Spaces:
Sleeping
Sleeping
| import math | |
| from functools import partial | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| class YOLOLoss(nn.Module): | |
| def __init__(self, anchors, num_classes, input_shape, cuda, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]], label_smoothing = 0, focal_loss = False, alpha = 0.25, gamma = 2): | |
| super(YOLOLoss, self).__init__() | |
| #-----------------------------------------------------------# | |
| # 13x13的特征层对应的anchor是[142, 110],[192, 243],[459, 401] | |
| # 26x26的特征层对应的anchor是[36, 75],[76, 55],[72, 146] | |
| # 52x52的特征层对应的anchor是[12, 16],[19, 36],[40, 28] | |
| #-----------------------------------------------------------# | |
| self.anchors = anchors | |
| self.num_classes = num_classes | |
| self.bbox_attrs = 5 + num_classes | |
| self.input_shape = input_shape | |
| self.anchors_mask = anchors_mask | |
| self.label_smoothing = label_smoothing | |
| self.balance = [0.4, 1.0, 4] | |
| self.box_ratio = 0.05 | |
| self.obj_ratio = 5 * (input_shape[0] * input_shape[1]) / (416 ** 2) | |
| self.cls_ratio = 1 * (num_classes / 80) | |
| self.focal_loss = focal_loss | |
| self.focal_loss_ratio = 10 | |
| self.alpha = alpha | |
| self.gamma = gamma | |
| self.ignore_threshold = 0.5 | |
| self.cuda = cuda | |
| def clip_by_tensor(self, t, t_min, t_max): | |
| t = t.float() | |
| result = (t >= t_min).float() * t + (t < t_min).float() * t_min | |
| result = (result <= t_max).float() * result + (result > t_max).float() * t_max | |
| return result | |
| def MSELoss(self, pred, target): | |
| return torch.pow(pred - target, 2) | |
| def BCELoss(self, pred, target): | |
| epsilon = 1e-7 | |
| pred = self.clip_by_tensor(pred, epsilon, 1.0 - epsilon) | |
| output = - target * torch.log(pred) - (1.0 - target) * torch.log(1.0 - pred) | |
| return output | |
| def box_ciou(self, b1, b2): | |
| """ | |
| 输入为： | |
| ---------- | |
| b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh | |
| b2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh | |
| 返回为： | |
| ------- | |
| ciou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1) | |
| """ | |
| #----------------------------------------------------# | |
| # 求出预测框左上角右下角 | |
| #----------------------------------------------------# | |
| b1_xy = b1[..., :2] | |
| b1_wh = b1[..., 2:4] | |
| b1_wh_half = b1_wh/2. | |
| b1_mins = b1_xy - b1_wh_half | |
| b1_maxes = b1_xy + b1_wh_half | |
| #----------------------------------------------------# | |
| # 求出真实框左上角右下角 | |
| #----------------------------------------------------# | |
| b2_xy = b2[..., :2] | |
| b2_wh = b2[..., 2:4] | |
| b2_wh_half = b2_wh/2. | |
| b2_mins = b2_xy - b2_wh_half | |
| b2_maxes = b2_xy + b2_wh_half | |
| #----------------------------------------------------# | |
| # 求真实框和预测框所有的iou | |
| #----------------------------------------------------# | |
| intersect_mins = torch.max(b1_mins, b2_mins) | |
| intersect_maxes = torch.min(b1_maxes, b2_maxes) | |
| intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes)) | |
| intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] | |
| b1_area = b1_wh[..., 0] * b1_wh[..., 1] | |
| b2_area = b2_wh[..., 0] * b2_wh[..., 1] | |
| union_area = b1_area + b2_area - intersect_area | |
| iou = intersect_area / torch.clamp(union_area,min = 1e-6) | |
| #----------------------------------------------------# | |
| # 计算中心的差距 | |
| #----------------------------------------------------# | |
| center_distance = torch.sum(torch.pow((b1_xy - b2_xy), 2), axis=-1) | |
| #----------------------------------------------------# | |
| # 找到包裹两个框的最小框的左上角和右下角 | |
