初始化：换发型/换发色/训练发型服务
包含： - hair_service_sd: 主服务（换发型/换发色/生发，端口8801） - photo_service: LoRA调度+训练（端口32678） - hair_grow_service: 调试测试页（端口8888，含4个测试页） - 批量训练脚本（batch_train_hairstyles.py） - 发际线mask自动识别（hairline_mask.py，4种方案） - 手绘mask换发型（hair_swap_manual.py） - 文档：README.md + LARGE_FILES.md + docs/ 大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md OSS/COS密钥已脱敏为环境变量，原文件备份在本地
This commit is contained in:
xsl
@@ -0,0 +1,2 @@
|
||||
from .functions import *
|
||||
from .modules import *
|
||||
@@ -0,0 +1,34 @@
|
||||
import torch
|
||||
from itertools import product as product
|
||||
import numpy as np
|
||||
from math import ceil
|
||||
|
||||
|
||||
class PriorBox(object):
|
||||
def __init__(self, cfg, image_size=None, phase='train'):
|
||||
super(PriorBox, self).__init__()
|
||||
self.min_sizes = cfg['min_sizes']
|
||||
self.steps = cfg['steps']
|
||||
self.clip = cfg['clip']
|
||||
self.image_size = image_size
|
||||
self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
|
||||
self.name = "s"
|
||||
|
||||
def forward(self):
|
||||
anchors = []
|
||||
for k, f in enumerate(self.feature_maps):
|
||||
min_sizes = self.min_sizes[k]
|
||||
for i, j in product(range(f[0]), range(f[1])):
|
||||
for min_size in min_sizes:
|
||||
s_kx = min_size / self.image_size[1]
|
||||
s_ky = min_size / self.image_size[0]
|
||||
dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
|
||||
dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
|
||||
for cy, cx in product(dense_cy, dense_cx):
|
||||
anchors += [cx, cy, s_kx, s_ky]
|
||||
|
||||
# back to torch land
|
||||
output = torch.Tensor(anchors).view(-1, 4)
|
||||
if self.clip:
|
||||
output.clamp_(max=1, min=0)
|
||||
return output
|
||||
@@ -0,0 +1,3 @@
|
||||
from .multibox_loss import MultiBoxLoss
|
||||
|
||||
__all__ = ['MultiBoxLoss']
|
||||
@@ -0,0 +1,125 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.autograd import Variable
|
||||
from utils.box_utils_Retina import match, log_sum_exp
|
||||
from models.layers.data import cfg_mnet
|
||||
GPU = cfg_mnet['gpu_train']
|
||||
|
||||
class MultiBoxLoss(nn.Module):
|
||||
"""SSD Weighted Loss Function
|
||||
Compute Targets:
|
||||
1) Produce Confidence Target Indices by matching ground truth boxes
|
||||
with (default) 'priorboxes' that have jaccard index > threshold parameter
|
||||
(default threshold: 0.5).
|
||||
2) Produce localization target by 'encoding' variance into offsets of ground
|
||||
truth boxes and their matched 'priorboxes'.
|
||||
3) Hard negative mining to filter the excessive number of negative examples
|
||||
that comes with using a large number of default bounding boxes.
|
||||
(default negative:positive ratio 3:1)
|
||||
Objective Loss:
|
||||
L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
|
||||
Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
|
||||
weighted by α which is set to 1 by cross val.
|
||||
Args:
|
||||
c: class confidences,
|
||||
l: predicted boxes,
|
||||
g: ground truth boxes
|
||||
N: number of matched default boxes
|
||||
See: https://arxiv.org/pdf/1512.02325.pdf for more details.
