colomi
0eb61f3e60
包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义 大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除， 由网盘单独上传。
299 lines
10 KiB
Python
299 lines
10 KiB
Python
from __future__ import division
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.init as init
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import torch.utils.model_zoo as model_zoo
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from torchvision import models
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# general libs
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import cv2
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import matplotlib.pyplot as plt
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from PIL import Image
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import numpy as np
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import math
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import time
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import tqdm
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import os
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import argparse
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import copy
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import sys
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from utils.helpers import *
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class ResBlock(nn.Module):
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def __init__(self, backbone, indim, outdim=None, stride=1):
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super(ResBlock, self).__init__()
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self.backbone = backbone
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if outdim == None:
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outdim = indim
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if indim == outdim and stride == 1:
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self.downsample = None
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else:
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self.downsample = nn.Conv2d(indim, outdim, kernel_size=3, padding=1, stride=stride)
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self.conv1 = nn.Conv2d(indim, outdim, kernel_size=3, padding=1, stride=stride)
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self.conv2 = nn.Conv2d(outdim, outdim, kernel_size=3, padding=1)
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def forward(self, x):
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if self.backbone == 'resnest101':
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r = self.conv1(F.relu(x, inplace=True))
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r = self.conv2(F.relu(r, inplace=True))
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else:
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r = self.conv1(F.relu(x))
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r = self.conv2(F.relu(r))
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if self.downsample is not None:
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x = self.downsample(x)
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return x + r
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class Encoder_M(nn.Module):
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def __init__(self, backbone):
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super(Encoder_M, self).__init__()
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if backbone == 'resnest101':
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self.conv1_m = nn.Conv2d(1, 128, kernel_size=7, stride=2, padding=3, bias=False)
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self.conv1_o = nn.Conv2d(1, 128, kernel_size=7, stride=2, padding=3, bias=False)
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else:
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self.conv1_m = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.conv1_o = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
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if backbone == 'resnet50':
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resnet = models.resnet50(pretrained=True)
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elif backbone == 'resnet18':
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resnet = models.resnet18(pretrained=True)
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self.conv1 = resnet.conv1
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self.bn1 = resnet.bn1
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self.relu = resnet.relu # 1/2, 64
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self.maxpool = resnet.maxpool
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self.res2 = resnet.layer1 # 1/4, 256
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self.res3 = resnet.layer2 # 1/8, 512
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self.res4 = resnet.layer3 # 1/8, 1024
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self.register_buffer('mean', torch.FloatTensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
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self.register_buffer('std', torch.FloatTensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
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def forward(self, in_f, in_m, in_o):
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f = (in_f - self.mean) / self.std
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m = torch.unsqueeze(in_m, dim=1).float() # add channel dim
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o = torch.unsqueeze(in_o, dim=1).float() # add channel dim
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x = self.conv1(f) + self.conv1_m(m) + self.conv1_o(o)
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x = self.bn1(x)
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c1 = self.relu(x) # 1/2, 64
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x = self.maxpool(c1) # 1/4, 64
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r2 = self.res2(x) # 1/4, 256
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r3 = self.res3(r2) # 1/8, 512
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r4 = self.res4(r3) # 1/8, 1024
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return r4, r3, r2, c1, f
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class Encoder_Q(nn.Module):
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def __init__(self, backbone):
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super(Encoder_Q, self).__init__()
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if backbone == 'resnet50':
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resnet = models.resnet50(pretrained=True)
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elif backbone == 'resnet18':
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resnet = models.resnet18(pretrained=True)
