import numpy as np import torch import torch.nn as nn def calc_iou(a, b): area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1]) iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0]) ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1]) iw = torch.clamp(iw, min=0) ih = torch.clamp(ih, min=0) ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih ua = torch.clamp(ua, min=1e-8) intersection = iw * ih IoU = intersection / ua return IoU class FocalLoss(nn.Module): #def __init__(self): def forward(self, classifications, regressions, anchors, annotations): alpha = 0.25 gamma = 2.0 batch_size = classifications.shape[0] classification_losses = [] regression_losses = [] anchor = anchors[0, :, :] anchor_widths = anchor[:, 2] - anchor[:, 0] anchor_heights = anchor[:, 3] - anchor[:, 1] anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights for j in range(batch_size): classification = classifications[j, :, :] regression = regressions[j, :, :] bbox_annotation = annotations[j, :, :] bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1] classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4) if bbox_annotation.shape[0] == 0: if torch.cuda.is_available(): alpha_factor = torch.ones(classification.shape).cuda() * alpha alpha_factor = 1. - alpha_factor focal_weight = classification focal_weight = alpha_factor * torch.pow(focal_weight, gamma) bce = -(torch.log(1.0 - classification)) # cls_loss = focal_weight * torch.pow(bce, gamma) cls_loss = focal_weight * bce classification_losses.append(cls_loss.sum()) regression_losses.append(torch.tensor(0).float().cuda()) else: alpha_factor = torch.ones(classification.shape) * alpha alpha_factor = 1. - alpha_factor focal_weight = classification focal_weight = alpha_factor * torch.pow(focal_weight, gamma) bce = -(torch.log(1.0 - classification)) # cls_loss = focal_weight * torch.pow(bce, gamma) cls_loss = focal_weight * bce classification_losses.append(cls_loss.sum()) regression_losses.append(torch.tensor(0).float()) continue IoU = calc_iou(anchors[0, :, :], bbox_annotation[:, :4]) # num_anchors x num_annotations IoU_max, IoU_argmax = torch.max(IoU, dim=1) # num_anchors x 1 #import pdb #pdb.set_trace() # compute the loss for classification targets = torch.ones(classification.shape) * -1 if torch.cuda.is_available(): targets = targets.cuda() targets[torch.lt(IoU_max, 0.4), :] = 0 positive_indices = torch.ge(IoU_max, 0.5) num_positive_anchors = positive_indices.sum() assigned_annotations = bbox_annotation[IoU_argmax, :] targets[positive_indices, :] = 0 targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1 if torch.cuda.is_available(): alpha_factor = torch.ones(targets.shape).cuda() * alpha else: alpha_factor = torch.ones(targets.shape) * alpha alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor) focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification) focal_weight = alpha_factor * torch.pow(focal_weight, gamma) bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification)) # cls_loss = focal_weight * torch.pow(bce, gamma) cls_loss = focal_weight * bce if torch.cuda.is_available(): cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, torch.zeros(cls_loss.shape).cuda()) else: cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, torch.zeros(cls_loss.shape)) classification_losses.append(cls_loss.sum()/torch.clamp(num_positive_anchors.float(), min=1.0)) # compute the loss for regression if positive_indices.sum() > 0: assigned_annotations = assigned_annotations[positive_indices, :] anchor_widths_pi = anchor_widths[positive_indices] anchor_heights_pi = anchor_heights[positive_indices] anchor_ctr_x_pi = anchor_ctr_x[positive_indices] anchor_ctr_y_pi = anchor_ctr_y[positive_indices] gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0] gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1] gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights # clip widths to 1 gt_widths = torch.clamp(gt_widths, min=1) gt_heights = torch.clamp(gt_heights, min=1) targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi targets_dw = torch.log(gt_widths / anchor_widths_pi) targets_dh = torch.log(gt_heights / anchor_heights_pi) targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh)) targets = targets.t() if torch.cuda.is_available(): targets = targets/torch.Tensor([[0.1, 0.1, 0.2, 0.2]]).cuda() else: targets = targets/torch.Tensor([[0.1, 0.1, 0.2, 0.2]]) negative_indices = 1 + (~positive_indices) regression_diff = torch.abs(targets - regression[positive_indices, :]) regression_loss = torch.where( torch.le(regression_diff, 1.0 / 9.0), 0.5 * 9.0 * torch.pow(regression_diff, 2), regression_diff - 0.5 / 9.0 ) regression_losses.append(regression_loss.mean()) else: if torch.cuda.is_available(): regression_losses.append(torch.tensor(0).float().cuda()) else: regression_losses.append(torch.tensor(0).float()) return torch.stack(classification_losses).mean(dim=0, keepdim=True), torch.stack(regression_losses).mean(dim=0, keepdim=True)