| #----------------------------------------------------# | |
| enclose_mins = torch.min(b1_mins, b2_mins) | |
| enclose_maxes = torch.max(b1_maxes, b2_maxes) | |
| enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes)) | |
| #----------------------------------------------------# | |
| # 计算对角线距离 | |
| #----------------------------------------------------# | |
| enclose_diagonal = torch.sum(torch.pow(enclose_wh,2), axis=-1) | |
| ciou = iou - 1.0 * (center_distance) / torch.clamp(enclose_diagonal,min = 1e-6) | |
| v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(b1_wh[..., 0] / torch.clamp(b1_wh[..., 1],min = 1e-6)) - torch.atan(b2_wh[..., 0] / torch.clamp(b2_wh[..., 1], min = 1e-6))), 2) | |
| alpha = v / torch.clamp((1.0 - iou + v), min=1e-6) | |
| ciou = ciou - alpha * v | |
| return ciou | |
| #---------------------------------------------------# | |
| # 平滑标签 | |
| #---------------------------------------------------# | |
| def smooth_labels(self, y_true, label_smoothing, num_classes): | |
| return y_true * (1.0 - label_smoothing) + label_smoothing / num_classes | |
| def forward(self, l, input, targets=None): | |
| #----------------------------------------------------# | |
| # l 代表使用的是第几个有效特征层 | |
| # input的shape为 bs, 3*(5+num_classes), 13, 13 | |
| # bs, 3*(5+num_classes), 26, 26 | |
| # bs, 3*(5+num_classes), 52, 52 | |
| # targets 真实框的标签情况 [batch_size, num_gt, 5] | |
| #----------------------------------------------------# | |
| #--------------------------------# | |
| # 获得图片数量，特征层的高和宽 | |
| #--------------------------------# | |
| bs = input.size(0) | |
| in_h = input.size(2) | |
| in_w = input.size(3) | |
| #-----------------------------------------------------------------------# | |
| # 计算步长 | |
| # 每一个特征点对应原来的图片上多少个像素点 | |
| # | |
| # 如果特征层为13x13的话，一个特征点就对应原来的图片上的32个像素点 | |
| # 如果特征层为26x26的话，一个特征点就对应原来的图片上的16个像素点 | |
| # 如果特征层为52x52的话，一个特征点就对应原来的图片上的8个像素点 | |
| # stride_h = stride_w = 32、16、8 | |
| #-----------------------------------------------------------------------# | |
| stride_h = self.input_shape[0] / in_h | |
| stride_w = self.input_shape[1] / in_w | |
| #-------------------------------------------------# | |
| # 此时获得的scaled_anchors大小是相对于特征层的 | |
| #-------------------------------------------------# | |
| scaled_anchors = [(a_w / stride_w, a_h / stride_h) for a_w, a_h in self.anchors] | |
| #-----------------------------------------------# | |
| # 输入的input一共有三个，他们的shape分别是 | |
| # bs, 3 * (5+num_classes), 13, 13 => bs, 3, 5 + num_classes, 13, 13 => batch_size, 3, 13, 13, 5 + num_classes | |
| # batch_size, 3, 13, 13, 5 + num_classes | |
| # batch_size, 3, 26, 26, 5 + num_classes | |
| # batch_size, 3, 52, 52, 5 + num_classes | |
| #-----------------------------------------------# | |
| prediction = input.view(bs, len(self.anchors_mask[l]), self.bbox_attrs, in_h, in_w).permute(0, 1, 3, 4, 2).contiguous() | |
| #-----------------------------------------------# | |
| # 先验框的中心位置的调整参数 | |
| #-----------------------------------------------# | |
| x = torch.sigmoid(prediction[..., 0]) | |
| y = torch.sigmoid(prediction[..., 1]) | |
| #-----------------------------------------------# | |
| # 先验框的宽高调整参数 | |
| #-----------------------------------------------# | |
| w = prediction[..., 2] | |
| h = prediction[..., 3] | |
| #-----------------------------------------------# | |
| # 获得置信度，是否有物体 | |
| #-----------------------------------------------# | |
| conf = torch.sigmoid(prediction[..., 4]) | |
| #-----------------------------------------------# | |
| # 种类置信度 | |
| #-----------------------------------------------# | |
