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Solver_joint.py
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Solver_joint.py
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import torch
from collections import OrderedDict
from torch.nn import utils, functional as F
from torch.optim import Adam
from torch.autograd import Variable
from torch.backends import cudnn
import scipy.misc as sm
import numpy as np
from networks.deeplab_resnet import resnet50_locate
from networks.vgg import vgg16_locate
import os
import torchvision.utils as vutils
import cv2
import math
import time
import mobula
from attention_sampler.attsampler_th import AttSampler
from KRN_edge import *
from KRN import KRN
mobula.op.load('attention_sampler')
class Solver(object):
def __init__(self, train_loader, test_loader, config):
self.train_loader = train_loader
self.test_loader = test_loader
self.config = config
self.iter_size = config.iter_size
self.show_every = config.show_every
self.lr_decay_epoch = [9]
self.build_model()
# self.build_model1()
if config.mode == 'test':
print('Loading pre-trained model from %s...' % self.config.model)
if self.config.cuda:
self.net.load_state_dict(torch.load(self.config.clm_model, map_location={'cuda:2':'cuda:0'}))
self.net_hou.load_state_dict(torch.load(self.config.fsm_model, map_location={'cuda:2':'cuda:0'}))
else:
self.net.load_state_dict(torch.load(self.config.model, map_location='cpu'))
self.net.eval()
# print the network information and parameter numbers
def print_network(self, model, name):
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(name)
print(model)
print("The number of parameters: {}".format(num_params))
# build the network
def build_model(self): # 训练好的模型
self.net = KRN(self.config.arch, *extra_layer(self.config.arch, resnet50_locate()))
if self.config.cuda:
self.net = self.net.cuda()
self.net.eval()
if self.config.mode == 'train':
self.net.load_state_dict(
torch.load(self.config.clm_model))
self.net_hou = KRN_edge(self.config.arch, *extra_layer(self.config.arch, resnet50_locate()))
if self.config.cuda:
self.net_hou = self.net_hou.cuda()
self.net_hou.eval() # use_global_stats = True
if self.config.mode == 'train':
self.net_hou.load_state_dict(torch.load(self.config.fsm_model))
self.lr = self.config.lr
self.wd = self.config.wd
self.optimizer = Adam([{'params': filter(lambda p: p.requires_grad, self.net.parameters())},
{'params': filter(lambda p: p.requires_grad, self.net_hou.parameters())}], lr=self.lr,
weight_decay=self.wd)
def test(self):
mode_name = 'sal_fuse'
time_s = time.time()
img_num = len(self.test_loader)
for i, data_batch in enumerate(self.test_loader):
images, name, im_size = data_batch['image'], data_batch['name'][0], np.asarray(data_batch['size'])
with torch.no_grad():
images = Variable(images)
if self.config.cuda:
images = images.cuda()
feasum_out, merge_solid, out_merge_solid1, out_merge_solid2, out_merge_solid3, out_merge_solid4 = self.net(
images)
map_s = feasum_out
map_sx = torch.unsqueeze(torch.max(map_s, 3)[0], dim=3) # ([1, 400, 1])
map_sx = torch.squeeze(map_sx, dim=1)
map_sy = torch.unsqueeze(torch.max(map_s, 2)[0], dim=3) # ([1, 342, 1])
map_sy = torch.squeeze(map_sy, dim=1)
sum_sx = torch.sum(map_sx, dim=(1, 2), keepdim=True)
sum_sy = torch.sum(map_sy, dim=(1, 2), keepdim=True)
map_sx /= sum_sx
map_sy /= sum_sy
semi_pred, grid = AttSampler(scale=1, dense=2)(images, map_sx, map_sy)
mapsssss, grid5 = AttSampler(scale=1, dense=2)(map_s, map_sx, map_sy)
# mapsssss,grid5 = AttSampler(scale=1, dense=2)(map_s, map_sx, map_sy)
data_pred, merge_solid, out_merge_solid1, out_merge_contour1, out_merge_solid2, out_merge_contour2, out_merge_solid3, out_merge_contour3, out_merge_solid4, out_merge_contour4 = self.net_hou(
