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- import torch
- from collections import OrderedDict
- from torch.nn import utils, functional as F
- from torch.optim import Adam, SGD
- from torch.autograd import Variable
- from torch.backends import cudnn
- from model import build_model, weights_init
- import scipy.misc as sm
- import numpy as np
- import os
- import torchvision.utils as vutils
- import cv2
- import torch.nn.functional as F
- import math
- import time
- import sys
- import PIL.Image
- import scipy.io
- import os
- import logging
- EPSILON = 1e-8
- p = OrderedDict()
- from dataset import get_loader
- base_model_cfg = 'resnet'
- p['lr_bone'] = 5e-5 # Learning rate resnet:5e-5, vgg:2e-5
- p['lr_branch'] = 0.025 # Learning rate
- p['wd'] = 0.0005 # Weight decay
- p['momentum'] = 0.90 # Momentum
- lr_decay_epoch = [15, 24] # [6, 9], now x3 #15
- nAveGrad = 10 # Update the weights once in 'nAveGrad' forward passes
- showEvery = 50
- tmp_path = 'tmp_see'
- class Solver(object):
- def __init__(self, train_loader, test_loader, config, save_fold=None):
- self.train_loader = train_loader
- self.test_loader = test_loader
- self.config = config
- self.save_fold = save_fold
- self.mean = torch.Tensor([123.68, 116.779, 103.939]).view(3, 1, 1) / 255.
- # inference: choose the side map (see paper)
- if config.visdom:
- self.visual = Viz_visdom("trueUnify", 1)
- self.build_model()
- if self.config.pre_trained: self.net.load_state_dict(torch.load(self.config.pre_trained))
- if config.mode == 'train':
- self.log_output = open("%s/logs/log.txt" % config.save_fold, 'w')
- else:
- print('Loading pre-trained model from %s...' % self.config.model)
- self.net_bone.load_state_dict(torch.load(self.config.model))
- self.net_bone.eval()
- 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))
- def get_params(self, base_lr):
- ml = []
- for name, module in self.net_bone.named_children():
- print(name)
- if name == 'loss_weight':
- ml.append({'params': module.parameters(), 'lr': p['lr_branch']})
- else:
- ml.append({'params': module.parameters()})
- return ml
- # build the network
- def build_model(self):
- self.net_bone = build_model(base_model_cfg)
- if self.config.cuda:
- self.net_bone = self.net_bone.cuda()
-
- self.net_bone.eval() # use_global_stats = True
- self.net_bone.apply(weights_init)
- if self.config.mode == 'train':
- if self.config.load_bone == '':
- if base_model_cfg == 'vgg':
- self.net_bone.base.load_pretrained_model(torch.load(self.config.vgg))
- elif base_model_cfg == 'resnet':
- self.net_bone.base.load_state_dict(torch.load(self.config.resnet))
- if self.config.load_bone != '': self.net_bone.load_state_dict(torch.load(self.config.load_bone))
- self.lr_bone = p['lr_bone']
- self.lr_branch = p['lr_branch']
- self.optimizer_bone = Adam(filter(lambda p: p.requires_grad, self.net_bone.parameters()), lr=self.lr_bone, weight_decay=p['wd'])
- self.print_network(self.net_bone, 'trueUnify bone part')
- # update the learning rate
- def update_lr(self, rate):
- for param_group in self.optimizer.param_groups:
- param_group['lr'] = param_group['lr'] * rate
- def test(self, test_mode=0):
- EPSILON = 1e-8
- img_num = len(self.test_loader)
- time_t = 0.0
- name_t = 'EGNet_ResNet50/'
- if not os.path.exists(os.path.join(self.save_fold, name_t)):
- os.mkdir(os.path.join(self.save_fold, name_t))
- for i, data_batch in enumerate(self.test_loader):
- self.config.test_fold = self.save_fold
- print(self.config.test_fold)
- 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()
- print(images.size())
- time_start = time.time()
- up_edge, up_sal, up_sal_f = self.net_bone(images)
- torch.cuda.synchronize()
- time_end = time.time()
- print(time_end - time_start)
- time_t = time_t + time_end - time_start
- pred = np.squeeze(torch.sigmoid(up_sal_f[-1]).cpu().data.numpy())
- multi_fuse = 255 * pred
-
-
- cv2.imwrite(os.path.join(self.config.test_fold,name_t, name[:-4] + '.png'), multi_fuse)
-
- print("--- %s seconds ---" % (time_t))
- print('Test Done!')
