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joint_solver.py
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joint_solver.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
from networks.joint_poolnet import build_model, weights_init
import scipy.misc as sm
import numpy as np
import os
import torchvision.utils as vutils
import cv2
import math
import time
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 = [8,]
self.build_model()
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.model))
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):
self.net = build_model(self.config.arch)
if self.config.cuda:
self.net = self.net.cuda()
# self.net.train()
self.net.eval() # use_global_stats = True
self.net.apply(weights_init)
if self.config.load == '':
self.net.base.load_pretrained_model(torch.load(self.config.pretrained_model))
else:
self.net.load_state_dict(torch.load(self.config.load))
self.lr = self.config.lr
self.wd = self.config.wd
self.optimizer = Adam(filter(lambda p: p.requires_grad, self.net.parameters()), lr=self.lr, weight_decay=self.wd)
self.print_network(self.net, 'PoolNet Structure')
def test(self, test_mode=1):
mode_name = ['edge_fuse', 'sal_fuse']
EPSILON = 1e-8
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'])
if test_mode == 0:
images = images.numpy()[0].transpose((1,2,0))
scale = [0.5, 1, 1.5, 2] # uncomment for multi-scale testing
# scale = [1]
multi_fuse = np.zeros(im_size, np.float32)
for k in range(0, len(scale)):
im_ = cv2.resize(images, None, fx=scale[k], fy=scale[k], interpolation=cv2.INTER_LINEAR)
im_ = im_.transpose((2, 0, 1))
im_ = torch.Tensor(im_[np.newaxis, ...])
with torch.no_grad():
im_ = Variable(im_)
if self.config.cuda:
im_ = im_.cuda()
preds = self.net(im_, mode=test_mode)
pred_0 = np.squeeze(torch.sigmoid(preds[1][0]).cpu().data.numpy())
pred_1 = np.squeeze(torch.sigmoid(preds[1][1]).cpu().data.numpy())
pred_2 = np.squeeze(torch.sigmoid(preds[1][2]).cpu().data.numpy())
pred_fuse = np.squeeze(torch.sigmoid(preds[0]).cpu().data.numpy())
pred = (pred_0 + pred_1 + pred_2 + pred_fuse) / 4
pred = (pred - np.min(pred) + EPSILON) / (np.max(pred) - np.min(pred) + EPSILON)
pred = cv2.resize(pred, (im_size[1], im_size[0]), interpolation=cv2.INTER_LINEAR)
multi_fuse += pred
multi_fuse /= len(scale)
multi_fuse = 255 * (1 - multi_fuse)
cv2.imwrite(os.path.join(self.config.test_fold, name[:-4] + '_' + mode_name[test_mode] + '.png'), multi_fuse)
elif test_mode == 1:
with torch.no_grad():
images = Variable(images)
if self.config.cuda:
images = images.cuda()
preds = self.net(images, mode=test_mode)
pred = np.squeeze(torch.sigmoid(preds).cpu().data.numpy())
multi_fuse = 255 * pred
cv2.imwrite(os.path.join(self.config.test_fold, name[:-4] + '_' + mode_name[test_mode] + '.png'), multi_fuse)
time_e = time.time()
print('Speed: %f FPS' % (img_num/(time_e-time_s)))
print('Test Done!')
# training phase
def train(self):
iter_num = 30000 # each batch only train 30000 iters.(This number is just a random choice...)
aveGrad = 0
for epoch in range(self.config.epoch):
r_edge_loss, r_sal_loss, r_sum_loss= 0,0,0
self.net.zero_grad()
for i, data_batch in enumerate(self.train_loader):
if (i + 1) == iter_num: break
edge_image, edge_label, sal_image, sal_label = data_batch['edge_image'], data_batch['edge_label'], data_batch['sal_image'], data_batch['sal_label']
if (sal_image.size(2) != sal_label.size(2)) or (sal_image.size(3) != sal_label.size(3)):
print('IMAGE ERROR, PASSING```')
continue
edge_image, edge_label, sal_image, sal_label= Variable(edge_image), Variable(edge_label), Variable(sal_image), Variable(sal_label)
if self.config.cuda:
edge_image, edge_label, sal_image, sal_label = edge_image.cuda(), edge_label.cuda(), sal_image.cuda(), sal_label.cuda()
# edge part
edge_pred = self.net(edge_image, mode=0)
edge_loss_fuse = bce2d(edge_pred[0], edge_label, reduction='sum')
edge_loss_part = []
for ix in edge_pred[1]:
edge_loss_part.append(bce2d(ix, edge_label, reduction='sum'))
edge_loss = (edge_loss_fuse + sum(edge_loss_part)) / (self.iter_size * self.config.batch_size)
r_edge_loss += edge_loss.data
# sal part
sal_pred = self.net(sal_image, mode=1)
sal_loss_fuse = F.binary_cross_entropy_with_logits(sal_pred, sal_label, reduction='sum')
sal_loss = sal_loss_fuse / (self.iter_size * self.config.batch_size)
r_sal_loss += sal_loss.data
loss = sal_loss + edge_loss
r_sum_loss += loss.data
loss.backward()
aveGrad += 1
# accumulate gradients as done in DSS
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] || Edge : %10.4f || Sal : %10.4f || Sum : %10.4f' % (
epoch, self.config.epoch, i, iter_num, r_edge_loss/x_showEvery, r_sal_loss/x_showEvery, r_sum_loss/x_showEvery))
print('Learning rate: ' + str(self.lr))
r_edge_loss, r_sal_loss, r_sum_loss= 0,0,0
if (epoch + 1) % self.config.epoch_save == 0:
torch.save(self.net.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(filter(lambda p: p.requires_grad, self.net.parameters()), lr=self.lr, weight_decay=self.wd)
torch.save(self.net.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)