒") print(f"加速比: {train_speedup:.2f}x") @@ -259,9 +292,9 @@ def __iter__(self): print(f" Rust: {rust_epoch_time * 100 / 60:.1f} 分钟") print(f" 节省: {(numpy_epoch_time - rust_epoch_time) * 100 / 60:.1f} 分钟") -print("\n" + "="*70) +print("\n" + "=" * 70) print("✅ 集成示例完成！") -print("="*70) +print("=" * 70) print(""" 📝 如何在你的项目中使用: diff --git a/med_core_rs/test_3d.py b/med_core_rs/test_3d.py index a89ed2a..25f819e 100644 --- a/med_core_rs/test_3d.py +++ b/med_core_rs/test_3d.py @@ -2,10 +2,11 @@ """ Test 3D volume preprocessing functions """ + import os import sys -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'target/release')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "target/release")) import time @@ -13,14 +14,15 @@ try: import med_core_rs + print("✅ med_core_rs 模块加载成功") except ImportError as e: print(f"❌ 无法加载 med_core_rs: {e}") sys.exit(1) -print("\n" + "="*60) +print("\n" + "=" * 60) print("🧪 3D 体积处理功能测试") -print("="*60) +print("=" * 60) # Test 1: Single volume normalization print("\n[测试 1] 单个 3D 体积归一化") @@ -35,8 +37,12 @@ # Percentile normalization result_percentile = med_core_rs.normalize_3d_percentile(volume, p_low=1.0, p_high=99.0) -print(f"Percentile 归一化: 范围 [{result_percentile.min():.4f}, {result_percentile.max():.4f}]") -assert result_percentile.min() >= 0.0 and result_percentile.max() <= 1.0, "Percentile 归一化失败" +print( + f"Percentile 归一化: 范围 [{result_percentile.min():.4f}, {result_percentile.max():.4f}]" +) +assert result_percentile.min() >= 0.0 and result_percentile.max() <= 1.0, ( + "Percentile 归一化失败" +) print("✅ Percentile 归一化正确") # Test 2: Batch processing @@ -49,8 +55,8 @@ result_batch = med_core_rs.normalize_3d_batch(volumes, method="percentile") elapsed = time.time() - start -print(f"批量处理完成: {elapsed*1000:.2f} ms") -print(f"吞吐量: {batch_size/elapsed:.1f} 体积/秒") +print(f"批量处理完成: {elapsed * 1000:.2f} ms") +print(f"吞吐量: {batch_size / elapsed:.1f} 体积/秒") print(f"输出形状: {result_batch.shape}") assert result_batch.shape == volumes.shape, "批量处理形状不匹配" print("✅ 批量处理正确") @@ -62,11 +68,13 @@ target_shape = (32, 64, 64) start = time.time() -resampled = med_core_rs.resample_3d(volume, target_shape[0], target_shape[1], target_shape[2]) +resampled = med_core_rs.resample_3d( + volume, target_shape[0], target_shape[1], target_shape[2] +) elapsed = time.time() - start print(f"重采样后形状: {resampled.shape}") -print(f"重采样耗时: {elapsed*1000:.2f} ms") +print(f"重采样耗时: {elapsed * 1000:.2f} ms") assert resampled.shape == target_shape, "重采样形状不匹配" print("✅ 重采样正确") @@ -80,6 +88,7 @@ rust_result = med_core_rs.normalize_3d_batch(volumes, method="minmax") rust_time = time.time() - start + # NumPy processing def numpy_normalize_batch(volumes): result = np.zeros_like(volumes) @@ -90,13 +99,18 @@ def numpy_normalize_batch(volumes): result[i] = (vol - vmin) / (vmax - vmin) return result + start = time.time() numpy_result = numpy_normalize_batch(volumes) numpy_time = time.time() - start speedup = numpy_time / rust_time -print(f"Rust 批量处理: {rust_time*1000:.2f} ms ({batch_size/rust_time:.1f} 体积/秒)") -print(f"NumPy 批量处理: {numpy_time*1000:.2f} ms ({batch_size/numpy_time:.1f} 体积/秒)") +print( + f"Rust 批量处理: {rust_time * 1000:.2f} ms ({batch_size / rust_time:.1f} 体积/秒)" +) +print( + f"NumPy 批量处理: {numpy_time * 1000:.2f} ms ({batch_size / numpy_time:.1f} 体积/秒)" +) print(f"🚀 加速比: {speedup:.2f}x") # Verify correctness @@ -105,13 +119,13 @@ def numpy_normalize_batch(volumes): assert max_diff < 1e-5, "结果不匹配" print("✅ 结果正确性验证通过") -print("\n" + "="*60) +print("\n" + "=" * 60) print("🎉 所有测试通过！") -print("="*60) +print("=" * 60) print("\n📊 性能总结:") -print(f" - 单体积归一化: ~{elapsed*1000:.1f} ms") -print(f" - 批量处理 ({batch_size} 体积): {rust_time*1000:.1f} ms") -print(f" - 吞吐量: {batch_size/rust_time:.1f} 体积/秒") +print(f" - 单体积归一化: ~{elapsed * 1000:.1f} ms") +print(f" - 批量处理 ({batch_size} 体积): {rust_time * 1000:.1f} ms") +print(f" - 吞吐量: {batch_size / rust_time:.1f} 体积/秒") print(f" - 相比 NumPy 加速: {speedup:.2f}x") -print(f" - 3D 重采样: ~{elapsed*1000:.1f} ms") +print(f" - 3D 重采样: ~{elapsed * 1000:.1f} ms") diff --git a/med_core_rs/test_mil.py b/med_core_rs/test_mil.py index 417da33..bc22360 100644 --- a/med_core_rs/test_mil.py +++ b/med_core_rs/test_mil.py @@ -2,10 +2,11 @@ """ Test MIL aggregation functions """ + import os import sys -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'target/release')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "target/release")) import time @@ -13,22 +14,26 @@ try: import med_core_rs + print("✅ med_core_rs 模块加载成功") except ImportError as e: print(f"❌ 无法加载 med_core_rs: {e}") sys.exit(1) -print("\n" + "="*60) +print("\n" + "=" * 60) print("🧪 MIL 聚合器功能测试") -print("="*60) +print("=" * 60) # Test 1: Max pooling print("\n[测试 1] Max Pooling MIL") -instances = np.array([ - [1.0, 2.0, 3.0, 4.0], - [5.0, 1.0, 2.0, 3.0], - [2.0, 4.0, 1.0, 5.0], -], dtype=np.float32) +instances = np.array( + [ + [1.0, 2.0, 3.0, 4.0], + [5.0, 1.0, 2.0, 3.0], + [2.0, 4.0, 1.0, 5.0], + ], + dtype=np.float32, +) print(f"输入实例: {instances.shape}") result = med_core_rs.max_pooling_mil(instances) @@ -39,11 +44,14 @@ # Test 2: Mean pooling print("\n[测试 2] Mean Pooling MIL") -instances = np.array([ - [1.0, 2.0], - [3.0, 4.0], - [5.0, 6.0], -], dtype=np.float32) +instances = np.array( + [ + [1.0, 2.0], + [3.0, 4.0], + [5.0, 6.0], + ], + dtype=np.float32, +) result = med_core_rs.mean_pooling_mil(instances) print(f"Mean pooling 结果: {result}") @@ -53,11 +61,14 @@ # Test 3: Attention MIL print("\n[测试 3] Attention MIL") -instances = np.array([ - [1.0, 2.0], - [3.0, 4.0], - [5.0, 6.0], -], dtype=np.float32) +instances = np.array( + [ + [1.0, 2.0], + [3.0, 4.0], + [5.0, 6.0], + ], + dtype=np.float32, +) attention_weights = np.array([[0.5], [0.3], [0.2]], dtype=np.float32) result = med_core_rs.attention_mil(instances, attention_weights) @@ -70,8 +81,10 @@ # Test 4: Batch processing print("\n[测试 4] 批量 MIL 聚合") n_bags = 100 -bags = [np.random.rand(np.random.randint(10, 50), 512).astype(np.float32) - for _ in range(n_bags)] +bags = [ + np.random.rand(np.random.randint(10, 50), 512).astype(np.float32) + for _ in range(n_bags) +] print(f"批量大小: {n_bags} bags") print("特征维度: 512") @@ -80,14 +93,14 @@ start = time.time() result_max = med_core_rs.batch_mil_aggregation(bags, method="max") time_max = time.time() - start -print(f"Max pooling: {time_max*1000:.2f} ms ({n_bags/time_max:.1f} bags/秒)") +print(f"Max pooling: {time_max * 1000:.2f} ms ({n_bags / time_max:.1f} bags/秒)") assert result_max.shape == (n_bags, 512), f"形状不匹配: {result_max.shape}" # Mean pooling start = time.time() result_mean = med_core_rs.batch_mil_aggregation(bags, method="mean") time_mean = time.time() - start -print(f"Mean pooling: {time_mean*1000:.2f} ms ({n_bags/time_mean:.1f} bags/秒)") +print(f"Mean pooling: {time_mean * 1000:.2f} ms ({n_bags / time_mean:.1f} bags/秒)") assert result_mean.shape == (n_bags, 512), f"形状不匹配: {result_mean.shape}" print("✅ 批量处理正确") @@ -95,25 +108,29 @@ # Test 5: Performance comparison print("\n[测试 5] 性能对比 (Rust vs NumPy)") n_bags = 200 -bags = [np.random.rand(np.random.randint(20, 100), 512).astype(np.float32) - for _ in range(n_bags)] +bags = [ + np.random.rand(np.random.randint(20, 100), 512).astype(np.float32) + for _ in range(n_bags) +] # Rust batch processing start = time.time() rust_result = med_core_rs.batch_mil_aggregation(bags, method="max") rust_time = time.time() - start + # NumPy processing def numpy_batch_max(bags): return np.array([bag.max(axis=0) for bag in bags]) + start = time.time() numpy_result = numpy_batch_max(bags) numpy_time = time.time() - start speedup = numpy_time / rust_time -print(f"Rust 批量处理: {rust_time*1000:.2f} ms ({n_bags/rust_time:.1f} bags/秒)") -print(f"NumPy 批量处理: {numpy_time*1000:.2f} ms ({n_bags/numpy_time:.1f} bags/秒)") +print(f"Rust 批量处理: {rust_time * 1000:.2f} ms ({n_bags / rust_time:.1f} bags/秒)") +print(f"NumPy 批量处理: {numpy_time * 1000:.2f} ms ({n_bags / numpy_time:.1f} bags/秒)") print(f"🚀 加速比: {speedup:.2f}x") # Verify correctness @@ -122,12 +139,12 @@ def