| pred_cls = torch.sigmoid(prediction[..., 5:]) | |
| #-----------------------------------------------# | |
| # 获得网络应该有的预测结果 | |
| #-----------------------------------------------# | |
| y_true, noobj_mask, box_loss_scale = self.get_target(l, targets, scaled_anchors, in_h, in_w) | |
| #---------------------------------------------------------------# | |
| # 将预测结果进行解码，判断预测结果和真实值的重合程度 | |
| # 如果重合程度过大则忽略，因为这些特征点属于预测比较准确的特征点 | |
| # 作为负样本不合适 | |
| #----------------------------------------------------------------# | |
| noobj_mask, pred_boxes = self.get_ignore(l, x, y, h, w, targets, scaled_anchors, in_h, in_w, noobj_mask) | |
| if self.cuda: | |
| y_true = y_true.type_as(x) | |
| noobj_mask = noobj_mask.type_as(x) | |
| box_loss_scale = box_loss_scale.type_as(x) | |
| #--------------------------------------------------------------------------# | |
| # box_loss_scale是真实框宽高的乘积，宽高均在0-1之间，因此乘积也在0-1之间。 | |
| # 2-宽高的乘积代表真实框越大，比重越小，小框的比重更大。 | |
| # 使用iou损失时，大中小目标的回归损失不存在比例失衡问题，故弃用 | |
| #--------------------------------------------------------------------------# | |
| box_loss_scale = 2 - box_loss_scale | |
| loss = 0 | |
| obj_mask = y_true[..., 4] == 1 | |
| n = torch.sum(obj_mask) | |
| if n != 0: | |
| #---------------------------------------------------------------# | |
| # 计算预测结果和真实结果的差距 | |
| # loss_loc ciou回归损失 | |
| # loss_cls 分类损失 | |
| #---------------------------------------------------------------# | |
| ciou = self.box_ciou(pred_boxes, y_true[..., :4]).type_as(x) | |
| # loss_loc = torch.mean((1 - ciou)[obj_mask] * box_loss_scale[obj_mask]) | |
| loss_loc = torch.mean((1 - ciou)[obj_mask]) | |
| loss_cls = torch.mean(self.BCELoss(pred_cls[obj_mask], y_true[..., 5:][obj_mask])) | |
| loss += loss_loc * self.box_ratio + loss_cls * self.cls_ratio | |
| #---------------------------------------------------------------# | |
| # 计算是否包含物体的置信度损失 | |
| #---------------------------------------------------------------# | |
| if self.focal_loss: | |
| pos_neg_ratio = torch.where(obj_mask, torch.ones_like(conf) * self.alpha, torch.ones_like(conf) * (1 - self.alpha)) | |
| hard_easy_ratio = torch.where(obj_mask, torch.ones_like(conf) - conf, conf) ** self.gamma | |
| loss_conf = torch.mean((self.BCELoss(conf, obj_mask.type_as(conf)) * pos_neg_ratio * hard_easy_ratio)[noobj_mask.bool() | obj_mask]) * self.focal_loss_ratio | |
| else: | |
| loss_conf = torch.mean(self.BCELoss(conf, obj_mask.type_as(conf))[noobj_mask.bool() | obj_mask]) | |
| loss += loss_conf * self.balance[l] * self.obj_ratio | |
| # if n != 0: | |
| # print(loss_loc * self.box_ratio, loss_cls * self.cls_ratio, loss_conf * self.balance[l] * self.obj_ratio) | |
| return loss | |
| def calculate_iou(self, _box_a, _box_b): | |
| #-----------------------------------------------------------# | |
| # 计算真实框的左上角和右下角 | |
| #-----------------------------------------------------------# | |
| b1_x1, b1_x2 = _box_a[:, 0] - _box_a[:, 2] / 2, _box_a[:, 0] + _box_a[:, 2] / 2 | |
| b1_y1, b1_y2 = _box_a[:, 1] - _box_a[:, 3] / 2, _box_a[:, 1] + _box_a[:, 3] / 2 | |
| #-----------------------------------------------------------# | |
| # 计算先验框获得的预测框的左上角和右下角 | |
| #-----------------------------------------------------------# | |
| b2_x1, b2_x2 = _box_b[:, 0] - _box_b[:, 2] / 2, _box_b[:, 0] + _box_b[:, 2] / 2 | |
| b2_y1, b2_y2 = _box_b[:, 1] - _box_b[:, 3] / 2, _box_b[:, 1] + _box_b[:, 3] / 2 | |
| #-----------------------------------------------------------# | |
| # 将真实框和预测框都转化成左上角右下角的形式 | |
| #-----------------------------------------------------------# | |
| box_a = torch.zeros_like(_box_a) | |