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self.conv1 = resnet.conv1
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self.bn1 = resnet.bn1
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self.relu = resnet.relu # 1/2, 64
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self.maxpool = resnet.maxpool
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self.res2 = resnet.layer1 # 1/4, 256
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self.res3 = resnet.layer2 # 1/8, 512
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self.res4 = resnet.layer3 # 1/8, 1024
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self.register_buffer('mean', torch.FloatTensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
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self.register_buffer('std', torch.FloatTensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
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def forward(self, in_f):
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f = (in_f - self.mean) / self.std
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x = self.conv1(f)
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x = self.bn1(x)
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c1 = self.relu(x) # 1/2, 64
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x = self.maxpool(c1) # 1/4, 64
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r2 = self.res2(x) # 1/4, 256
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r3 = self.res3(r2) # 1/8, 512
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r4 = self.res4(r3) # 1/8, 1024
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return r4, r3, r2, c1, f
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class Refine(nn.Module):
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def __init__(self, backbone, inplanes, planes, scale_factor=2):
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super(Refine, self).__init__()
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self.convFS = nn.Conv2d(inplanes, planes, kernel_size=(3, 3), padding=(1, 1), stride=1)
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self.ResFS = ResBlock(backbone, planes, planes)
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self.ResMM = ResBlock(backbone, planes, planes)
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self.scale_factor = scale_factor
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def forward(self, f, pm):
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s = self.ResFS(self.convFS(f))
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m = s + F.interpolate(pm, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
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m = self.ResMM(m)
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return m
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class Decoder(nn.Module):
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def __init__(self, mdim, scale_rate, backbone):
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super(Decoder, self).__init__()
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self.backbone = backbone
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if backbone == 'resnest101':
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self.convFM = nn.Conv2d(256, mdim, kernel_size=(3, 3), padding=(1, 1), stride=1)
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else:
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self.convFM = nn.Conv2d(1024 // scale_rate, mdim, kernel_size=(3, 3), padding=(1, 1), stride=1)
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self.ResMM = ResBlock(backbone, mdim, mdim)
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self.RF3 = Refine(backbone, 512 // scale_rate, mdim) # 1/8 -> 1/4
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self.RF2 = Refine(backbone, 256 // scale_rate, mdim) # 1/4 -> 1
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self.pred2 = nn.Conv2d(mdim, 2, kernel_size=(3, 3), padding=(1, 1), stride=1)
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def forward(self, r4, r3, r2):
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m4 = self.ResMM(self.convFM(r4))
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m3 = self.RF3(r3, m4) # out: 1/8, 256
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m2 = self.RF2(r2, m3) # out: 1/4, 256
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if self.backbone == 'resnest101':
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p2 = self.pred2(F.relu(m2, inplace=True))
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else:
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p2 = self.pred2(F.relu(m2))
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p = F.interpolate(p2, scale_factor=4, mode='bilinear', align_corners=False)
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return p # , p2, p3, p4
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class Memory(nn.Module):
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def __init__(self):
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super(Memory, self).__init__()
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def forward(self, m_in, m_out, q_in, q_out): # m_in: o,c,t,h,w
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B, D_e, T, H, W = m_in.size()
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_, D_o, _, _, _ = m_out.size()
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mi = m_in.view(B, D_e, T * H * W)
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mi = torch.transpose(mi, 1, 2) # b, THW, emb
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qi = q_in.view(B, D_e, H * W) # b, emb, HW
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p = torch.bmm(mi, qi) # b, THW, HW
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p = p / math.sqrt(D_e)
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p = F.softmax(p, dim=1) # b, THW, HW
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mo = m_out.view(B, D_o, T * H * W)
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mem = torch.bmm(mo, p) # Weighted-sum B, D_o, HW
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mem = mem.view(B, D_o, H, W)
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mem_out = torch.cat([mem, q_out], dim=1)
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return mem_out, p
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class KeyValue(nn.Module):
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# Not using location
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def __init__(self, indim, keydim, valdim):
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super(KeyValue, self).__init__()
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self.Key = nn.Conv2d(indim, keydim, kernel_size=(3, 3), padding=(1, 1), stride=1)
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self.Value = nn.Conv2d(indim, valdim, kernel_size=(3, 3), padding=(1, 1), stride=1)