numpy_batch_max(bags): assert max_diff < 1e-5, "结果不匹配" print("✅ 结果正确性验证通过") -print("\n" + "="*60) +print("\n" + "=" * 60) print("🎉 所有测试通过！") -print("="*60) +print("=" * 60) print("\n📊 性能总结:") -print(f" - Max pooling (100 bags): {time_max*1000:.1f} ms") -print(f" - Mean pooling (100 bags): {time_mean*1000:.1f} ms") -print(f" - 吞吐量: {n_bags/rust_time:.1f} bags/秒") +print(f" - Max pooling (100 bags): {time_max * 1000:.1f} ms") +print(f" - Mean pooling (100 bags): {time_mean * 1000:.1f} ms") +print(f" - 吞吐量: {n_bags / rust_time:.1f} bags/秒") print(f" - 相比 NumPy 加速: {speedup:.2f}x") diff --git a/med_core_rs/test_percentile_analysis.py b/med_core_rs/test_percentile_analysis.py index fec4e06..6dec16c 100644 --- a/med_core_rs/test_percentile_analysis.py +++ b/med_core_rs/test_percentile_analysis.py @@ -11,14 +11,16 @@ def test_percentile_algorithms(): """对比不同大小数据的 percentile 计算""" - print("="*70) + print("=" * 70) print("Percentile 算法性能对比") - print("="*70) + print("=" * 70) - sizes = [256*256, 512*512, 1024*1024] + sizes = [256 * 256, 512 * 512, 1024 * 1024] for size in sizes: - print(f"\n数据大小: {size:,} 个元素 ({int(np.sqrt(size))}×{int(np.sqrt(size))})") + print( + f"\n数据大小: {size:,} 个元素 ({int(np.sqrt(size))}×{int(np.sqrt(size))})" + ) data = np.random.rand(size).astype(np.float32) * 255 @@ -31,7 +33,7 @@ def test_percentile_algorithms(): times.append(time.perf_counter() - start) numpy_time = np.mean(times) - print(f" NumPy percentile: {numpy_time*1000:6.2f} ms") + print(f" NumPy percentile: {numpy_time * 1000:6.2f} ms") # 纯 Python 排序 times = [] @@ -45,15 +47,16 @@ def test_percentile_algorithms(): times.append(time.perf_counter() - start) sort_time = np.mean(times) - print(f" NumPy sort: {sort_time*1000:6.2f} ms") - print(f" 排序 vs percentile: {sort_time/numpy_time:.2f}x") + print(f" NumPy sort: {sort_time * 1000:6.2f} ms") + print(f" 排序 vs percentile: {sort_time / numpy_time:.2f}x") + if __name__ == "__main__": test_percentile_algorithms() - print("\n" + "="*70) + print("\n" + "=" * 70) print("结论:") - print("="*70) + print("=" * 70) print("NumPy 的 percentile 函数已经高度优化（使用 quickselect）") print("我们的 Rust 实现需要：") print(" 1. 数据从 Python 传到 Rust（开销）") diff --git a/med_core_rs/test_quick.py b/med_core_rs/test_quick.py index fcd9ad6..c61e6be 100644 --- a/med_core_rs/test_quick.py +++ b/med_core_rs/test_quick.py @@ -16,6 +16,7 @@ normalize_intensity_minmax, normalize_intensity_percentile, ) + print("✅ 模块导入成功！") except ImportError as e: print(f"❌ 导入失败: {e}") @@ -30,16 +31,20 @@ normalized = normalize_intensity_minmax(image) elapsed = time.perf_counter() - start -print(f" 输出: {normalized.shape}, 范围: [{normalized.min():.2f}, {normalized.max():.2f}]") -print(f" ⏱️ 耗时: {elapsed*1000:.2f} ms") +print( + f" 输出: {normalized.shape}, 范围: [{normalized.min():.2f}, {normalized.max():.2f}]" +) +print(f" ⏱️ 耗时: {elapsed * 1000:.2f} ms") # 测试 2: Percentile 归一化 print("\n📊 测试 2: Percentile 归一化") start = time.perf_counter() normalized = normalize_intensity_percentile(image, 1.0, 99.0) elapsed = time.perf_counter() - start -print(f" 输出: {normalized.shape}, 范围: [{normalized.min():.2f}, {normalized.max():.2f}]") -print(f" ⏱️ 耗时: {elapsed*1000:.2f} ms") +print( + f" 输出: {normalized.shape}, 范围: [{normalized.min():.2f}, {normalized.max():.2f}]" +) +print(f" ⏱️ 耗时: {elapsed * 1000:.2f} ms") # 测试 3: 批量处理 print("\n📊 测试 3: 批量处理 (100 张图像)") @@ -51,9 +56,9 @@ elapsed = time.perf_counter() - start print(f" 输出: {normalized_batch.shape}") -print(f" ⏱️ 总耗时: {elapsed*1000:.2f} ms") -print(f" ⏱️ 单张耗时: {elapsed/100*1000:.2f} ms") -print(f" 📈 吞吐量: {100/elapsed:.1f} 张/秒") +print(f" ⏱️ 总耗时: {elapsed * 1000:.2f} ms") +print(f" ⏱️ 单张耗时: {elapsed / 100 * 1000:.2f} ms") +print(f" 📈 吞吐量: {100 / elapsed:.1f} 张/秒") # 测试 4: 中心裁剪 print("\n📊 测试 4: 中心裁剪") @@ -62,7 +67,7 @@ cropped = center_crop_rust(image, 224, 224) elapsed = time.perf_counter() - start print(f" 输入: {image.shape} -> 输出: {cropped.shape}") -print(f" ⏱️ 耗时: {elapsed*1000:.2f} ms") +print(f" ⏱️ 耗时: {elapsed * 1000:.2f} ms") # 测试 5: 正确性验证 print("\n📊 测试 5: 正确性验证") diff --git a/notebooks/quickstart.ipynb b/notebooks/quickstart.ipynb index ed5292c..4d004bd 100644 --- a/notebooks/quickstart.ipynb +++ b/notebooks/quickstart.ipynb @@ -13,8 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "print('MedFusion 已加载')" + "print(\"MedFusion 已加载\")" ] } ], diff --git a/scripts/analyze_coverage.py b/scripts/analyze_coverage.py index d0cdb85..fa2c493 100644 --- a/scripts/analyze_coverage.py +++ b/scripts/analyze_coverage.py @@ -14,10 +14,10 @@ def find_python_files(directory): python_files = [] for root, dirs, files in os.walk(directory): # Skip __pycache__ and hidden directories - dirs[:] = [d for d in dirs if not d.startswith('__') and not d.startswith('.')] + dirs[:] = [d for d in dirs if not d.startswith("__") and not d.startswith(".")] for file in files: - if file.endswith('.py') and not file.startswith('__'): + if file.endswith(".py") and not file.startswith("__"): python_files.append(os.path.join(root, file)) return sorted(python_files) @@ -28,7 +28,7 @@ def find_test_files(directory): test_files = [] for root, _dirs, files in os.walk(directory): for file in files: - if file.startswith('test_') and file.endswith('.py'): + if file.startswith("test_") and file.endswith(".py"): test_files.append(os.path.join(root, file)) return sorted(test_files) @@ -37,15 +37,15 @@ def find_test_files(directory): def get_module_name(filepath, base_dir): """Convert file path to module name.""" rel_path = os.path.relpath(filepath, base_dir) - module = rel_path.replace(os.sep, '.').replace('.py', '') + module = rel_path.replace(os.sep, ".").replace(".py", "") return module def analyze_coverage(): """Analyze test coverage.""" base_dir = Path(__file__).parent.parent - med_core_dir = base_dir / 'med_core' - tests_dir = base_dir / 'tests' + med_core_dir = base_dir / "med_core" + tests_dir = base_dir / "tests" print("=" * 80) print("MedFusion Test Coverage Analysis") @@ -76,8 +76,8 @@ def analyze_coverage(): filename = os.path.basename(filepath) # Extract module name from test file # e.g., test_backbones.py -> backbones - if filename.startswith('test_'): - module = filename[5:].replace('.py', '') + if filename.startswith("test_"): + module = filename[5:].replace(".py", "") if module not in test_modules: test_modules[module] = [] test_modules[module].append(filepath) @@ -109,7 +109,9 @@ def analyze_coverage(): print("-" * 80) coverage_pct = (total_with_tests / len(modules) * 100) if modules else 0 - print(f"Overall module coverage: {total_with_tests}/{len(modules)} ({coverage_pct:.1f}%)") + print( + f"Overall module coverage: {total_with_tests}/{len(modules)} ({coverage_pct:.1f}%)" + ) print() # Identify modules needing tests @@ -143,7 +145,7 @@ def analyze_coverage(): return needs_tests -if __name__ == '__main__': +if __name__ == "__main__": needs_tests = analyze_coverage() if needs_tests: diff --git a/scripts/ci_diagnostic.py b/scripts/ci_diagnostic.py index 4cad98c..19ec9a6 100644 --- a/scripts/ci_diagnostic.py +++ b/scripts/ci_diagnostic.py @@ -6,21 +6,20 @@ import subprocess import sys from pathlib import Path -from typing import List, Tuple class Colors: - GREEN = '\033[0;32m' - RED = '\033[0;31m' - YELLOW = '\033[1;33m' - BLUE = '\033[0;34m' - NC = '\033[0m' + GREEN = "\033[0;32m" + RED = "\033[0;31m" + YELLOW = "\033[1;33m" + BLUE = "\033[0;34m" + NC = "\033[0m" def print_header(text: str): print(f"\n{'=' * 80}") print(f" {text}") - print('=' * 80) + print("=" * 80) def print_step(text: str): @@ -39,15 +38,11 @@ def print_warning(text: str): print(f"{Colors.YELLOW}⚠ {text}{Colors.NC}") -def run_command(cmd: List[str], check: bool = True) -> Tuple[bool, str]: +def run_command(cmd: list[str], check: bool = True) -> tuple[bool, str]: """运行命令并返回结果""" try: result = subprocess.run( - cmd, - capture_output=True, - text=True, - check=check, - timeout=60 + cmd, capture_output=True, text=True, check=check, timeout=60 ) return True, result.stdout + result.stderr except subprocess.CalledProcessError