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Deep narrow head on evolved feature dims.
Analytical evolution selected the 100 most informative dims.
Now train a deep nonlinear MLP on those 100 dims with spatial context.
"""
import argparse
import json
import math
import os
import sys
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, SCRIPT_DIR)
CACHE_DIR = os.environ.get("ARENA_CACHE_DIR")
COCO_ROOT = os.environ.get("ARENA_COCO_ROOT")
VAL_CACHE = os.environ.get("ARENA_VAL_CACHE")
RESOLUTION = 640
NUM_CLASSES = 80
def cofiber_decompose(f, n_scales):
cofibers = []; residual = f
for _ in range(n_scales - 1):
omega = F.avg_pool2d(residual, 2)
sigma_omega = F.interpolate(omega, size=residual.shape[2:], mode="bilinear", align_corners=False)
cofibers.append(residual - sigma_omega); residual = omega
cofibers.append(residual); return cofibers
class EvolvedDeepHead(nn.Module):
"""Deep MLP on evolved feature dims with spatial depthwise convolutions."""
def __init__(self, evolved_dims, hidden=128, n_layers=10, n_scales=3):
super().__init__()
self.evolved_dims = evolved_dims
self.n_scales = n_scales
K = len(evolved_dims)
self.dim_idx = nn.Parameter(torch.tensor(evolved_dims, dtype=torch.long), requires_grad=False)
self.scale_norms = nn.ModuleList([nn.LayerNorm(768) for _ in range(n_scales)])
# Deep MLP with interleaved spatial convolutions
layers = []
in_dim = K
for i in range(n_layers):
layers.append(nn.Linear(in_dim, hidden))
layers.append(nn.GELU())
if i % 2 == 1: # spatial conv every other layer
layers.append(SpatialDWConv(hidden))
in_dim = hidden
self.backbone = nn.Sequential(*layers)
# Separate output heads
self.cls_head = nn.Linear(hidden, NUM_CLASSES)
self.reg_head = nn.Linear(hidden, 4)
self.ctr_head = nn.Linear(hidden, 1)
self.scale_params = nn.Parameter(torch.ones(n_scales))
def forward(self, spatial):
cofibers = cofiber_decompose(spatial, self.n_scales)
cls_l, reg_l, ctr_l = [], [], []
for i, cof in enumerate(cofibers):
B, C, H, W = cof.shape
f = self.scale_norms[i](cof.permute(0, 2, 3, 1).reshape(-1, C))
# Select evolved dims
f_sel = f[:, self.dim_idx]
# Deep MLP with spatial context
# Need to reshape for spatial convs
f_sel = f_sel.reshape(B, H, W, -1)
h = self._forward_with_spatial(f_sel, B, H, W)
# Output heads
cls = self.cls_head(h.reshape(-1, h.shape[-1])).reshape(B, H, W, -1).permute(0, 3, 1, 2)
reg_raw = (self.reg_head(h.reshape(-1, h.shape[-1])) * self.scale_params[i]).clamp(-10, 10)
reg = reg_raw.exp().reshape(B, H, W, 4).permute(0, 3, 1, 2)
ctr = self.ctr_head(h.reshape(-1, h.shape[-1])).reshape(B, H, W, 1).permute(0, 3, 1, 2)
cls_l.append(cls); reg_l.append(reg); ctr_l.append(ctr)
return cls_l, reg_l, ctr_l
def _forward_with_spatial(self, x, B, H, W):
"""Run the backbone layers, reshaping for spatial convs."""
# x: (B, H, W, K)
for layer in self.backbone:
if isinstance(layer, SpatialDWConv):
x = layer(x, B, H, W)
elif isinstance(layer, nn.Linear):
x = layer(x)
elif isinstance(layer, nn.GELU):
x = layer(x)
return x
class SpatialDWConv(nn.Module):
"""Depthwise 3x3 conv that operates on (B, H, W, C) tensors."""