-
- # training phase
- def train(self):
- iter_num = len(self.train_loader.dataset) // self.config.batch_size
- aveGrad = 0
- F_v = 0
- if not os.path.exists(tmp_path):
- os.mkdir(tmp_path)
- for epoch in range(self.config.epoch):
- r_edge_loss, r_sal_loss, r_sum_loss= 0,0,0
- self.net_bone.zero_grad()
- for i, data_batch in enumerate(self.train_loader):
- sal_image, sal_label, sal_edge = data_batch['sal_image'], data_batch['sal_label'], data_batch['sal_edge']
- if sal_image.size()[2:] != sal_label.size()[2:]:
- print("Skip this batch")
- continue
- sal_image, sal_label, sal_edge = Variable(sal_image), Variable(sal_label), Variable(sal_edge)
- if self.config.cuda:
- sal_image, sal_label, sal_edge = sal_image.cuda(), sal_label.cuda(), sal_edge.cuda()
- up_edge, up_sal, up_sal_f = self.net_bone(sal_image)
- # edge part
- edge_loss = []
- for ix in up_edge:
- edge_loss.append(bce2d_new(ix, sal_edge, reduction='sum'))
- edge_loss = sum(edge_loss) / (nAveGrad * self.config.batch_size)
- r_edge_loss += edge_loss.data
- # sal part
- sal_loss1= []
- sal_loss2 = []
- for ix in up_sal:
- sal_loss1.append(F.binary_cross_entropy_with_logits(ix, sal_label, reduction='sum'))
- for ix in up_sal_f:
- sal_loss2.append(F.binary_cross_entropy_with_logits(ix, sal_label, reduction='sum'))
- sal_loss = (sum(sal_loss1) + sum(sal_loss2)) / (nAveGrad * self.config.batch_size)
-
- r_sal_loss += sal_loss.data
- loss = sal_loss + edge_loss
- r_sum_loss += loss.data
- loss.backward()
- aveGrad += 1
- if aveGrad % nAveGrad == 0:
-
- self.optimizer_bone.step()
- self.optimizer_bone.zero_grad()
- aveGrad = 0
- if i % showEvery == 0:
- print('epoch: [%2d/%2d], iter: [%5d/%5d] || Edge : %10.4f || Sal : %10.4f || Sum : %10.4f' % (
- epoch, self.config.epoch, i, iter_num, r_edge_loss*(nAveGrad * self.config.batch_size)/showEvery,
- r_sal_loss*(nAveGrad * self.config.batch_size)/showEvery,
- r_sum_loss*(nAveGrad * self.config.batch_size)/showEvery))
- print('Learning rate: ' + str(self.lr_bone))
- r_edge_loss, r_sal_loss, r_sum_loss= 0,0,0
- if i % 200 == 0:
- vutils.save_image(torch.sigmoid(up_sal_f[-1].data), tmp_path+'/iter%d-sal-0.jpg' % i, normalize=True, padding = 0)
- vutils.save_image(sal_image.data, tmp_path+'/iter%d-sal-data.jpg' % i, padding = 0)
- vutils.save_image(sal_label.data, tmp_path+'/iter%d-sal-target.jpg' % i, padding = 0)
-
- if (epoch + 1) % self.config.epoch_save == 0:
- torch.save(self.net_bone.state_dict(), '%s/models/epoch_%d_bone.pth' % (self.config.save_fold, epoch + 1))
-
- if epoch in lr_decay_epoch:
- self.lr_bone = self.lr_bone * 0.1
- self.optimizer_bone = Adam(filter(lambda p: p.requires_grad, self.net_bone.parameters()), lr=self.lr_bone, weight_decay=p['wd'])
- torch.save(self.net_bone.state_dict(), '%s/models/final_bone.pth' % self.config.save_fold)
-
- def bce2d_new(input, target, reduction=None):
- assert(input.size() == target.size())
- pos = torch.eq(target, 1).float()
- neg = torch.eq(target, 0).float()
- # ing = ((torch.gt(target, 0) & torch.lt(target, 1))).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)
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