| """Cofiber Threshold V2: same as V1 but with 2-layer box regression (768->32->4). |
| |
| Targets the mAP@0.75 collapse in V1 (0.8) caused by single-layer box regression. |
| Classification and cofiber decomposition are identical to V1. |
| ~92K total params (under NanoDet-m-0.5x head at 94K). |
| """ |
|
|
| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from losses.fcos import fcos_loss |
| from utils.decode import make_locations, decode_fcos |
|
|
| 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 CofiberThresholdV2(nn.Module): |
| """Cofiber decomposition + LayerNorm + prototype cls + 2-layer box regression. ~92K params.""" |
| name = "cofiber_threshold_v2" |
| needs_intermediates = False |
|
|
| def __init__(self, feat_dim=768, num_classes=NUM_CLASSES, n_scales=3, reg_hidden=32): |
| super().__init__() |
| self.n_scales = n_scales |
| self.scale_norms = nn.ModuleList([nn.LayerNorm(feat_dim) for _ in range(n_scales)]) |
| |
| self.prototypes = nn.Parameter(torch.randn(num_classes, feat_dim) * 0.01) |
| self.proto_bias = nn.Parameter(torch.zeros(num_classes)) |
| |
| self.reg_hidden = nn.Linear(feat_dim, reg_hidden) |
| self.reg_act = nn.GELU() |
| self.reg_out = nn.Linear(reg_hidden, 4) |
| |
| self.ctr_weight = nn.Parameter(torch.randn(1, feat_dim) * 0.01) |
| self.ctr_bias = nn.Parameter(torch.zeros(1)) |
| self.scale_params = nn.Parameter(torch.ones(n_scales)) |
|
|
| def forward(self, spatial, inter=None): |
| 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)) |
| cls = (f @ self.prototypes.T + self.proto_bias).reshape(B, H, W, -1).permute(0, 3, 1, 2) |
| reg_raw = (self.reg_out(self.reg_act(self.reg_hidden(f))) * self.scale_params[i]).clamp(-10, 10) |
| reg = torch.exp(reg_raw).reshape(B, H, W, 4).permute(0, 3, 1, 2) |
| ctr = (f @ self.ctr_weight.T + self.ctr_bias).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 loss(self, preds, locs, boxes_b, labels_b): |
| return fcos_loss(*preds, locs, boxes_b, labels_b) |
|
|
| def decode(self, preds, locs, **kw): |
| return decode_fcos(*preds, locs, **kw) |
|
|
| def get_locs(self, spatial): |
| dummy = cofiber_decompose(spatial[:1], self.n_scales) |
| sizes = [(c.shape[2], c.shape[3]) for c in dummy] |
| strides = [16 * (2 ** i) for i in range(self.n_scales)] |
| return make_locations(sizes, strides, spatial.device) |
|
|