as e: @@ -167,8 +162,7 @@ def check_code_quality(): # Ruff 检查 print_step("Ruff Linting") success, output = run_command( - ["ruff", "check", "med_core/", "tests/", "--output-format=text"], - check=False + ["ruff", "check", "med_core/", "tests/", "--output-format=text"], check=False ) if success: print_success("Ruff 检查通过") @@ -179,8 +173,7 @@ def check_code_quality(): # Ruff 格式检查 print_step("Ruff Format") success, output = run_command( - ["ruff", "format", "--check", "med_core/", "tests/"], - check=False + ["ruff", "format", "--check", "med_core/", "tests/"], check=False ) if success: print_success("代码格式正确") @@ -191,8 +184,7 @@ def check_code_quality(): # mypy 类型检查 print_step("mypy 类型检查") success, output = run_command( - ["mypy", "med_core/", "--ignore-missing-imports"], - check=False + ["mypy", "med_core/", "--ignore-missing-imports"], check=False ) if success: print_success("类型检查通过") @@ -221,8 +213,7 @@ def check_tests(): # 运行测试 print_step("运行测试") success, output = run_command( - [sys.executable, "-m", "pytest", "tests/", "-v", "--tb=short"], - check=False + [sys.executable, "-m", "pytest", "tests/", "-v", "--tb=short"], check=False ) if success: print_success("测试通过") @@ -255,8 +246,7 @@ def check_scripts(): # 检查语法 success, output = run_command( - [sys.executable, "-m", "py_compile", script], - check=False + [sys.executable, "-m", "py_compile", script], check=False ) if success: print_success(f"{script}") @@ -287,8 +277,17 @@ def check_docker(): # 尝试验证 Dockerfile 语法 print_step("验证 Dockerfile 语法") success, output = run_command( - ["docker", "build", "--no-cache", "-f", "Dockerfile", "-t", "medfusion:test", "."], - check=False + [ + "docker", + "build", + "--no-cache", + "-f", + "Dockerfile", + "-t", + "medfusion:test", + ".", + ], + check=False, ) if success: print_success("Docker 构建成功") @@ -320,8 +319,7 @@ def check_examples(): for example in example_files: success, output = run_command( - [sys.executable, "-m", "py_compile", str(example)], - check=False + [sys.executable, "-m", "py_compile", str(example)], check=False ) if success: print_success(f"{example.name}") diff --git a/scripts/compare_benchmarks.py b/scripts/compare_benchmarks.py index 20bc2bc..ffc43cc 100644 --- a/scripts/compare_benchmarks.py +++ b/scripts/compare_benchmarks.py @@ -46,7 +46,9 @@ def compare_dict(base_dict, curr_dict, path=""): if isinstance(base_val, dict) and isinstance(curr_val, dict): # 递归比较 compare_dict(base_val, curr_val, f"{path}.{key}" if path else key) - elif isinstance(base_val, (int, float)) and isinstance(curr_val, (int, float)): + elif isinstance(base_val, (int, float)) and isinstance( + curr_val, (int, float) + ): # 比较数值 if key == "throughput": # 吞吐量：越高越好 @@ -97,7 +99,9 @@ def print_comparison(comparisons, verbose=False): # 按是否回归分组 regressions = [c for c in comparisons if c["is_regression"]] - improvements = [c for c in comparisons if c["change"] > 5 and not c["is_regression"]] + improvements = [ + c for c in comparisons if c["change"] > 5 and not c["is_regression"] + ] stable = [c for c in comparisons if abs(c["change"]) <= 5] # 打印回归 diff --git a/scripts/full_workflow_test.py b/scripts/full_workflow_test.py index 762e586..e0237bc 100644 --- a/scripts/full_workflow_test.py +++ b/scripts/full_workflow_test.py @@ -114,7 +114,7 @@ def main(): config.data.image_dir = str(data_dir / "images") config.logging.output_dir = "outputs/full_workflow_test" - logger.info(f"✅ 配置加载成功") + logger.info("✅ 配置加载成功") logger.info(f" - 实验名称: {config.experiment_name}") logger.info(f" - 模型: {config.model.vision.backbone}") logger.info(f" - 融合方式: {config.model.fusion.fusion_type}") @@ -155,7 +155,7 @@ def main(): num_workers=0, # 避免多进程问题 ) - logger.info(f"✅ 数据集准备完成") + logger.info("✅ 数据集准备完成") logger.info(f" - 训练集: {len(train_ds)} 样本") logger.info(f" - 验证集: {len(val_ds)} 样本") logger.info(f" - 测试集: {len(test_ds)} 样本") @@ -194,7 +194,7 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) - logger.info(f"✅ 模型创建完成") + logger.info("✅ 模型创建完成") logger.info(f" - 设备: {device}") logger.info(f" - 参数量: {sum(p.numel() for p in model.parameters()):,}") @@ -204,7 +204,9 @@ def main(): logger.info("=" * 60) criterion = nn.CrossEntropyLoss() - optimizer = optim.Adam(model.parameters(), lr=config.training.optimizer.learning_rate) + optimizer = optim.Adam( + model.parameters(), lr=config.training.optimizer.learning_rate + ) for epoch in range(config.training.num_epochs): model.train() @@ -238,9 +240,11 @@ def main(): train_loss = total_loss / len(dataloaders["train"]) train_acc = 100.0 * correct / total - logger.info(f"Epoch {epoch + 1}/{config.training.num_epochs} - Loss: {train_loss:.4f}, Acc: {train_acc:.2f}%") + logger.info( + f"Epoch {epoch + 1}/{config.training.num_epochs} - Loss: {train_loss:.4f}, Acc: {train_acc:.2f}%" + ) - logger.info(f"✅ 训练完成") + logger.info("✅ 训练完成") # 6. 评估模型 logger.info("\n" + "=" * 60) @@ -277,7 +281,7 @@ def main(): y_prob=all_probs, ) - logger.info(f"✅ 评估完成") + logger.info("✅ 评估完成") logger.info(f" - Accuracy: {metrics['accuracy']:.4f}") logger.info(f" - AUC: {metrics['auc']:.4f}") logger.info(f" - F1 Score: {metrics['f1']:.4f}") @@ -313,10 +317,10 @@ def main(): logger.info("\n" + "=" * 60) logger.info("🎉 完整工作流测试完成！") logger.info("=" * 60) - logger.info(f"测试结果:") - logger.info(f" ✅ 配置加载: 成功") + logger.info("测试结果:") + logger.info(" ✅ 配置加载: 成功") logger.info(f" ✅ 数据准备: 成功 ({len(full_dataset)} 样本)") - logger.info(f" ✅ 模型创建: 成功") + logger.info(" ✅ 模型创建: 成功") logger.info(f" ✅ 模型训练: 成功 ({config.training.num_epochs} epochs)") logger.info(f" ✅ 模型评估: 成功 (Acc: {metrics['accuracy']:.2%})") logger.info(f" {report_status}: 报告生成") diff --git a/scripts/generate_mock_data.py b/scripts/generate_mock_data.py index 5321265..05a9a17 100644 --- a/scripts/generate_mock_data.py +++ b/scripts/generate_mock_data.py @@ -51,7 +51,9 @@ def generate_mock_images(output_dir: Path, num_images: int = 10, image_size: int return image_paths -def generate_mock_metadata(output_dir: Path, image_paths: list[str], num_samples: int = 10): +def generate_mock_metadata( + output_dir: Path, image_paths: list[str], num_samples: int = 10 +): """ Generate mock patient metadata CSV. @@ -109,7 +111,9 @@ def main(): print(f"Output directory: {output_dir.absolute()}") # Generate images - print(f"\n1. Generating {args.num_samples} images ({args.image_size}x{args.image_size})...") + print( + f"\n1. Generating {args.num_samples} images ({args.image_size}x{args.image_size})..." + ) image_paths = generate_mock_images( output_dir, num_images=args.num_samples, image_size=args.image_size ) @@ -117,7 +121,9 @@ def main(): # Generate metadata print("\n2. Generating metadata CSV...") - csv_path = generate_mock_metadata(output_dir, image_paths, num_samples=args.num_samples) + csv_path = generate_mock_metadata( + output_dir, image_paths, num_samples=args.num_samples + ) print(f" ✓ Metadata saved to {csv_path}") # Summary @@ -126,12 +132,14 @@ def main(): print("=" * 60) print("\nGenerated files:") print(f" - {args.num_samples} images in {output_dir / 'images'}") - print(" - metadata.csv with columns: patient_id, image_path, age, gender, diagnosis") + print( + " - metadata.csv with columns: patient_id, image_path, age, gender, diagnosis" + ) print("\nTo use this data:") print(" 1. Update your config YAML:") print(" data:") - print(f" csv_path: \"{csv_path}\"") - print(f" image_dir: \"{output_dir}\"") + print(f' csv_path: "{csv_path}"') + print(f' image_dir: "{output_dir}"') print(" 2. Run training:") print(" uv run med-train --config configs/your_config.yaml") diff --git a/scripts/generate_test_stubs.py b/scripts/generate_test_stubs.py index 28c61cd..0b278a6 100644 --- a/scripts/generate_test_stubs.py +++ b/scripts/generate_test_stubs.py @@ -36,16 +36,14 @@ def test_basic_functionality(self): def create_test_file(module_name, test_path): """Create a basic test file for a module.""" - class_name = ''.join(word.capitalize() for word in module_name.split('_')) + class_name = "".join(word.capitalize() for word in module_name.split("_")) import_path = f"med_core.