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def forward(self, x):
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return self.Key(x), self.Value(x)
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class STM(nn.Module):
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def __init__(self, backbone='resnet50'):
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super(STM, self).__init__()
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self.backbone = backbone
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assert backbone == 'resnet50' or backbone == 'resnet18' or backbone == 'resnest101'
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scale_rate = (1 if (backbone == 'resnet50' or backbone == 'resnest101') else 4)
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self.Encoder_M = Encoder_M(backbone)
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self.Encoder_Q = Encoder_Q(backbone)
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self.KV_M_r4 = KeyValue(1024 // scale_rate, keydim=128 // scale_rate, valdim=512 // scale_rate)
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self.KV_Q_r4 = KeyValue(1024 // scale_rate, keydim=128 // scale_rate, valdim=512 // scale_rate)
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self.Memory = Memory()
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self.Decoder = Decoder(256, scale_rate, backbone)
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def Pad_memory(self, mems, num_objects, K):
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pad_mems = []
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for mem in mems:
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pad_mem = ToCuda(torch.zeros(1, K, mem.size()[1], 1, mem.size()[2], mem.size()[3]))
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pad_mem[0, 1:num_objects + 1, :, 0] = mem
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pad_mems.append(pad_mem)
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return pad_mems
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def memorize(self, frame, masks, num_objects):
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# memorize a frame
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num_objects = num_objects[0].item()
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_, K, H, W = masks.shape # B = 1
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(frame, masks), pad = pad_divide_by([frame, masks], 16, (frame.size()[2], frame.size()[3]))
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# make batch arg list
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B_list = {'f': [], 'm': [], 'o': []}
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for o in range(1, num_objects + 1): # 1 - no
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B_list['f'].append(frame)
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B_list['m'].append(masks[:, o])
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B_list['o'].append((torch.sum(masks[:, 1:o], dim=1) + \
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torch.sum(masks[:, o + 1:num_objects + 1], dim=1)).clamp(0, 1))
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# make Batch
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B_ = {}
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for arg in B_list.keys():
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B_[arg] = torch.cat(B_list[arg], dim=0)
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r4, _, _, _, _ = self.Encoder_M(B_['f'], B_['m'], B_['o'])
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k4, v4 = self.KV_M_r4(r4) # num_objects, 128 and 512, H/16, W/16
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k4, v4 = self.Pad_memory([k4, v4], num_objects=num_objects, K=K)
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return k4, v4
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def Soft_aggregation(self, ps, K):
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num_objects, H, W = ps.shape
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em = ToCuda(torch.zeros(1, K, H, W))
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em[0, 0] = torch.prod(1 - ps, dim=0) # bg prob
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em[0, 1:num_objects + 1] = ps # obj prob
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em = torch.clamp(em, 1e-7, 1 - 1e-7)
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logit = torch.log((em / (1 - em)))
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return logit
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def segment(self, frame, keys, values, num_objects):
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num_objects = num_objects[0].item()
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_, K, keydim, T, H, W = keys.shape # B = 1
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# pad
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[frame], pad = pad_divide_by([frame], 16, (frame.size()[2], frame.size()[3]))
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r4, r3, r2, _, _ = self.Encoder_Q(frame)
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k4, v4 = self.KV_Q_r4(r4) # 1, dim, H/16, W/16
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# expand to --- no, c, h, w
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k4e, v4e = k4.expand(num_objects, -1, -1, -1), v4.expand(num_objects, -1, -1, -1)
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r3e, r2e = r3.expand(num_objects, -1, -1, -1), r2.expand(num_objects, -1, -1, -1)
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# memory select kv:(1, K, C, T, H, W)
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m4, viz = self.Memory(keys[0, 1:num_objects + 1], values[0, 1:num_objects + 1], k4e, v4e)
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logits = self.Decoder(m4, r3e, r2e)
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ps = F.softmax(logits, dim=1)[:, 1] # no, h, w
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# ps = indipendant possibility to belong to each object
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logit = self.Soft_aggregation(ps, K) # 1, K, H, W
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if pad[2] + pad[3] > 0:
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logit = logit[:, :, pad[2]:-pad[3], :]
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if pad[0] + pad[1] > 0:
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logit = logit[:, :, :, pad[0]:-pad[1]]
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return logit
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def forward(self, *args, **kwargs):
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if args[1].dim() > 4: # keys
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return self.segment(*args, **kwargs)
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else:
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return self.memorize(*args, **kwargs) |