| box_b = torch.zeros_like(_box_b) | |
| box_a[:, 0], box_a[:, 1], box_a[:, 2], box_a[:, 3] = b1_x1, b1_y1, b1_x2, b1_y2 | |
| box_b[:, 0], box_b[:, 1], box_b[:, 2], box_b[:, 3] = b2_x1, b2_y1, b2_x2, b2_y2 | |
| #-----------------------------------------------------------# | |
| # A为真实框的数量，B为先验框的数量 | |
| #-----------------------------------------------------------# | |
| A = box_a.size(0) | |
| B = box_b.size(0) | |
| #-----------------------------------------------------------# | |
| # 计算交的面积 | |
| #-----------------------------------------------------------# | |
| max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) | |
| min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2)) | |
| inter = torch.clamp((max_xy - min_xy), min=0) | |
| inter = inter[:, :, 0] * inter[:, :, 1] | |
| #-----------------------------------------------------------# | |
| # 计算预测框和真实框各自的面积 | |
| #-----------------------------------------------------------# | |
| area_a = ((box_a[:, 2]-box_a[:, 0]) * (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] | |
| area_b = ((box_b[:, 2]-box_b[:, 0]) * (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] | |
| #-----------------------------------------------------------# | |
| # 求IOU | |
| #-----------------------------------------------------------# | |
| union = area_a + area_b - inter | |
| return inter / union # [A,B] | |
| def get_target(self, l, targets, anchors, in_h, in_w): | |
| #-----------------------------------------------------# | |
| # 计算一共有多少张图片 | |
| #-----------------------------------------------------# | |
| bs = len(targets) | |
| #-----------------------------------------------------# | |
| # 用于选取哪些先验框不包含物体 | |
| #-----------------------------------------------------# | |
| noobj_mask = torch.ones(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False) | |
| #-----------------------------------------------------# | |
| # 让网络更加去关注小目标 | |
| #-----------------------------------------------------# | |
| box_loss_scale = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, requires_grad = False) | |
| #-----------------------------------------------------# | |
| # batch_size, 3, 13, 13, 5 + num_classes | |
| #-----------------------------------------------------# | |
| y_true = torch.zeros(bs, len(self.anchors_mask[l]), in_h, in_w, self.bbox_attrs, requires_grad = False) | |
| for b in range(bs): | |
| if len(targets[b])==0: | |
| continue | |
| batch_target = torch.zeros_like(targets[b]) | |
| #-------------------------------------------------------# | |
| # 计算出正样本在特征层上的中心点 | |
| #-------------------------------------------------------# | |
| batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_w | |
| batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h | |
| batch_target[:, 4] = targets[b][:, 4] | |
| batch_target = batch_target.cpu() | |
| #-------------------------------------------------------# | |
| # 将真实框转换一个形式 | |
| # num_true_box, 4 | |
| #-------------------------------------------------------# | |
| gt_box = torch.FloatTensor(torch.cat((torch.zeros((batch_target.size(0), 2)), batch_target[:, 2:4]), 1)) | |
| #-------------------------------------------------------# | |
| # 将先验框转换一个形式 | |
| # 9, 4 | |
| #-------------------------------------------------------# | |
| anchor_shapes = torch.FloatTensor(torch.cat((torch.zeros((len(anchors), 2)), torch.FloatTensor(anchors)), 1)) | |
| #-------------------------------------------------------# | |
| # 计算交并比 | |
| # self.calculate_iou(gt_box, anchor_shapes) = [num_true_box, 9]每一个真实框和9个先验框的重合情况 | |
| # best_ns: | |