{module_name}" content = BASIC_TEST_TEMPLATE.format( - module_name=module_name, - class_name=class_name, - import_path=import_path + module_name=module_name, class_name=class_name, import_path=import_path ) - with open(test_path, 'w') as f: + with open(test_path, "w") as f: f.write(content) print(f"✅ Created: {test_path}") @@ -54,15 +52,15 @@ def create_test_file(module_name, test_path): def main(): """Generate test files for modules without tests.""" base_dir = Path(__file__).parent.parent - tests_dir = base_dir / 'tests' + tests_dir = base_dir / "tests" # Modules that need tests (from coverage analysis) modules_needing_tests = [ - 'extractors', - 'models', - 'shared', - 'utils', - 'visualization', + "extractors", + "models", + "shared", + "utils", + "visualization", ] print("=" * 80) @@ -85,5 +83,5 @@ def main(): print("=" * 80) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/quick_ci_test.py b/scripts/quick_ci_test.py index 9c7401e..cc7f038 100644 --- a/scripts/quick_ci_test.py +++ b/scripts/quick_ci_test.py @@ -2,6 +2,7 @@ """ 简化的 CI 本地测试脚本 """ + import subprocess import sys from pathlib import Path diff --git a/scripts/quick_simulation_test.py b/scripts/quick_simulation_test.py index 786e4bb..2508dea 100644 --- a/scripts/quick_simulation_test.py +++ b/scripts/quick_simulation_test.py @@ -14,13 +14,11 @@ """ import logging -import sys import time from pathlib import Path import numpy as np import pandas as pd -import torch from PIL import Image # 设置日志 diff --git a/scripts/simple_test.py b/scripts/simple_test.py index 2590e40..472626e 100644 --- a/scripts/simple_test.py +++ b/scripts/simple_test.py @@ -23,7 +23,9 @@ from med_core.backbones import create_tabular_backbone, create_vision_backbone from med_core.fusion import MultiModalFusionModel, create_fusion_module -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) logger = logging.getLogger(__name__) # 测试记录 diff --git a/scripts/smoke_test.py b/scripts/smoke_test.py index 0baac67..89481f6 100644 --- a/scripts/smoke_test.py +++ b/scripts/smoke_test.py @@ -54,7 +54,9 @@ def analyze_model_architecture(model: nn.Module, config): print(f" Frozen: {config.model.vision.freeze_backbone}") print(f" Output Dim: {vision.output_dim}") print(f" Parameters: {sum(p.numel() for p in vision.parameters()):,}") - print(f" Trainable: {sum(p.numel() for p in vision.parameters() if p.requires_grad):,}") + print( + f" Trainable: {sum(p.numel() for p in vision.parameters() if p.requires_grad):,}" + ) # Tabular Backbone print_subsection("Tabular Backbone") @@ -63,7 +65,9 @@ def analyze_model_architecture(model: nn.Module, config): print(f" Hidden Dims: {config.model.tabular.hidden_dims}") print(f" Output Dim: {tabular.output_dim}") print(f" Parameters: {sum(p.numel() for p in tabular.parameters()):,}") - print(f" Trainable: {sum(p.numel() for p in tabular.parameters() if p.requires_grad):,}") + print( + f" Trainable: {sum(p.numel() for p in tabular.parameters() if p.requires_grad):,}" + ) # Fusion Module print_subsection("Fusion Module") @@ -122,9 +126,13 @@ def test_dimension_alignment(model: nn.Module, sample_batch): # Check auxiliary heads if present if "vision_logits" in outputs: - print(f" Vision Logits (aux): {tuple(outputs['vision_logits'].shape)}") + print( + f" Vision Logits (aux): {tuple(outputs['vision_logits'].shape)}" + ) if "tabular_logits" in outputs: - print(f" Tabular Logits (aux): {tuple(outputs['tabular_logits'].shape)}") + print( + f" Tabular Logits (aux): {tuple(outputs['tabular_logits'].shape)}" + ) else: logits = outputs print_subsection("Forward Pass Results") @@ -197,7 +205,9 @@ def test_gradient_flow(model: nn.Module, sample_batch, criterion): return False -def test_training_loop(model, train_loader, val_loader, criterion, optimizer, num_epochs=3): +def test_training_loop( + model, train_loader, val_loader, criterion, optimizer, num_epochs=3 +): """Test training loop and loss convergence.""" print_section("TRAINING LOOP TEST") @@ -258,7 +268,9 @@ def test_training_loop(model, train_loader, val_loader, criterion, optimizer, nu avg_val_loss = epoch_val_loss / num_val_batches val_losses.append(avg_val_loss) - print(f" Epoch {epoch + 1}/{num_epochs}: Train Loss = {avg_train_loss:.4f}, Val Loss = {avg_val_loss:.4f}") + print( + f" Epoch {epoch + 1}/{num_epochs}: Train Loss = {avg_train_loss:.4f}, Val Loss = {avg_val_loss:.4f}" + ) # Analyze loss trends print_subsection("Loss Analysis") @@ -291,7 +303,9 @@ def generate_report(results: dict): status = "✓ PASS" if passed else "✗ FAIL" print(f" {status}: {test_name}") - print(f"\n Overall Status: {'✓ ALL TESTS PASSED' if all_passed else '✗ SOME TESTS FAILED'}") + print( + f"\n Overall Status: {'✓ ALL TESTS PASSED' if all_passed else '✗ SOME TESTS FAILED'}" + ) if all_passed: print("\n 🎉 Framework is ready for production use!") @@ -302,7 +316,9 @@ def generate_report(results: dict): def main(): - parser = argparse.ArgumentParser(description="Run smoke test for Med-Core framework") + parser = argparse.ArgumentParser( + description="Run smoke test for Med-Core framework" + ) parser.add_argument( "--config", type=str, @@ -426,7 +442,12 @@ def main(): # Test 4: Training Loop optimizer = torch.optim.Adam(model.parameters(), lr=0.001) train_losses, val_losses = test_training_loop( - model, dataloaders["train"], dataloaders["val"], criterion, optimizer, num_epochs=args.epochs + model, + dataloaders["train"], + dataloaders["val"], + criterion, + optimizer, + num_epochs=args.epochs, ) results["Training Loop"] = True diff --git a/scripts/test_attention_supervision.py b/scripts/test_attention_supervision.py index ace3d7b..1882487 100644 --- a/scripts/test_attention_supervision.py +++ b/scripts/test_attention_supervision.py @@ -81,6 +81,7 @@ def generate_synthetic_data(output_dir: Path, num_samples: int = 200): data.append(record) import pandas as pd + df = pd.DataFrame(data) csv_path = output_dir / "dataset.csv" df.to_csv(csv_path, index=False) @@ -89,11 +90,13 @@ def generate_synthetic_data(output_dir: Path, num_samples: int = 200): return csv_path -def train_model(model, train_loader, val_loader, device, num_epochs=5, model_name="Model"): +def train_model( + model, train_loader, val_loader, device, num_epochs=5, model_name="Model" +): """训练模型""" - logger.info(f"\n{'='*60}") + logger.info(f"\n{'=' * 60}") logger.info(f"训练 {model_name}") - logger.info(f"{'='*60}") + logger.info(f"{'=' * 60}") criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) @@ -163,8 +166,8 @@ def train_model(model, train_loader, val_loader, device, num_epochs=5, model_nam logger.info( f"Epoch {epoch + 1}/{num_epochs} - " - f"Train Loss: {train_loss/len(train_loader):.4f}, Train Acc: {train_acc:.2f}% | " - f"Val Loss: {val_loss/len(val_loader):.4f}, Val Acc: {val_acc:.2f}%" + f"Train Loss: {train_loss / len(train_loader):.4f}, Train Acc: {train_acc:.2f}% | " + f"Val Loss: {val_loss / len(val_loader):.4f}, Val Acc: {val_acc:.2f}%" ) logger.info(f"✅ {model_name} 训练完成 - 最佳验证准确率: {best_val_acc:.2f}%") @@ -173,9 +176,9 @@ def train_model(model, train_loader, val_loader, device, num_epochs=5, model_nam def evaluate_model(model, test_loader, device, model_name="Model"): """评估模型""" - logger.info(f"\n{'='*60}") + logger.info(f"\n{'=' * 60}") logger.info(f"评估 {model_name}") - logger.info(f"{'='*60}") + logger.info(f"{'=' * 60}") model.eval() all_preds = [] @@ -207,7 +210,7 @@ def evaluate_model(model, test_loader, device, model_name="Model"): y_prob=all_probs, ) - logger.info(f"测试结果:") + logger.info("测试结果:") logger.info(f" - Accuracy: {metrics['accuracy']:.4f}") logger.info(f" - AUC: {metrics['auc']:.4f}") logger.info(f" - F1 Score: {metrics['f1']:.4f}") @@ -264,7 +267,7 @@ def main(): num_workers=0, ) - logger.info(f"✅ 数据集准备完成") + logger.info("✅ 数据集准备完成") logger.info(f" - 训练集: {len(train_ds)} 样本") logger.info(f" - 验证集: {len(val_ds)} 样本") logger.info(f" - 测试集: {len(test_ds)} 样本") @@ -303,7 +306,7 @@ def main(): num_classes=2, ) - logger.info(f"✅ 基线模型创建完成") + logger.info("✅ 基线模型创建完成") # 4. 