semi_pred)
##################################restore##############################################
x_index = grid[0, 1, :, 0] # 400
y_index = grid[0, :, 1, 1] # 300
new_data_size = tuple(data_pred.shape[1:4])
new_data = torch.empty(new_data_size[0], new_data_size[1], new_data_size[2],
device=images.device) # 创建新的图
new_data_final = torch.empty(new_data_size[0], new_data_size[1], new_data_size[2],
device=images.device) # 创建新的图
x_index = (x_index + 1) * new_data_size[2] / 2
y_index = (y_index + 1) * new_data_size[1] / 2
xl = 0
grid_l = x_index[0]
data_l = data_pred[:, :, :, 0]
for num in range(1, len(x_index)):
grid_r = x_index[num]
xr = torch.ceil(grid_r) - 1
xr = xr.int()
data_r = data_pred[:, :, :, num]
for h in range(xl + 1, xr + 1):
if h == grid_r:
new_data[:, :, h] = data_r
else:
new_data[:, :, h] = ((h - grid_l) * data_r / (grid_r - grid_l)) + (
(grid_r - h) * data_l / (grid_r - grid_l))
xl = xr
grid_l = grid_r
data_l = data_r
new_data[:, :, 0] = new_data[:, :, 1]
try:
for h in range(xr + 1, len(x_index)):
new_data[:, :, h] = new_data[:, :, xr]
except:
print('h', h)
print('xr', xr)
yl = 0
grid1_l = y_index[0]
data1_l = new_data[:, 0, :]
for num in range(1, len(y_index)):
grid1_r = y_index[num]
yr = torch.ceil(grid1_r) - 1
yr = yr.int()
data1_r = new_data[:, num, :]
for h in range(yl + 1, yr + 1):
if h == grid1_r:
new_data_final[:, h, :] = data1_r
else:
new_data_final[:, h, :] = ((h - grid1_l) * data1_r / (grid1_r - grid1_l)) + (
(grid1_r - h) * data1_l / (grid1_r - grid1_l))
yl = yr
grid1_l = grid1_r
data1_l = data1_r
new_data_final[:, 0, :] = new_data_final[:, 1, :]
try:
for h in range(yr + 1, len(y_index)):
new_data_final[:, h, :] = new_data_final[:, yr, :]
except:
print('h', h)
print('yr', yr)
preds = torch.unsqueeze(new_data_final, dim=1)
pred = np.squeeze(preds).cpu().data.numpy()
multi_fuse = 255 * pred
cv2.imwrite(os.path.join(self.config.test_fold, name[:-4] + '_' + mode_name + '.png'), multi_fuse)
time_e = time.time()
print('Speed: %f FPS' % (img_num / (time_e - time_s)))
print('Test Done!')
def train(self):
iter_num = len(self.train_loader.dataset) // self.config.batch_size
aveGrad = 0
x_showEvery = 0
for epoch in range(self.config.epoch):
r_sal_loss = 0
r_sal_loss1 = 0
self.net_hou.zero_grad()
for i, data_batch in enumerate(self.train_loader):
sal_image, sal_label, sal_edge, sal_saliency = data_batch['sal_image'], data_batch['sal_label'], \
data_batch['sal_edge'], data_batch['sal_saliency']
if (sal_image.size(2) != sal_label.size(2)) or (sal_image.size(3) != sal_label.size(3)):
print('IMAGE ERROR, PASSING```')
continue
sal_image, sal_label, sal_edge, sal_saliency = Variable(sal_image), Variable(sal_label), Variable(
sal_edge), Variable(sal_saliency)
if self.config.cuda:
# cudnn.benchmark = True
sal_image, sal_label, sal_edge, sal_saliency = sal_image.cuda(), sal_label.cuda(), sal_edge.cuda(), sal_saliency.cuda()
feasum_out, merge_solid, out_merge_solid1, out_merge_solid2, out_merge_solid3, out_merge_solid4 = self.net(
sal_image)
high_score = torch.trunc(sal_saliency, out=None)
sal_feasum_out_loss = mulloss(feasum_out, sal_saliency, high_score)
sal_solid_loss = mulloss(merge_solid, sal_saliency, high_score)
sal_solid_loss1 = mulloss(out_merge_solid1, sal_saliency, high_score)
sal_solid_loss2 = mulloss(out_merge_solid2, sal_saliency, high_score)
sal_solid_loss3 = mulloss(out_merge_solid3, sal_saliency, high_score)
sal_solid_loss4 = mulloss(out_merge_solid4, sal_saliency, high_score)
sal_sal_loss = (