| # [每个真实框最大的重合度max_iou, 每一个真实框最重合的先验框的序号] | |
| #-------------------------------------------------------# | |
| best_ns = torch.argmax(self.calculate_iou(gt_box, anchor_shapes), dim=-1) | |
| for t, best_n in enumerate(best_ns): | |
| if best_n not in self.anchors_mask[l]: | |
| continue | |
| #----------------------------------------# | |
| # 判断这个先验框是当前特征点的哪一个先验框 | |
| #----------------------------------------# | |
| k = self.anchors_mask[l].index(best_n) | |
| #----------------------------------------# | |
| # 获得真实框属于哪个网格点 | |
| #----------------------------------------# | |
| i = torch.floor(batch_target[t, 0]).long() | |
| j = torch.floor(batch_target[t, 1]).long() | |
| #----------------------------------------# | |
| # 取出真实框的种类 | |
| #----------------------------------------# | |
| c = batch_target[t, 4].long() | |
| #----------------------------------------# | |
| # noobj_mask代表无目标的特征点 | |
| #----------------------------------------# | |
| noobj_mask[b, k, j, i] = 0 | |
| #----------------------------------------# | |
| # tx、ty代表中心调整参数的真实值 | |
| #----------------------------------------# | |
| y_true[b, k, j, i, 0] = batch_target[t, 0] | |
| y_true[b, k, j, i, 1] = batch_target[t, 1] | |
| y_true[b, k, j, i, 2] = batch_target[t, 2] | |
| y_true[b, k, j, i, 3] = batch_target[t, 3] | |
| y_true[b, k, j, i, 4] = 1 | |
| y_true[b, k, j, i, c + 5] = 1 | |
| #----------------------------------------# | |
| # 用于获得xywh的比例 | |
| # 大目标loss权重小，小目标loss权重大 | |
| #----------------------------------------# | |
| box_loss_scale[b, k, j, i] = batch_target[t, 2] * batch_target[t, 3] / in_w / in_h | |
| return y_true, noobj_mask, box_loss_scale | |
| def get_ignore(self, l, x, y, h, w, targets, scaled_anchors, in_h, in_w, noobj_mask): | |
| #-----------------------------------------------------# | |
| # 计算一共有多少张图片 | |
| #-----------------------------------------------------# | |
| bs = len(targets) | |
| #-----------------------------------------------------# | |
| # 生成网格，先验框中心，网格左上角 | |
| #-----------------------------------------------------# | |
| grid_x = torch.linspace(0, in_w - 1, in_w).repeat(in_h, 1).repeat( | |
| int(bs * len(self.anchors_mask[l])), 1, 1).view(x.shape).type_as(x) | |
| grid_y = torch.linspace(0, in_h - 1, in_h).repeat(in_w, 1).t().repeat( | |
| int(bs * len(self.anchors_mask[l])), 1, 1).view(y.shape).type_as(x) | |
| # 生成先验框的宽高 | |
| scaled_anchors_l = np.array(scaled_anchors)[self.anchors_mask[l]] | |
| anchor_w = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([0])).type_as(x) | |
| anchor_h = torch.Tensor(scaled_anchors_l).index_select(1, torch.LongTensor([1])).type_as(x) | |
| anchor_w = anchor_w.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(w.shape) | |
| anchor_h = anchor_h.repeat(bs, 1).repeat(1, 1, in_h * in_w).view(h.shape) | |
| #-------------------------------------------------------# | |
| # 计算调整后的先验框中心与宽高 | |
| #-------------------------------------------------------# | |
| pred_boxes_x = torch.unsqueeze(x + grid_x, -1) | |
| pred_boxes_y = torch.unsqueeze(y + grid_y, -1) | |
| pred_boxes_w = torch.unsqueeze(torch.exp(w) * anchor_w, -1) | |
| pred_boxes_h = torch.unsqueeze(torch.exp(h) * anchor_h, -1) | |
| pred_boxes = torch.cat([pred_boxes_x, pred_boxes_y, pred_boxes_w, pred_boxes_h], dim = -1) | |
| for b in range(bs): | |
| #-------------------------------------------------------# | |
| # 将预测结果转换一个形式 | |
| # pred_boxes_for_ignore num_anchors, 4 | |
| #-------------------------------------------------------# | |
| pred_boxes_for_ignore = pred_boxes[b].view(-1, 4) | |
| #-------------------------------------------------------# | |