创建注意力监督模型 logger.info("\n" + "=" * 60) @@ -337,7 +340,7 @@ def main(): num_classes=2, ) - logger.info(f"✅ 注意力模型创建完成") + logger.info("✅ 注意力模型创建完成") # 5. 训练基线模型 best_val_acc_baseline = train_model( @@ -380,22 +383,24 @@ def main(): logger.info("📊 对比结果") logger.info("=" * 60) - logger.info(f"\n验证集最佳准确率:") + logger.info("\n验证集最佳准确率:") logger.info(f" 基线模型: {best_val_acc_baseline:.2f}%") logger.info(f" 注意力模型: {best_val_acc_attention:.2f}%") logger.info(f" 提升: {best_val_acc_attention - best_val_acc_baseline:+.2f}%") - logger.info(f"\n测试集准确率:") + logger.info("\n测试集准确率:") logger.info(f" 基线模型: {metrics_baseline['accuracy']:.4f}") logger.info(f" 注意力模型: {metrics_attention['accuracy']:.4f}") - logger.info(f" 提升: {metrics_attention['accuracy'] - metrics_baseline['accuracy']:+.4f}") + logger.info( + f" 提升: {metrics_attention['accuracy'] - metrics_baseline['accuracy']:+.4f}" + ) - logger.info(f"\n测试集 AUC:") + logger.info("\n测试集 AUC:") logger.info(f" 基线模型: {metrics_baseline['auc']:.4f}") logger.info(f" 注意力模型: {metrics_attention['auc']:.4f}") logger.info(f" 提升: {metrics_attention['auc'] - metrics_baseline['auc']:+.4f}") - logger.info(f"\n测试集 F1 Score:") + logger.info("\n测试集 F1 Score:") logger.info(f" 基线模型: {metrics_baseline['f1']:.4f}") logger.info(f" 注意力模型: {metrics_attention['f1']:.4f}") logger.info(f" 提升: {metrics_attention['f1'] - metrics_baseline['f1']:+.4f}") @@ -405,18 +410,18 @@ def main(): logger.info("💡 结论") logger.info("=" * 60) - improvement = metrics_attention['accuracy'] - metrics_baseline['accuracy'] + improvement = metrics_attention["accuracy"] - metrics_baseline["accuracy"] if improvement > 0.02: # 提升超过 2% logger.info("✅ 注意力监督有明显效果，建议保留") - logger.info(f" 准确率提升: {improvement:.4f} ({improvement*100:.2f}%)") + logger.info(f" 准确率提升: {improvement:.4f} ({improvement * 100:.2f}%)") elif improvement > 0: logger.info("⚠️ 注意力监督有轻微提升，但不明显") - logger.info(f" 准确率提升: {improvement:.4f} ({improvement*100:.2f}%)") + logger.info(f" 准确率提升: {improvement:.4f} ({improvement * 100:.2f}%)") logger.info(" 建议：在真实数据上测试后再决定") else: logger.info("❌ 注意力监督没有提升，甚至可能降低性能") - logger.info(f" 准确率变化: {improvement:.4f} ({improvement*100:.2f}%)") + logger.info(f" 准确率变化: {improvement:.4f} ({improvement * 100:.2f}%)") logger.info(" 建议：删除注意力监督模块（2,678 行代码）") logger.info("\n注意：这是合成数据测试，真实数据可能有不同结果") diff --git a/scripts/test_enhanced_report.py b/scripts/test_enhanced_report.py index 1bdd944..ddd1ead 100644 --- a/scripts/test_enhanced_report.py +++ b/scripts/test_enhanced_report.py @@ -14,7 +14,6 @@ import matplotlib.pyplot as plt import numpy as np -import torch from sklearn.metrics import roc_curve # Add project root to path @@ -28,8 +27,7 @@ ) logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) logger = logging.getLogger(__name__) @@ -42,10 +40,7 @@ def create_sample_metrics(accuracy: float = 0.95, n_samples: int = 100): n_negative = n_samples - n_positive # True labels - y_true = np.concatenate([ - np.ones(n_positive), - np.zeros(n_negative) - ]) + y_true = np.concatenate([np.ones(n_positive), np.zeros(n_negative)]) # Predictions with specified accuracy y_pred = y_true.copy() @@ -69,18 +64,18 @@ def create_high_res_roc_plot(y_true, y_scores, output_path: Path, dpi: int = 300 fpr, tpr, _ = roc_curve(y_true, y_scores) plt.figure(figsize=(8, 6)) - plt.plot(fpr, tpr, linewidth=2, label='ROC Curve') - plt.plot([0, 1], [0, 1], 'k--', linewidth=1, label='Random Classifier') + plt.plot(fpr, tpr, linewidth=2, label="ROC Curve") + plt.plot([0, 1], [0, 1], "k--", linewidth=1, label="Random Classifier") plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) - plt.xlabel('False Positive Rate', fontsize=12) - plt.ylabel('True Positive Rate', fontsize=12) - plt.title('Receiver Operating Characteristic (ROC) Curve', fontsize=14) + plt.xlabel("False Positive Rate", fontsize=12) + plt.ylabel("True Positive Rate", fontsize=12) + plt.title("Receiver Operating Characteristic (ROC) Curve", fontsize=14) plt.legend(loc="lower right", fontsize=11) plt.grid(True, alpha=0.3) plt.tight_layout() - plt.savefig(output_path, dpi=dpi, bbox_inches='tight') + plt.savefig(output_path, dpi=dpi, bbox_inches="tight") plt.close() logger.info(f"✅ High-resolution ROC curve saved: {output_path}") @@ -88,34 +83,43 @@ def create_high_res_roc_plot(y_true, y_scores, output_path: Path, dpi: int = 300 def create_high_res_confusion_matrix(metrics, output_path: Path, dpi: int = 300): """Create high-resolution confusion matrix plot.""" - cm = np.array([ - [metrics.true_negatives, metrics.false_positives], - [metrics.false_negatives, metrics.true_positives] - ]) + cm = np.array( + [ + [metrics.true_negatives, metrics.false_positives], + [metrics.false_negatives, metrics.true_positives], + ] + ) fig, ax = plt.subplots(figsize=(8, 6)) - im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues) + im = ax.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues) ax.figure.colorbar(im, ax=ax) - ax.set(xticks=np.arange(cm.shape[1]), - yticks=np.arange(cm.shape[0]), - xticklabels=['Negative', 'Positive'], - yticklabels=['Negative', 'Positive'], - title='Confusion Matrix', - ylabel='True Label', - xlabel='Predicted Label') + ax.set( + xticks=np.arange(cm.shape[1]), + yticks=np.arange(cm.shape[0]), + xticklabels=["Negative", "Positive"], + yticklabels=["Negative", "Positive"], + title="Confusion Matrix", + ylabel="True Label", + xlabel="Predicted Label", + ) # Add text annotations - thresh = cm.max() / 2. + thresh = cm.max() / 2.0 for i in range(cm.shape[0]): for j in range(cm.shape[1]): - ax.text(j, i, format(cm[i, j], 'd'), - ha="center", va="center", - color="white" if cm[i, j] > thresh else "black", - fontsize=14) + ax.text( + j, + i, + format(cm[i, j], "d"), + ha="center", + va="center", + color="white" if cm[i, j] > thresh else "black", + fontsize=14, + ) plt.tight_layout() - plt.savefig(output_path, dpi=dpi, bbox_inches='tight') + plt.savefig(output_path, dpi=dpi, bbox_inches="tight") plt.close() logger.info(f"✅ High-resolution confusion matrix saved: {output_path}") @@ -183,7 +187,9 @@ def test_statistical_comparison(): # Generate baseline and improved metrics baseline_metrics, _, _ = create_sample_metrics(accuracy=0.85, n_samples=200) - improved_metrics, y_true, y_scores = create_sample_metrics(accuracy=0.92, n_samples=200) + improved_metrics, y_true, y_scores = create_sample_metrics( + accuracy=0.92, n_samples=200 + ) logger.info(f"Baseline 准确率: {baseline_metrics.accuracy:.4f}") logger.info(f"改进后准确率: {improved_metrics.accuracy:.4f}") @@ -213,7 +219,7 @@ def test_statistical_comparison(): logger.info(f"✅ 报告生成成功: {report_path}") # Read and display statistical tests section - with open(report_path, 'r', encoding='utf-8') as f: + with open(report_path, encoding="utf-8") as f: content = f.read() if "Statistical Significance Tests" in content: logger.info("✅ 统计检验部分已添加到报告") @@ -271,7 +277,7 @@ def test_latex_output(): logger.info(f"✅ LaTeX 报告生成成功: {latex_path}") # Display LaTeX table - with open(latex_path, 'r', encoding='utf-8') as f: + with open(latex_path, encoding="utf-8") as f: content = f.read() # Extract table start = content.find("\\begin{table}") diff --git a/scripts/test_pluggability.py b/scripts/test_pluggability.py index bee4a5d..87d3561 100644 --- a/scripts/test_pluggability.py +++ b/scripts/test_pluggability.py @@ -28,7 +28,11 @@ if "PYTEST_CURRENT_TEST" in os.environ or "pytest" in sys.modules: try: import pytest # type: ignore - pytest.skip("Script-style tests are skipped during pytest collection", allow_module_level=True) + + pytest.skip( + "Script-style tests are skipped during pytest collection", + allow_module_level=True, + ) except Exception: # If pytest cannot be imported for some reason, simply exit early to avoid collection. # This branch is conservative: in normal script execution pytest won't be in sys.modules @@ -285,8 +289,8 @@ def main(): failed = sum(1 for r in results if r["status"] == "FAIL") print(f"\n Total Tests: {total_tests}") - print(f" Passed: {passed} ({passed/total_tests*100:.1f}%)") - print(f" Failed: {failed} ({failed/total_tests*100:.1f}%)") + print(f" Passed: {passed} ({passed / total_tests * 100:.1f}%)") + print(f" Failed: {failed} ({failed / total_tests * 100:.1f}%)") if failed > 0: print("\n Failed Combinations:") @@ -302,14 +306,26 @@ def main(): for vision in vision_backbones: vision_results = [r for r in results if r["vision"] == vision] vision_passed = sum(1 for r in vision_results if r["status"] == "PASS") - status = "PASS" if vision_passed == len(vision_results) else "PARTIAL" if vision_passed > 0 else "FAIL" + status = ( + "PASS" + if vision_passed == len(vision_results) + else "PARTIAL" + if vision_passed > 0 + else "FAIL" + ) print_result("Vision", vision, status) print("\n Fusion Strategies:") for fusion in fusion_types: fusion_results = [r for r in results if r["fusion"] == fusion] fusion_passed = sum(1 for r in fusion_results if r["status"] == "PASS") - status = "PASS" if fusion_passed == len(fusion_results) else "PARTIAL" if fusion_passed > 0 else "FAIL" + status = ( + "PASS" + if fusion_passed == len(fusion_results) + else "PARTIAL" + if fusion_passed > 0 + else "FAIL" + ) print_result("Fusion", fusion, status) print("\n Tabular Configurations:") @@ -317,7 +333,13 @@ def main(): tabular_str = str(tabular) tabular_results = [r for r in results if r["tabular"] == tabular_str] tabular_passed = sum(1 for r in tabular_results if r["status"] == "PASS") - status = "PASS" if tabular_passed == len(tabular_results) else "PARTIAL" if tabular_passed > 0 else "FAIL" + status = ( + "PASS" + if tabular_passed == len(tabular_results) + else "PARTIAL" + if tabular_passed > 0 + else "FAIL" + ) print_result("Tabular", tabular_str, status) # Final verdict diff --git a/scripts/verify_ci_fixes.py b/scripts/verify_ci_fixes.py index 970c7e4..52d575a 100644 --- a/scripts/verify_ci_fixes.py +++ b/scripts/verify_ci_fixes.py @@ -2,6 +2,7 @@ """ CI 配置验证脚本 - 检查所有修复是否正确应用 """ + import sys from pathlib import Path @@ -14,13 +15,13 @@ def check_pyproject(): issues = [] # 检查 dev 依赖位置 - if '[project.optional-dependencies]' in content and 'dev = [' in content: + if "[project.optional-dependencies]" in content and "dev = [" in content: print(" ✓ dev 依赖在正确位置 (optional-dependencies)") else: issues.append("dev 依赖配置错误") # 检查是否还有 dependency-groups - if '[dependency-groups]' in content: + if "[dependency-groups]" in content: issues.append("仍然存在 [dependency-groups] 节") else: print(" ✓ 已移除 [dependency-groups]") @@ -48,13 +49,13 @@ def check_ci_workflow(): issues.append("测试矩阵配置不正确") # 检查安全工具版本 - if 'bandit==1.7.5 safety==3.0.1' in content: + if "bandit==1.7.5 safety==3.0.1" in content: print(" ✓ 安全工具版本已固定") else: issues.append("安全工具版本未固定") # 检查 Docker 平台 - if 'platforms: linux/amd64' in content: + if "platforms: linux/amd64" in content: print(" ✓ Docker 构建限制为单平台") else: issues.append("Docker 平台配置不正确") @@ -70,13 +71,13 @@ def check_setup_action(): issues = [] # 检查缓存配置 - if 'actions/cache@v4' in content: + if "actions/cache@v4" in content: print(" ✓ 已添加依赖缓存") else: issues.append("缺少缓存配置") # 检查缓存路径 - if '~/.cache/uv' in content: + if "~/.cache/uv" in content: print(" ✓ uv 缓存路径正确") else: issues.append("uv 缓存路径不正确") @@ -158,7 +159,9 @@ def main(): if passed == total: print("\n✓ 所有配置修复已正确应用！") print("\n下一步:") - print(" 1. 提交更改: git add -A && git commit -m 'fix: CI configuration issues'") + print( + " 1. 提交更改: git add -A && git commit -m 'fix: CI configuration issues'" + ) print(" 2. 推送到远程: git push") print(" 3. 观察 GitHub Actions 运行结果") return 0 diff --git a/src/data/dataset.py b/src/data/dataset.py index 306319d..8be6b06 100644 --- a/src/data/dataset.py +++ b/src/data/dataset.py @@ -25,6 +25,7 @@ # Optional DICOM support try: import pydicom + PYDICOM_AVAILABLE = True except ImportError: PYDICOM_AVAILABLE = False @@ -51,7 +52,9 @@ def __init__( label_column: str = "label", feature_columns: list[str] | None = None, transform: Callable | None = None, - intensity_norm: Literal["minmax", "zscore", "percentile", "none"] = "percentile", + intensity_norm: Literal[ + "minmax", "zscore", "percentile", "none" + ] = "percentile", percentile_range: tuple[float, float] = (1.0, 99.0), target_size: tuple[int, int] | None = None, ): @@ -86,7 +89,9 @@ def __init__( # Auto-detect feature columns if not provided if feature_columns is None: exclude_cols = {image_column, label_column, "patient_id", "study_id"} - feature_columns = [col for col in self.df.columns if col not in exclude_cols] + feature_columns = [ + col for col in self.df.columns if col not in exclude_cols + ] self.feature_columns = feature_columns @@ -129,7 +134,9 @@ def _load_image(self, path: str | Path) -> np.ndarray: if suffix == ".dcm": if not PYDICOM_AVAILABLE: - raise ImportError("pydicom is required for DICOM files. Install with: pip install pydicom") + raise ImportError( + "pydicom is required for DICOM files. Install with: pip install pydicom" + ) return self._load_dicom(path) elif suffix == ".npy": diff --git a/src/engine/trainer.py b/src/engine/trainer.py index 7dd99ac..ed78610 100644 --- a/src/engine/trainer.py +++ b/src/engine/trainer.py @@ -94,7 +94,9 @@ def __init__( self.checkpoint_dir.mkdir(parents=True, exist_ok=True) # Setup AMP - self.scaler = GradScaler('cuda' if device.type == 'cuda' else 'cpu', enabled=use_amp) + self.scaler = GradScaler( + "cuda" if device.type == "cuda" else "cpu", enabled=use_amp + ) # Setup logging self.writer = SummaryWriter(log_dir=str(self.log_dir)) @@ -102,7 +104,7 @@ def __init__( # Early stopping self.early_stopping_patience = early_stopping_patience - self.best_val_loss = float('inf') + self.best_val_loss = float("inf") self.best_val_auc = 0.0 self.patience_counter = 0 @@ -133,17 +135,17 @@ def _compute_medical_metrics( try: if len(np.unique(y_true)) == 2: # Binary classification - metrics['auc_roc'] = roc_auc_score(y_true, y_prob[:, 1]) + metrics["auc_roc"] = roc_auc_score(y_true, y_prob[:, 1]) else: # Multiclass - metrics['auc_roc'] = roc_auc_score( - y_true, y_prob, multi_class='ovr', average='macro' + metrics["auc_roc"] = roc_auc_score( + y_true, y_prob, multi_class="ovr", average="macro" ) except Exception: - metrics['auc_roc'] = 0.0 + metrics["auc_roc"] = 0.0 # F1 Score - metrics['f1_score'] = f1_score(y_true, y_pred, average='macro', zero_division=0) + metrics["f1_score"] = f1_score(y_true, y_pred, average="macro", zero_division=0) # Confusion matrix for sensitivity and specificity cm = confusion_matrix(y_true, y_pred) @@ -151,10 +153,10 @@ def _compute_medical_metrics( if cm.shape[0] == 2: # Binary classification tn, fp, fn, tp = cm.ravel() - metrics['sensitivity'] = tp / (tp + fn) if (tp + fn) > 0 else 0.0 - metrics['specificity'] = tn / (tn + fp) if (tn + fp) > 0 else 0.0 - metrics['precision'] = tp / (tp + fp) if (tp + fp) > 0 else 0.0 - metrics['npv'] = tn / (tn + fn) if (tn + fn) > 0 else 0.0 + metrics["sensitivity"] = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + metrics["specificity"] = tn / (tn + fp) if (tn + fp) > 0 else 0.0 + metrics["precision"] = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + metrics["npv"] = tn / (tn + fn) if (tn + fn) > 0 else 0.0 else: # Multiclass - compute macro-averaged metrics sensitivities = [] @@ -171,11 +173,11 @@ def _compute_medical_metrics( sensitivities.append(sens) specificities.append(spec) - metrics['sensitivity'] = np.mean(sensitivities) - metrics['specificity'] = np.mean(specificities) + metrics["sensitivity"] = np.mean(sensitivities) + metrics["specificity"] = np.mean(specificities) # Accuracy - metrics['accuracy'] = (y_true == y_pred).mean() + metrics["accuracy"] = (y_true == y_pred).mean() return metrics @@ -190,7 +192,10 @@ def _train_epoch(self) -> dict[str, float]: total_loss = 0.0 num_batches = 0 - pbar = tqdm(self.train_loader, desc=f"Epoch {self.current_epoch + 1}/{self.num_epochs} [Train]") + pbar = tqdm( + self.train_loader, + desc=f"Epoch {self.current_epoch + 1}/{self.num_epochs} [Train]", + ) for images, features, labels in pbar: images = images.to(self.device) @@ -223,14 +228,16 @@ def _train_epoch(self) -> dict[str, float]: self.global_step += 1 # Update progress bar - pbar.set_postfix({'loss': f'{loss.item():.4f}'}) + pbar.set_postfix({"loss": f"{loss.item():.4f}"}) # Log to TensorBoard if self.global_step % 10 == 0: - self.writer.add_scalar('train/batch_loss', loss.item(), self.global_step) + self.writer.add_scalar( + "train/batch_loss", loss.item(), self.global_step + ) avg_loss = total_loss / num_batches - return {'loss': avg_loss} + return {"loss": avg_loss} def _validate_epoch(self) -> dict[str, float]: """ @@ -247,7 +254,10 @@ def _validate_epoch(self) -> dict[str, float]: all_preds = [] all_probs = [] - pbar = tqdm(self.val_loader, desc=f"Epoch {self.current_epoch + 1}/{self.num_epochs} [Val]") + pbar = tqdm( + self.val_loader, + desc=f"Epoch {self.current_epoch + 1}/{self.num_epochs} [Val]", + ) with torch.no_grad(): for images, features, labels in pbar: @@ -279,7 +289,7 @@ def _validate_epoch(self) -> dict[str, float]: y_prob = np.array(all_probs) metrics = self._compute_medical_metrics(y_true, y_pred, y_prob) - metrics['loss'] = avg_loss + metrics["loss"] = avg_loss return metrics @@ -292,22 +302,24 @@ def _save_checkpoint(self, filename: str, metrics: dict[str, float]) -> None: metrics: Current metrics """ checkpoint = { - 'epoch': self.current_epoch, - 'model_state_dict': self.model.state_dict(), - 'optimizer_state_dict': self.optimizer.state_dict(), - 'scaler_state_dict': self.scaler.state_dict(), - 'metrics': metrics, - 'best_val_loss': self.best_val_loss, - 'best_val_auc': self.best_val_auc, + "epoch": self.current_epoch, + "model_state_dict": self.model.state_dict(), + "optimizer_state_dict": self.optimizer.state_dict(), + "scaler_state_dict": self.scaler.state_dict(), + "metrics": metrics, + "best_val_loss": self.best_val_loss, + "best_val_auc": self.best_val_auc, } if self.scheduler is not None: - checkpoint['scheduler_state_dict'] = self.scheduler.state_dict() + checkpoint["scheduler_state_dict"] = self.scheduler.state_dict() path = self.checkpoint_dir / filename torch.save(checkpoint, path) - def _log_metrics(self, train_metrics: dict[str, float], val_metrics: dict[str, float]) -> None: + def _log_metrics( + self, train_metrics: dict[str, float], val_metrics: dict[str, float] + ) -> None: """ Log metrics to TensorBoard and JSON. @@ -317,30 +329,30 @@ def _log_metrics(self, train_metrics: dict[str, float], val_metrics: dict[str, f """ # TensorBoard for key, value in train_metrics.items(): - self.writer.add_scalar(f'train/{key}', value, self.current_epoch) + self.writer.add_scalar(f"train/{key}", value, self.current_epoch) for key, value in val_metrics.items(): - self.writer.add_scalar(f'val/{key}', value, self.current_epoch) + self.writer.add_scalar(f"val/{key}", value, self.current_epoch) # Learning rate if self.scheduler is not None: lr = self.scheduler.get_last_lr()[0] else: - lr = self.optimizer.param_groups[0]['lr'] - self.writer.add_scalar('learning_rate', lr, self.current_epoch) + lr = self.optimizer.param_groups[0]["lr"] + self.writer.add_scalar("learning_rate", lr, self.current_epoch) # JSON history epoch_metrics = { - 'epoch': self.current_epoch, - 'train': train_metrics, - 'val': val_metrics, - 'lr': lr, + "epoch": self.current_epoch, + "train": train_metrics, + "val": val_metrics, + "lr": lr, } self.metrics_history.append(epoch_metrics) # Save JSON - json_path = self.log_dir / 'metrics_history.json' - with open(json_path, 'w') as f: + json_path = self.log_dir / "metrics_history.json" + with open(json_path, "w") as f: json.dump(self.metrics_history, f, indent=2) def train(self) -> dict[str, float]: @@ -377,23 +389,25 @@ def train(self) -> dict[str, float]: # Learning rate scheduling if self.scheduler is not None: - if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): - self.scheduler.step(val_metrics['loss']) + if isinstance( + self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau + ): + self.scheduler.step(val_metrics["loss"]) else: self.scheduler.step() # Save best model - if val_metrics['auc_roc'] > self.best_val_auc: - self.best_val_auc = val_metrics['auc_roc'] - self.best_val_loss = val_metrics['loss'] - self._save_checkpoint('best_model.pth', val_metrics) + if val_metrics["auc_roc"] > self.best_val_auc: + self.best_val_auc = val_metrics["auc_roc"] + self.best_val_loss = val_metrics["loss"] + self._save_checkpoint("best_model.pth", val_metrics) print(f" ✓ New best model saved (AUC: {self.best_val_auc:.4f})") self.patience_counter = 0 else: self.patience_counter += 1 # Save last model - self._save_checkpoint('last_model.pth', val_metrics) + self._save_checkpoint("last_model.pth", val_metrics) # Early stopping if self.patience_counter >= self.early_stopping_patience: @@ -408,8 +422,8 @@ def train(self) -> dict[str, float]: print(f"Best validation loss: {self.best_val_loss:.4f}") return { - 'best_val_auc': self.best_val_auc, - 'best_val_loss': self.best_val_loss, + "best_val_auc": self.best_val_auc, + "best_val_loss": self.best_val_loss, } def load_checkpoint(self, checkpoint_path: str | Path) -> None: @@ -421,15 +435,15 @@ def load_checkpoint(self, checkpoint_path: str | Path) -> None: """ checkpoint = torch.load(checkpoint_path, map_location=self.device) - self.model.load_state_dict(checkpoint['model_state_dict']) - self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) - self.scaler.load_state_dict(checkpoint['scaler_state_dict']) + self.model.load_state_dict(checkpoint["model_state_dict"]) + self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + self.scaler.load_state_dict(checkpoint["scaler_state_dict"]) - if self.scheduler is not None and 'scheduler_state_dict' in checkpoint: - self.scheduler.load_state_dict(checkpoint['scheduler_state_dict']) + if self.scheduler is not None and "scheduler_state_dict" in checkpoint: + self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) - self.current_epoch = checkpoint['epoch'] - self.best_val_loss = checkpoint.get('best_val_loss', float('inf')) - self.best_val_auc = checkpoint.get('best_val_auc', 0.0) + self.current_epoch = checkpoint["epoch"] + self.best_val_loss = checkpoint.get("best_val_loss", float("inf")) + self.best_val_auc = checkpoint.get("best_val_auc", 0.0) print(f"Loaded checkpoint from epoch {self.current_epoch}") diff --git a/src/utils/clean_table.py b/src/utils/clean_table.py index f65be2a..69d0274 100644 --- a/src/utils/clean_table.py +++ b/src/utils/clean_table.py @@ -42,7 +42,9 @@ def __init__( self, dictionary_path: str | Path | None = None, outlier_method: Literal["iqr", "zscore", "range"] = "range", - missing_strategy: Literal["drop", "mean", "median", "mode", "forward_fill"] = "median", + missing_strategy: Literal[ + "drop", "mean", "median", "mode", "forward_fill" + ] = "median", encoding_strategy: Literal["onehot", "label", "ordinal"] = "label", verbose: bool = True, ): @@ -82,7 +84,7 @@ def _load_dictionary(self) -> dict: print(f"Warning: Dictionary not found at {self.dictionary_path}") return {} - with open(self.dictionary_path, encoding='utf-8') as f: + with open(self.dictionary_path, encoding="utf-8") as f: return yaml.safe_load(f) def _get_valid_range(self, column: str) -> tuple[float, float] | None: @@ -96,8 +98,10 @@ def _get_valid_range(self, column: str) -> tuple[float, float] | None: for field_name, field_info in category_data.items(): if isinstance(field_info, dict): # Check if this field matches the column - if field_name == column or column in field_info.get('aliases', []): - return field_info.get('valid_range') + if field_name == column or column in field_info.get( + "aliases", [] + ): + return field_info.get("valid_range") return None @@ -267,19 +271,39 @@ def encode_categorical( # Common medical categorical mappings gender_map = { - "male": 0, "男": 0, "M": 0, "m": 0, - "female": 1, "女": 1, "F": 1, "f": 1, + "male": 0, + "男": 0, + "M": 0, + "m": 0, + "female": 1, + "女": 1, + "F": 1, + "f": 1, } smoking_map = { - "never": 0, "从不": 0, "no": 