def __init__(self, channels):
super().__init__()
self.conv = nn.Conv2d(channels, channels, 3, padding=1, groups=channels)
def forward(self, x, B, H, W):
# x: (B, H, W, C) or (B*H*W, C)
if x.dim() == 4:
c = x.shape[-1]
x = x.permute(0, 3, 1, 2) # (B, C, H, W)
x = self.conv(x)
x = x.permute(0, 2, 3, 1) # (B, H, W, C)
return x
def make_locations(feature_sizes, strides, device):
locs = []
for (h, w), s in zip(feature_sizes, strides):
ys = (torch.arange(h, device=device, dtype=torch.float32) + 0.5) * s
xs = (torch.arange(w, device=device, dtype=torch.float32) + 0.5) * s
gy, gx = torch.meshgrid(ys, xs, indexing="ij")
locs.append(torch.stack([gx.flatten(), gy.flatten()], -1))
return locs
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--hidden", type=int, default=128)
parser.add_argument("--layers", type=int, default=10)
parser.add_argument("--epochs", type=int, default=20)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--lr", type=float, default=1e-3)
args = parser.parse_args()
# Load evolved dims
evolved_path = os.path.join(SCRIPT_DIR, "circuit", "evolved_extreme.json")
with open(evolved_path) as f:
evolved = json.load(f)
dims = None
for r in evolved:
if r["K"] == 100:
dims = sorted(list(set(r["genome"])))
break
if dims is None:
print("No K=100 genome found"); return
print("=" * 60)
print(f"Deep Evolved Head: {len(dims)} dims, {args.hidden} hidden, {args.layers} layers")
print("=" * 60, flush=True)
head = EvolvedDeepHead(dims, hidden=args.hidden, n_layers=args.layers).cuda()
n_params = sum(p.numel() for p in head.parameters() if p.requires_grad)
print(f" {n_params:,} trainable params", flush=True)
# Training setup
from cache_and_train_fast import compute_loss
manifest = json.load(open(os.path.join(CACHE_DIR, "manifest.json")))
n_shards = manifest["n_shards"]
n_images = manifest["n_images"]
steps_per_epoch = n_images // args.batch_size
total_steps = steps_per_epoch * args.epochs
warmup = int(total_steps * 0.03)
optimizer = torch.optim.AdamW(head.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda s:
s / max(warmup, 1) if s < warmup else
0.5 * (1 + math.cos(math.pi * (s - warmup) / max(total_steps - warmup, 1))))
strides = [16, 32, 64]
H = RESOLUTION // 16
locs = make_locations([(H, H), (H//2, H//2), (H//4, H//4)], strides, torch.device("cuda"))
shard_paths = [os.path.join(CACHE_DIR, f"shard_{i:04d}.pt") for i in range(n_shards)]
print(f" {n_images} images, batch {args.batch_size}, {total_steps} steps, {args.epochs} epochs")
print(f" Training...", flush=True)
head.train()
global_step = 0
t0 = time.time()
for epoch in range(args.epochs):
shard_order = torch.randperm(n_shards).tolist()
epoch_t0 = time.time()
for shard_idx in shard_order:
if global_step >= total_steps: break
shard = torch.load(shard_paths[shard_idx], map_location="cpu", weights_only=False)
within = torch.randperm(len(shard)).tolist()
for batch_start in range(0, len(shard), args.batch_size):
if global_step >= total_steps: break
batch_idx = within[batch_start:batch_start + args.batch_size]
if len(batch_idx) < 2: continue
spatial = torch.stack([shard[i]["spatial"] for i in batch_idx]).float().cuda()
boxes = [shard[i]["boxes"].cuda() for i in batch_idx]
labels = [shard[i]["labels"].cuda() for i in batch_idx]
try:
cls_l, reg_l, ctr_l = head(spatial)
loss = compute_loss(cls_l, reg_l, ctr_l, locs, boxes, labels)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(head.parameters(), 5.0)
optimizer.step()
scheduler.step()
if global_step % 200 == 0:
torch.cuda.synchronize()
global_step += 1
if global_step % 100 == 0:
lr = scheduler.get_last_lr()[0]
elapsed = time.time() - t0
print(f" step {global_step}/{total_steps} (ep {epoch+1}) "
f"loss={loss.item():.4f} lr={lr:.2e} "
f"{global_step/elapsed:.1f} it/s", flush=True)
except RuntimeError as e:
if "out of memory" in str(e):
torch.cuda.empty_cache()
optimizer.zero_grad()
global_step += 1
scheduler.step()
continue
raise
del shard
print(f" Epoch {epoch+1}/{args.epochs} complete ({time.time()-epoch_t0:.0f}s)", flush=True)
# Save
out_dir = os.path.join(SCRIPT_DIR, "heads", "cofiber_threshold", "evolved_deep")
os.makedirs(out_dir, exist_ok=True)
out_path = os.path.join(out_dir, f"evolved_deep_{args.hidden}h_{args.layers}l_{args.epochs}ep.pth")
torch.save(head.state_dict(), out_path)
elapsed = time.time() - t0
print(f"\nSaved: {out_path}")
print(f"{n_params:,} params, {elapsed/60:.1f} minutes")
if __name__ == "__main__":
main()
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