| # 计算真实框，并把真实框转换成相对于特征层的大小 | |
| # gt_box num_true_box, 4 | |
| #-------------------------------------------------------# | |
| if len(targets[b]) > 0: | |
| batch_target = torch.zeros_like(targets[b]) | |
| #-------------------------------------------------------# | |
| # 计算出正样本在特征层上的中心点 | |
| #-------------------------------------------------------# | |
| batch_target[:, [0,2]] = targets[b][:, [0,2]] * in_w | |
| batch_target[:, [1,3]] = targets[b][:, [1,3]] * in_h | |
| batch_target = batch_target[:, :4].type_as(x) | |
| #-------------------------------------------------------# | |
| # 计算交并比 | |
| # anch_ious num_true_box, num_anchors | |
| #-------------------------------------------------------# | |
| anch_ious = self.calculate_iou(batch_target, pred_boxes_for_ignore) | |
| #-------------------------------------------------------# | |
| # 每个先验框对应真实框的最大重合度 | |
| # anch_ious_max num_anchors | |
| #-------------------------------------------------------# | |
| anch_ious_max, _ = torch.max(anch_ious, dim = 0) | |
| anch_ious_max = anch_ious_max.view(pred_boxes[b].size()[:3]) | |
| noobj_mask[b][anch_ious_max > self.ignore_threshold] = 0 | |
| return noobj_mask, pred_boxes | |
| def weights_init(net, init_type='normal', init_gain = 0.02): | |
| def init_func(m): | |
| classname = m.__class__.__name__ | |
| if hasattr(m, 'weight') and classname.find('Conv') != -1: | |
| if init_type == 'normal': | |
| torch.nn.init.normal_(m.weight.data, 0.0, init_gain) | |
| elif init_type == 'xavier': | |
| torch.nn.init.xavier_normal_(m.weight.data, gain=init_gain) | |
| elif init_type == 'kaiming': | |
| torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') | |
| elif init_type == 'orthogonal': | |
| torch.nn.init.orthogonal_(m.weight.data, gain=init_gain) | |
| else: | |
| raise NotImplementedError('initialization method [%s] is not implemented' % init_type) | |
| elif classname.find('BatchNorm2d') != -1: | |
| torch.nn.init.normal_(m.weight.data, 1.0, 0.02) | |
| torch.nn.init.constant_(m.bias.data, 0.0) | |
| print('initialize network with %s type' % init_type) | |
| net.apply(init_func) | |
| def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10): | |
| def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters): | |
| if iters <= warmup_total_iters: | |
| # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start | |
| lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2) + warmup_lr_start | |
| elif iters >= total_iters - no_aug_iter: | |
| lr = min_lr | |
| else: | |
| lr = min_lr + 0.5 * (lr - min_lr) * ( | |
| 1.0 + math.cos(math.pi* (iters - warmup_total_iters) / (total_iters - warmup_total_iters - no_aug_iter)) | |
| ) | |
| return lr | |
| def step_lr(lr, decay_rate, step_size, iters): | |
| if step_size < 1: | |
| raise ValueError("step_size must above 1.") | |
| n = iters // step_size | |
| out_lr = lr * decay_rate ** n | |
| return out_lr | |
| if lr_decay_type == "cos": | |
| warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3) | |
| warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6) | |
| no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15) | |
| func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter) | |
| else: | |
| decay_rate = (min_lr / lr) ** (1 / (step_num - 1)) | |
| step_size = total_iters / step_num | |
| func = partial(step_lr, lr, decay_rate, step_size) | |
| return func | |
| def set_optimizer_lr(optimizer, lr_scheduler_func, epoch): | |
| lr = lr_scheduler_func(epoch) | |
| for param_group in optimizer.param_groups: | |
| param_group['lr'] = lr | |