0, "否": 0, - "former": 1, "曾经": 1, "quit": 1, - "current": 2, "目前": 2, "yes": 2, "是": 2, + "never": 0, + "从不": 0, + "no": 0, + "否": 0, + "former": 1, + "曾经": 1, + "quit": 1, + "current": 2, + "目前": 2, + "yes": 2, + "是": 2, } binary_map = { - "no": 0, "否": 0, "false": 0, "0": 0, - "yes": 1, "是": 1, "true": 1, "1": 1, + "no": 0, + "否": 0, + "false": 0, + "0": 0, + "yes": 1, + "是": 1, + "true": 1, + "1": 1, } col_lower = column.lower() @@ -352,7 +376,7 @@ def handle_missing( self.cleaning_stats["missing_filled"] += missing_count elif self.missing_strategy == "forward_fill": - df_filled[column].fillna(method='ffill', inplace=True) + df_filled[column].fillna(method="ffill", inplace=True) self.cleaning_stats["missing_filled"] += missing_count return df_filled @@ -403,7 +427,10 @@ def clean( print("\nStep 3: Encoding categorical variables...") for col in df_clean.columns: - if df_clean[col].dtype == 'object' or df_clean[col].dtype.name == 'category': + if ( + df_clean[col].dtype == "object" + or df_clean[col].dtype.name == "category" + ): df_clean = self.encode_categorical(df_clean, col) # Step 4: Handle missing values @@ -415,16 +442,18 @@ def clean( # Summary if self.verbose: - print("\n" + "="*60) + print("\n" + "=" * 60) print("Cleaning Summary:") print(f" Outliers detected: {self.cleaning_stats['outliers_detected']}") print(f" Outliers fixed: {self.cleaning_stats['outliers_fixed']}") print(f" Logical errors: {self.cleaning_stats['logical_errors']}") print(f" Missing values: {self.cleaning_stats['missing_values']}") print(f" Missing filled: {self.cleaning_stats['missing_filled']}") - print(f" Categorical encoded: {self.cleaning_stats['categorical_encoded']}") + print( + f" Categorical encoded: {self.cleaning_stats['categorical_encoded']}" + ) print(f" Output shape: {df_clean.shape}") - print("="*60) + print("=" * 60) return df_clean @@ -437,13 +466,13 @@ def get_cleaning_report(self) -> dict: if __name__ == "__main__": # Create sample clinical data with issues sample_data = { - 'patient_id': ['P001', 'P002', 'P003', 'P004', 'P005'], - 'age': [45, 200, 38, -5, 61], # Outliers: 200, -5 - 'gender': ['男', 'F', '女', 'M', '男'], - 'systolic_bp': [120, 0, 145, 160, 135], # Outlier: 0 - 'diastolic_bp': [80, 70, 95, 100, 85], - 'glucose': [5.2, np.nan, 7.8, 9.1, 5.0], # Missing value - 'smoking': ['从不', 'yes', 'no', '目前', 'former'], + "patient_id": ["P001", "P002", "P003", "P004", "P005"], + "age": [45, 200, 38, -5, 61], # Outliers: 200, -5 + "gender": ["男", "F", "女", "M", "男"], + "systolic_bp": [120, 0, 145, 160, 135], # Outlier: 0 + "diastolic_bp": [80, 70, 95, 100, 85], + "glucose": [5.2, np.nan, 7.8, 9.1, 5.0], # Missing value + "smoking": ["从不", "yes", "no", "目前", "former"], } df = pd.DataFrame(sample_data) @@ -458,7 +487,7 @@ def get_cleaning_report(self) -> dict: outlier_method="range", missing_strategy="median", encoding_strategy="label", - verbose=True + verbose=True, ) # Clean data diff --git a/uv.lock b/uv.lock index 782e74e..20a682d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.11" +requires-python = ">=3.11, <3.14" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'linux'", "python_full_version >= '3.12' and sys_platform != 'linux'", @@ -104,40 +104,6 @@ wheels = [ { url = "https://mirrors.aliyun.com/pypi/packages/e8/35/4a3daeb8b9fab49240d21c04d50732313295e4bd813a465d840236dd0ce1/aiohttp-3.13.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8057c98e0c8472d8846b9c79f56766bcc57e3e8ac7bfd510482332366c56c591" }, { url = "https://mirrors.aliyun.com/pypi/packages/bc/9f/d643bb3c5fb99547323e635e251c609fbbc660d983144cfebec529e09264/aiohttp-3.13.3-cp313-cp313-win32.whl", hash = "sha256:1449ceddcdbcf2e0446957863af03ebaaa03f94c090f945411b61269e2cb5daf" }, { url = "https://mirrors.aliyun.com/pypi/packages/4e/f1/ab0395f8a79933577cdd996dd2f9aa6014af9535f65dddcf88204682fe62/aiohttp-3.13.3-cp313-cp313-win_amd64.whl", hash = "sha256:693781c45a4033d31d4187d2436f5ac701e7bbfe5df40d917736108c1cc7436e" }, - 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"data": { - "config": { - "data_path": "/path/to/data" - } - } + "data": {"config": {"data_path": "/path/to/data"}}, }, { "id": "node2", "type": "backbone_selector", - "data": { - "config": { - "backbone_type": "resnet18", - "pretrained": True - } - } + "data": {"config": {"backbone_type": "resnet18", "pretrained": True}}, }, { "id": "node3", "type": "trainer", "data": { - "config": { - "epochs": 2, - "batch_size": 32, - "learning_rate": 0.001 - } - } - } + "config": {"epochs": 2, "batch_size": 32, "learning_rate": 0.001} + }, + }, ], "edges": [ - { - "id": "e1", - "source": "node1", - "target": "node3" - }, - { - "id": "e2", - "source": "node2", - "target": "node3" - } - ] + {"id": "e1", "source": "node1", "target": "node3"}, + {"id": "e2", "source": "node2", "target": "node3"}, + ], } # 创建执行引擎 @@ -85,7 +65,7 @@ async def progress_callback(node_id, status, execution, progress): print(f" 状态: {result['status']}") print(f" 执行节点数: {len(result['executions'])}") - if result['status'] == 'success': + if result["status"] == "success": print(" 统计信息:") print(f" 总节点: {result['statistics']['total_nodes']}") print(f" 完成节点: {result['statistics']['completed_nodes']}") @@ -95,7 +75,7 @@ async def progress_callback(node_id, status, execution, progress): print(f" 错误: {result.get('error')}") print("\n❌ 工作流执行引擎测试失败!") - return result['status'] == 'success' + return result["status"] == "success" async def test_training_service(): @@ -112,7 +92,7 @@ async def test_training_service(): "backbone": "resnet18", "num_classes": 10, "pretrained": False, # 不使用预训练以加快测试 - "feature_dim": 128 + "feature_dim": 128, }, "data_config": { "num_samples": 100 # 少量数据用于测试 @@ -125,8 +105,8 @@ async def test_training_service(): "use_amp": False, # 不使用混合精度以兼容 CPU "gradient_checkpointing": False, "use_scheduler": False, - "save_model": False - } + "save_model": False, + }, } # 创建训练服务 @@ -173,9 +153,9 @@ async def progress_callback(data): print(f" Epoch: {status['current_epoch']}/{status['total_epochs']}") print(f" 耗时: {status['duration']:.2f}s") - if status['status'] == 'completed': + if status["status"] == "completed": print(" 最终指标:") - metrics = status['metrics'] + metrics = status["metrics"] print(f" Train Loss: {metrics.get('train_loss', 0):.4f}") print(f" Train Acc: {metrics.get('train_acc', 0):.2f}%") print(f" Val Loss: {metrics.get('val_loss', 0):.4f}") @@ -190,6 +170,7 @@ async def progress_callback(data): except Exception as e: print(f"\n❌ 训练服务测试失败: {e}") import traceback + traceback.print_exc() return False @@ -208,6 +189,7 @@ async def main(): except Exception as e: print(f"\n❌ 工作流执行引擎测试异常: {e}") import traceback + traceback.print_exc() results.append(("工作流执行引擎", False)) @@ -218,6 +200,7 @@ async def main(): except Exception as e: print(f"\n❌ 训练服务测试异常: {e}") import traceback + traceback.print_exc() results.append(("训练服务", False)) diff --git a/web/test_optimizations.py b/web/test_optimizations.py index 1bb57fc..b46d496 100644 --- a/web/test_optimizations.py +++ b/web/test_optimizations.py @@ -2,12 +2,14 @@ """ 快速测试脚本 - 验证所有优化功能 """ + import os import sys from datetime import UTC # 添加 backend 到 Python 路径 -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend')) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "backend")) + def test_auth(): """测试认证模块""" @@ -37,6 +39,7 @@ def test_auth(): except Exception as e: print(f" ❌ 认证模块测试失败: {e}") import traceback + traceback.print_exc() return False @@ -63,7 +66,6 @@ def test_database(): """测试数据库配置""" print("\n💾 测试数据库配置...") try: - from app.core.database import engine from app.models.database import utc_now @@ -118,7 +120,7 @@ def test_workflow_engine(): ], "edges": [ {"source": "node1", "target": "node2"}, - ] + ], } engine = WorkflowEngine(workflow) @@ -160,7 +162,7 @@ def main(): print(f"{name:20s} {status}") print("-" * 60) - print(f"总计: {passed}/{total} 通过 ({passed/total*100:.0f}%)") + print(f"总计: {passed}/{total} 通过 ({passed / total * 100:.0f}%)") print("=" * 60) if passed == total: