feat: 导入 IRSTD 研究代码与文档（小目标检测）

- 新增 irstd 核心包、scripts、tests、configs
- 新增研究文档：literature_review / mathematical_analysis / novelty_matrix / experiment_plan / research_decision / research_log / manuscript_outline
- 新增 evidence、results 数据与审计产物
- 新增 pyproject.toml、requirements、.gitignore

scripts/benchmark.py

@@ -0,0 +1,37 @@
+"""Hardware measurement only; random input is NOT accuracy evaluation."""
+import argparse,json,time,statistics
+from pathlib import Path
+import torch
+from irstd.models import TinyUNet
+from irstd.core import candidate_pool
+
+def main():
+ p=argparse.ArgumentParser();p.add_argument('--out',required=True);p.add_argument('--size',type=int,default=256)
+ p.add_argument('--device',default='cuda');p.add_argument('--iterations',type=int,default=100);a=p.parse_args()
+ if a.iterations<1 or a.size<2:p.error('Invalid benchmark dimensions')
+ model=TinyUNet().to(a.device).eval();x=torch.randn(1,1,a.size,a.size,device=a.device)
+ cuda=str(a.device).startswith('cuda')
+ def sync():
+  if cuda:torch.cuda.synchronize()
+ if cuda:torch.cuda.reset_peak_memory_stats()
+ times=[];full=[]
+ with torch.no_grad():
+  for i in range(20+a.iterations):
+   sync();start=time.perf_counter();y=model(x).sigmoid();sync();middle=time.perf_counter()
+   candidate_pool(y[0,0].cpu().numpy());end=time.perf_counter()
+   if i>=20:times.append((middle-start)*1000);full.append((end-start)*1000)
+ # Exact conv MAC estimate via hooks, measured for declared resolution.
+ macs=[0]
+ def hook(m,inp,out):
+  macs[0]+=out.numel()*(m.in_channels//m.groups)*m.kernel_size[0]*m.kernel_size[1]
+ handles=[m.register_forward_hook(hook) for m in model.modules() if isinstance(m,torch.nn.Conv2d)]
+ with torch.no_grad():model(x)
+ for h in handles:h.remove()
+ Path(a.out).write_text(json.dumps(dict(device=a.device,gpu=torch.cuda.get_device_name() if cuda else None,
+  input=[1,1,a.size,a.size],dtype='float32',warmup=20,iterations=a.iterations,
+  parameters=sum(p.numel() for p in model.parameters()),conv_MACs=macs[0],conv_FLOPs_2_per_MAC=2*macs[0],
+  model_latency_median_ms=statistics.median(times),end_to_end_median_ms=statistics.median(full),
+  peak_allocated_bytes=torch.cuda.max_memory_allocated() if cuda else None,
+  excludes='Disk IO, candidate MLP, graph solver; FLOPs exclude interpolation/activations/pooling',
+  accuracy='not measured; random input'),indent=2))
+if __name__=='__main__':main()