|
||||
"""
|
||||
|
||||
def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
|
||||
super(MultiBoxLoss, self).__init__()
|
||||
self.num_classes = num_classes
|
||||
self.threshold = overlap_thresh
|
||||
self.background_label = bkg_label
|
||||
self.encode_target = encode_target
|
||||
self.use_prior_for_matching = prior_for_matching
|
||||
self.do_neg_mining = neg_mining
|
||||
self.negpos_ratio = neg_pos
|
||||
self.neg_overlap = neg_overlap
|
||||
self.variance = [0.1, 0.2]
|
||||
|
||||
def forward(self, predictions, priors, targets):
|
||||
"""Multibox Loss
|
||||
Args:
|
||||
predictions (tuple): A tuple containing loc preds, conf preds,
|
||||
and prior boxes from SSD net.
|
||||
conf shape: torch.size(batch_size,num_priors,num_classes)
|
||||
loc shape: torch.size(batch_size,num_priors,4)
|
||||
priors shape: torch.size(num_priors,4)
|
||||
|
||||
ground_truth (tensor): Ground truth boxes and labels for a batch,
|
||||
shape: [batch_size,num_objs,5] (last idx is the label).
|
||||
"""
|
||||
|
||||
loc_data, conf_data, landm_data = predictions
|
||||
priors = priors
|
||||
num = loc_data.size(0)
|
||||
num_priors = (priors.size(0))
|
||||
|
||||
# match priors (default boxes) and ground truth boxes
|
||||
loc_t = torch.Tensor(num, num_priors, 4)
|
||||
landm_t = torch.Tensor(num, num_priors, 10)
|
||||
conf_t = torch.LongTensor(num, num_priors)
|
||||
for idx in range(num):
|
||||
truths = targets[idx][:, :4].data
|
||||
labels = targets[idx][:, -1].data
|
||||
landms = targets[idx][:, 4:14].data
|
||||
defaults = priors.data
|
||||
match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
|
||||
if GPU:
|
||||
loc_t = loc_t.cuda()
|
||||
conf_t = conf_t.cuda()
|
||||
landm_t = landm_t.cuda()
|
||||
|
||||
zeros = torch.tensor(0).cuda()
|
||||
# landm Loss (Smooth L1)
|
||||
# Shape: [batch,num_priors,10]
|
||||
pos1 = conf_t > zeros
|
||||
num_pos_landm = pos1.long().sum(1, keepdim=True)
|
||||
N1 = max(num_pos_landm.data.sum().float(), 1)
|
||||
pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
|
||||
landm_p = landm_data[pos_idx1].view(-1, 10)
|
||||
landm_t = landm_t[pos_idx1].view(-1, 10)
|
||||
loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
|
||||
|
||||
|
||||
pos = conf_t != zeros
|
||||
conf_t[pos] = 1
|
||||
|
||||
# Localization Loss (Smooth L1)
|
||||
# Shape: [batch,num_priors,4]
|
||||
pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
|
||||
loc_p = loc_data[pos_idx].view(-1, 4)
|
||||
loc_t = loc_t[pos_idx].view(-1, 4)
|
||||
loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
|
||||
|
||||
# Compute max conf across batch for hard negative mining
|
||||
batch_conf = conf_data.view(-1, self.num_classes)
|
||||
loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
|
||||
|
||||
# Hard Negative Mining
|
||||
loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
|
||||
loss_c = loss_c.view(num, -1)
|
||||
_, loss_idx = loss_c.sort(1, descending=True)
|
||||
_, idx_rank = loss_idx.sort(1)
|
||||
num_pos = pos.long().sum(1, keepdim=True)
|
||||
num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
|
||||
neg = idx_rank < num_neg.expand_as(idx_rank)
|
||||
|
||||
# Confidence Loss Including Positive and Negative Examples
|
||||
pos_idx = pos.unsqueeze(2).expand_as(conf_data)
|
||||
neg_idx = neg.unsqueeze(2).expand_as(conf_data)
|
||||
conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
|
||||
targets_weighted = conf_t[(pos+neg).gt(0)]
|
||||
loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
|
||||
|
||||
# Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
|
||||
N = max(num_pos.data.sum().float(), 1)
|
||||
loss_l /= N
|
||||
loss_c /= N
|
||||
loss_landm /= N1
|
||||
|
||||
return loss_l, loss_c, loss_landm
|
||||
Reference in New Issue
Block a user