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| import math
from functools import partial
import torch
import torch.nn as nn
class MultiboxLoss(nn.Module):
def __init__(self, num_classes, alpha=1.0, neg_pos_ratio=3.0,
background_label_id=0, negatives_for_hard=100.0):
self.num_classes = num_classes
self.alpha = alpha
self.neg_pos_ratio = neg_pos_ratio
if background_label_id != 0:
raise Exception('Only 0 as background label id is supported')
self.background_label_id = background_label_id
self.negatives_for_hard = torch.FloatTensor([negatives_for_hard])[0]
def _l1_smooth_loss(self, y_true, y_pred):
abs_loss = torch.abs(y_true - y_pred)
sq_loss = 0.5 * (y_true - y_pred)**2
l1_loss = torch.where(abs_loss < 1.0, sq_loss, abs_loss - 0.5)
return torch.sum(l1_loss, -1)
def _softmax_loss(self, y_true, y_pred):
y_pred = torch.clamp(y_pred, min=1e-7)
softmax_loss = -torch.sum(y_true * torch.log(y_pred),
axis=-1)
return softmax_loss
def forward(self, y_true, y_pred):
num_boxes = y_true.size()[1]
y_pred = torch.cat([y_pred[0], nn.Softmax(-1)(y_pred[1])], dim=-1)
conf_loss = self._softmax_loss(y_true[:, :, 4:-1], y_pred[:, :, 4:])
loc_loss = self._l1_smooth_loss(y_true[:, :, :4],
y_pred[:, :, :4])
pos_loc_loss = torch.sum(loc_loss * y_true[:, :, -1],
axis=1)
pos_conf_loss = torch.sum(conf_loss * y_true[:, :, -1],
axis=1)
num_pos = torch.sum(y_true[:, :, -1], axis=-1)
num_neg = torch.min(self.neg_pos_ratio * num_pos, num_boxes - num_pos)
pos_num_neg_mask = num_neg > 0
has_min = torch.sum(pos_num_neg_mask)
num_neg_batch = torch.sum(
num_neg) if has_min > 0 else self.negatives_for_hard
confs_start = 4 + self.background_label_id + 1
confs_end = confs_start + self.num_classes - 1
max_confs = torch.sum(y_pred[:, :, confs_start:confs_end], dim=2)
max_confs = (max_confs * (1 - y_true[:, :, -1])).view([-1])
_, indices = torch.topk(max_confs, k=int(
num_neg_batch.cpu().numpy().tolist()))
neg_conf_loss = torch.gather(conf_loss.view([-1]), 0, indices)
num_pos = torch.where(num_pos != 0, num_pos, torch.ones_like(num_pos))
total_loss = torch.sum(
pos_conf_loss) + torch.sum(neg_conf_loss) + torch.sum(self.alpha * pos_loc_loss)
total_loss = total_loss / torch.sum(num_pos)
return total_loss
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=0.1, warmup_lr_ratio=0.1, no_aug_iter_ratio=0.3, 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) * 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
|