2 * sal_feasum_out_loss + sal_solid_loss + sal_solid_loss1 + sal_solid_loss2 + sal_solid_loss3 + sal_solid_loss4)
map_s = feasum_out
map_sx = torch.unsqueeze(torch.max(map_s, 3)[0], dim=3) # ([1, 400, 1])
map_sx = torch.squeeze(map_sx, dim=1)
map_sy = torch.unsqueeze(torch.max(map_s, 2)[0], dim=3) # ([1, 342, 1])
map_sy = torch.squeeze(map_sy, dim=1)
sum_sx = torch.sum(map_sx, dim=(1, 2), keepdim=True)
sum_sy = torch.sum(map_sy, dim=(1, 2), keepdim=True)
map_sx /= sum_sx
map_sy /= sum_sy
#################################################################################################
semi_pred, grid = AttSampler(scale=1, dense=2)(sal_image, map_sx, map_sy) #
edge, grid = AttSampler(scale=1, dense=2)(sal_edge, map_sx, map_sy) #
label, grid = AttSampler(scale=1, dense=2)(sal_label, map_sx, map_sy) #
feasum_out, merge_solid, out_merge_solid1, out_merge_contour1, out_merge_solid2, out_merge_contour2, out_merge_solid3, out_merge_contour3, out_merge_solid4, out_merge_contour4 = self.net_hou(
semi_pred)
data_pred = feasum_out
########################### restore #####################
x_index = grid[0, 1, :, 0] # 400
y_index = grid[0, :, 1, 1] # 300
new_data_size = tuple(data_pred.shape[1:4])
new_data = torch.empty(new_data_size[0], new_data_size[1], new_data_size[2],
device=sal_image.device)
new_data_final = torch.empty(new_data_size[0], new_data_size[1], new_data_size[2],
device=sal_image.device)
x_index = (x_index + 1) * new_data_size[2] / 2
y_index = (y_index + 1) * new_data_size[1] / 2
xl = 0
grid_l = x_index[0]
data_l = data_pred[:, :, :, 0]
for num in range(1, len(x_index)):
grid_r = x_index[num]
xr = torch.ceil(grid_r) - 1
xr = xr.int()
data_r = data_pred[:, :, :, num]
for h in range(xl + 1, xr + 1):
if h == grid_r:
new_data[:, :, h] = data_r
else:
new_data[:, :, h] = ((h - grid_l) * data_r / (grid_r - grid_l)) + (
(grid_r - h) * data_l / (grid_r - grid_l))
xl = xr
grid_l = grid_r
data_l = data_r
new_data[:, :, 0] = new_data[:, :, 1]
try:
for h in range(xr + 1, len(x_index)):
new_data[:, :, h] = new_data[:, :, xr]
except:
print('h', h)
print('xr', xr)
yl = 0
grid1_l = y_index[0]
data1_l = new_data[:, 0, :]
for num in range(1, len(y_index)):
grid1_r = y_index[num]
yr = torch.ceil(grid1_r) - 1
yr = yr.int()
data1_r = new_data[:, num, :]
for h in range(yl + 1, yr + 1):
if h == grid1_r:
new_data_final[:, h, :] = data1_r
else:
new_data_final[:, h, :] = ((h - grid1_l) * data1_r / (grid1_r - grid1_l)) + (
(grid1_r - h) * data1_l / (grid1_r - grid1_l))
yl = yr
grid1_l = grid1_r
data1_l = data1_r
new_data_final[:, 0, :] = new_data_final[:, 1, :]
try:
for h in range(yr + 1, len(y_index)):
new_data_final[:, h, :] = new_data_final[:, yr, :]
except:
print('h', h)
print('yr', yr)
new_data_final = torch.unsqueeze(new_data_final, dim=1)
########################### loss function #####################
solid_loss = F.binary_cross_entropy(new_data_final, sal_label, reduction='mean') + iou_loss(
new_data_final, sal_label)
solid_loss0 = F.binary_cross_entropy(merge_solid, label, reduction='mean') + iou_loss(merge_solid,
label)
solid_loss1 = F.binary_cross_entropy(out_merge_solid1, label, reduction='mean') + iou_loss(
out_merge_solid1, label)
edge_loss1 = bce2d(out_merge_contour1, edge, reduction='mean')
solid_loss2 = F.binary_cross_entropy(out_merge_solid2, label, reduction='mean') + iou_loss(
out_merge_solid2, label)
edge_loss2 = bce2d(out_merge_contour2, edge, reduction='mean')
solid_loss3 = F.binary_cross_entropy(out_merge_solid3, label, reduction='mean') + iou_loss(
out_merge_solid3, label)
edge_loss3 = bce2d(out_merge_contour3, edge, reduction='mean')
