from einops.einops import rearrange import torch import torch.nn as nn import torch.nn.functional as F from roma.utils.utils import get_gt_warp import wandb import roma import math class RobustLosses(nn.Module): def __init__( self, robust=False, center_coords=False, scale_normalize=False, ce_weight=0.01, local_loss=True, local_dist=4.0, local_largest_scale=8, smooth_mask = False, depth_interpolation_mode = "bilinear", mask_depth_loss = False, relative_depth_error_threshold = 0.05, alpha = 1., c = 1e-3, ): super().__init__() self.robust = robust # measured in pixels self.center_coords = center_coords self.scale_normalize = scale_normalize self.ce_weight = ce_weight self.local_loss = local_loss self.local_dist = local_dist self.local_largest_scale = local_largest_scale self.smooth_mask = smooth_mask self.depth_interpolation_mode = depth_interpolation_mode self.mask_depth_loss = mask_depth_loss self.relative_depth_error_threshold = relative_depth_error_threshold self.avg_overlap = dict() self.alpha = alpha self.c = c def gm_cls_loss(self, x2, prob, scale_gm_cls, gm_certainty, scale): with torch.no_grad(): B, C, H, W = scale_gm_cls.shape device = x2.device cls_res = round(math.sqrt(C)) G = torch.meshgrid(*[torch.linspace(-1+1/cls_res, 1 - 1/cls_res, steps = cls_res,device = device) for _ in range(2)]) G = torch.stack((G[1], G[0]), dim = -1).reshape(C,2) GT = (G[None,:,None,None,:]-x2[:,None]).norm(dim=-1).min(dim=1).indices cls_loss = F.cross_entropy(scale_gm_cls, GT, reduction = 'none')[prob > 0.99] if not torch.any(cls_loss): cls_loss = (certainty_loss * 0.0) # Prevent issues where prob is 0 everywhere certainty_loss = F.binary_cross_entropy_with_logits(gm_certainty[:,0], prob) losses = { f"gm_certainty_loss_{scale}": certainty_loss.mean(), f"gm_cls_loss_{scale}": cls_loss.mean(), } wandb.log(losses, step = roma.GLOBAL_STEP) return losses def delta_cls_loss(self, x2, prob, flow_pre_delta, delta_cls, certainty, scale, offset_scale): with torch.no_grad(): B, C, H, W = delta_cls.shape device = x2.device cls_res = round(math.sqrt(C)) G = torch.meshgrid(*[torch.linspace(-1+1/cls_res, 1 - 1/cls_res, steps = cls_res,device = device) for _ in range(2)]) G = torch.stack((G[1], G[0]), dim = -1).reshape(C,2) * offset_scale GT = (G[None,:,None,None,:] + flow_pre_delta[:,None] - x2[:,None]).norm(dim=-1).min(dim=1).indices cls_loss = F.cross_entropy(delta_cls, GT, reduction = 'none')[prob > 0.99] if not torch.any(cls_loss): cls_loss = (certainty_loss * 0.0) # Prevent issues where prob is 0 everywhere certainty_loss = F.binary_cross_entropy_with_logits(certainty[:,0], prob) losses = { f"delta_certainty_loss_{scale}": certainty_loss.mean(), f"delta_cls_loss_{scale}": cls_loss.mean(), } wandb.log(losses, step = roma.GLOBAL_STEP) return losses def regression_loss(self, x2, prob, flow, certainty, scale, eps=1e-8, mode = "delta"): epe = (flow.permute(0,2,3,1) - x2).norm(dim=-1) if scale == 1: pck_05 = (epe[prob > 0.99] < 0.5 * (2/512)).float().mean() wandb.log({"train_pck_05": pck_05}, step = roma.GLOBAL_STEP) ce_loss = F.binary_cross_entropy_with_logits(certainty[:, 0], prob) a = self.alpha cs = self.c * scale x = epe[prob > 0.99] reg_loss = cs**a * ((x/(cs))**2 + 1**2)**(a/2) if not torch.any(reg_loss): reg_loss = (ce_loss * 0.0) # Prevent issues where prob is 0 everywhere losses = { f"{mode}_certainty_loss_{scale}": ce_loss.mean(), f"{mode}_regression_loss_{scale}": reg_loss.mean(), } wandb.log(losses, step = roma.GLOBAL_STEP) return losses def forward(self, corresps, batch): scales = list(corresps.keys()) tot_loss = 0.0 # scale_weights due to differences in scale for regression gradients and classification gradients scale_weights = {1:1, 2:1, 4:1, 8:1, 16:1} for scale in scales: scale_corresps = corresps[scale] scale_certainty, flow_pre_delta, delta_cls, offset_scale, scale_gm_cls, scale_gm_certainty, flow, scale_gm_flow = ( scale_corresps["certainty"], scale_corresps["flow_pre_delta"], scale_corresps.get("delta_cls"), scale_corresps.get("offset_scale"), scale_corresps.get("gm_cls"), scale_corresps.get("gm_certainty"), scale_corresps["flow"], scale_corresps.get("gm_flow"), ) flow_pre_delta = rearrange(flow_pre_delta, "b d h w -> b h w d") b, h, w, d = flow_pre_delta.shape gt_warp, gt_prob = get_gt_warp( batch["im_A_depth"], batch["im_B_depth"], batch["T_1to2"], batch["K1"], batch["K2"], H=h, W=w, ) x2 = gt_warp.float() prob = gt_prob if self.local_largest_scale >= scale: prob = prob * ( F.interpolate(prev_epe[:, None], size=(h, w), mode="nearest-exact")[:, 0] < (2 / 512) * (self.local_dist[scale] * scale)) if scale_gm_cls is not None: gm_cls_losses = self.gm_cls_loss(x2, prob, scale_gm_cls, scale_gm_certainty, scale) gm_loss = self.ce_weight * gm_cls_losses[f"gm_certainty_loss_{scale}"] + gm_cls_losses[f"gm_cls_loss_{scale}"] tot_loss = tot_loss + scale_weights[scale] * gm_loss elif scale_gm_flow is not None: gm_flow_losses = self.regression_loss(x2, prob, scale_gm_flow, scale_gm_certainty, scale, mode = "gm") gm_loss = self.ce_weight * gm_flow_losses[f"gm_certainty_loss_{scale}"] + gm_flow_losses[f"gm_regression_loss_{scale}"] tot_loss = tot_loss + scale_weights[scale] * gm_loss if delta_cls is not None: delta_cls_losses = self.delta_cls_loss(x2, prob, flow_pre_delta, delta_cls, scale_certainty, scale, offset_scale) delta_cls_loss = self.ce_weight * delta_cls_losses[f"delta_certainty_loss_{scale}"] + delta_cls_losses[f"delta_cls_loss_{scale}"] tot_loss = tot_loss + scale_weights[scale] * delta_cls_loss else: delta_regression_losses = self.regression_loss(x2, prob, flow, scale_certainty, scale) reg_loss = self.ce_weight * delta_regression_losses[f"delta_certainty_loss_{scale}"] + delta_regression_losses[f"delta_regression_loss_{scale}"] tot_loss = tot_loss + scale_weights[scale] * reg_loss prev_epe = (flow.permute(0,2,3,1) - x2).norm(dim=-1).detach() return tot_loss