solid_loss4 = F.binary_cross_entropy(out_merge_solid4, label, reduction='mean') + iou_loss(
out_merge_solid4, label)
edge_loss4 = bce2d(out_merge_contour4, edge, reduction='mean')
final_sal_loss = (
2 * solid_loss + solid_loss0 + edge_loss1 + solid_loss1 + edge_loss2 + solid_loss2 + edge_loss3 + solid_loss3 + edge_loss4 + solid_loss4)
sal_loss = (sal_sal_loss + final_sal_loss) / (self.iter_size * self.config.batch_size)
sal_loss_fuse = F.binary_cross_entropy(new_data_final, sal_label, reduction='sum')
sal_loss_fuse1 = F.binary_cross_entropy(new_data_final, sal_label, reduction='sum')
r_sal_loss += sal_loss_fuse.data
r_sal_loss1 += sal_loss_fuse1.data
x_showEvery += 1
sal_loss.backward()
aveGrad += 1
if aveGrad % self.iter_size == 0:
self.optimizer.step()
self.optimizer.zero_grad()
aveGrad = 0
if i % (self.show_every // self.config.batch_size) == 0:
# if i == 0:
# x_showEvery = 1
print('epoch: [%2d/%2d], iter: [%5d/%5d] || Sal : %10.4f || Sal1 : %10.4f' % (
epoch, self.config.epoch, i, iter_num, r_sal_loss / x_showEvery, r_sal_loss1 / x_showEvery))
print('Learning rate: ' + str(self.lr))
r_sal_loss = 0
r_sal_loss1 = 0
x_showEvery = 0
if (epoch + 1) % self.config.epoch_save == 0:
torch.save(self.net.state_dict(), '%s/models/epoch_net_%d.pth' % (self.config.save_folder, epoch + 1))
torch.save(self.net_hou.state_dict(), '%s/models/epoch_%d.pth' % (self.config.save_folder, epoch + 1))
if epoch in self.lr_decay_epoch:
self.lr = self.lr * 0.1
self.optimizer = Adam([{'params': filter(lambda p: p.requires_grad, self.net.parameters())},
{'params': filter(lambda p: p.requires_grad, self.net_hou.parameters())}],
lr=self.lr,
weight_decay=self.wd)
torch.save(self.net.state_dict(), '%s/models/net_final.pth' % self.config.save_folder)
torch.save(self.net_hou.state_dict(), '%s/models/final.pth' % self.config.save_folder)
def bce2d(input, target, reduction=None):
assert (input.size() == target.size())
pos = torch.eq(target, 1).float()
neg = torch.eq(target, 0).float()
num_pos = torch.sum(pos)
num_neg = torch.sum(neg)
num_total = num_pos + num_neg
alpha = num_neg / num_total
beta = 1.1 * num_pos / num_total
# target pixel = 1 -> weight beta
# target pixel = 0 -> weight 1-beta
weights = alpha * pos + beta * neg
return F.binary_cross_entropy_with_logits(input, target, weights, reduction=reduction)
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
# compute the IoU of the foreground
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]) - Iand1
IoU1 = Iand1 / Ior1
# IoU loss is (1-IoU1)
IoU = IoU + (1 - IoU1)
return IoU / b
class IOU(torch.nn.Module):
def __init__(self, size_average=True):
super(IOU, self).__init__()
self.size_average = size_average
def forward(self, pred, target):
return _iou(pred, target, self.size_average)
iou_loss = IOU(size_average=True)
class Mul_loss(nn.Module):
"""L1 Charbonnierloss."""
def __init__(self):
super(Mul_loss, self).__init__()
self.eps = 1e-8
def forward(self, x, y, gt):
vx = x - torch.mean(x)
vy = y - torch.mean(y)
CCloss = torch.sum(vx * vy) / ((torch.sqrt(torch.sum(vx ** 2)) * torch.sqrt(torch.sum(vy ** 2))) + self.eps)
x_map_norm = (x - torch.mean(x)) / (torch.std(x) + self.eps)
y_map_norm = (y - torch.mean(y)) / (torch.std(y) + self.eps)
diff = torch.abs(x_map_norm - y_map_norm)
m = torch.sum(torch.mul(diff, gt))
# print(m)
num = torch.sum(gt) + self.eps #
NSSloss = torch.div(m, num)
max_x = torch.max(x)
x = x / max_x
sum_x = torch.sum(x)
sum_y = torch.sum(y)
x = x / (sum_x + self.eps)
y = y / (sum_y + self.eps)
KLDloss = torch.sum(y * torch.log(self.eps + y / (x + self.eps)))
return 1 - CCloss + NSSloss + KLDloss
mulloss = Mul_loss()