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train.py
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train.py
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from loss import Loss
from torch.optim import Adam
from tools import custom_print
import datetime
import torch
from val import validation,validation_with_flow
from torch.utils.data import DataLoader
from data_processed import VideoDataset
def train(net, device, q, log_txt_file, val_datapath, models_train_best, models_train_last, lr=1e-4, lr_de_epoch=25000,
epochs=100000, log_interval=100, val_interval=1000):
optimizer = Adam(net.parameters(), lr, weight_decay=1e-6)
loss = Loss().cuda()
best_p, best_j = 0, 1
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
for epoch in range(1, epochs+1):
img, cls_gt, mask_gt = q.get()
net.zero_grad()
img, cls_gt, mask_gt = img.cuda(), cls_gt.cuda(), mask_gt.cuda()
pred_cls, pred_mask = net(img)
all_loss, m_loss, c_loss, s_loss, iou_loss = loss(pred_mask, mask_gt, pred_cls, cls_gt)
all_loss.backward()
epoch_loss = all_loss.item()
m_l = m_loss.item()
c_l = c_loss.item()
s_l = s_loss.item()
i_l = iou_loss.item()
ave_loss += epoch_loss
ave_m_loss += m_l
ave_c_loss += c_l
ave_s_loss += s_l
ave_i_loss += i_l
optimizer.step()
if epoch % log_interval == 0:
ave_loss = ave_loss / log_interval
ave_m_loss = ave_m_loss / log_interval
ave_c_loss = ave_c_loss / log_interval
ave_s_loss = ave_s_loss / log_interval
ave_i_loss = ave_i_loss / log_interval
custom_print(datetime.datetime.now().strftime('%F %T') +
' lr: %e, epoch: [%d/%d], all_loss: [%.4f], m_loss: [%.4f], c_loss: [%.4f], s_loss: [%.4f], i_loss: [%.4f]' %
(lr, epoch, epochs, ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss), log_txt_file, 'a+')
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
if epoch % val_interval == 0:
net.eval()
with torch.no_grad():
custom_print(datetime.datetime.now().strftime('%F %T') +
' now is evaluating the coseg dataset', log_txt_file, 'a+')
ave_p, ave_j = validation(net, val_datapath, device, group_size=5, img_size=224, img_dir_name='image', gt_dir_name='groundtruth',
img_ext=['.jpg', '.jpg', '.jpg', '.jpg'], gt_ext=['.png', '.bmp', '.jpg', '.png'])
if ave_p[3] > best_p:
# follow yourself save condition
best_p = ave_p[3]
best_j = ave_j[0]
torch.save(net.state_dict(), models_train_best)
torch.save(net.state_dict(), models_train_last)
custom_print('-' * 100, log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' iCoseg8 p: [%.4f], j: [%.4f]' %
(ave_p[0], ave_j[0]), log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' MSRC7 p: [%.4f], j: [%.4f]' %
(ave_p[1], ave_j[1]), log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' Int_300 p: [%.4f], j: [%.4f]' %
(ave_p[2], ave_j[2]), log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' PAS_VOC p: [%.4f], j: [%.4f]' %
(ave_p[3], ave_j[3]), log_txt_file, 'a+')
custom_print('-' * 100, log_txt_file, 'a+')
net.train()
if epoch % lr_de_epoch == 0:
optimizer = Adam(net.parameters(), lr/2, weight_decay=1e-6)
lr = lr / 2
def train_finetune(net, data_path,device, bs, log_txt_file, val_datapath, models_train_best, models_train_last, lr=1e-4, lr_de_epoch=25000,
epochs=100000, log_interval=100, val_interval=1000):
optimizer = Adam(net.parameters(), lr, weight_decay=1e-6)
train_loader=DataLoader(VideoDataset(data_path,epochs*bs,use_flow=False), num_workers=4,
batch_size=bs, shuffle=True, drop_last=False,pin_memory=False)
loss = Loss().cuda()
best_p, best_j = 0, 1
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
epoch=0
for data,mask in train_loader:
epoch+=1
data=data.view(-1,data.shape[2],data.shape[3],data.shape[4])
mask=mask.view(-1,mask.shape[2],mask.shape[3])
img,cls_gt, mask_gt = data,torch.rand(bs,78),mask
net.zero_grad()
img, cls_gt, mask_gt = img.cuda(), cls_gt.cuda(), mask_gt.cuda()
pred_cls, pred_mask = net(img)
all_loss, m_loss, c_loss, s_loss, iou_loss = loss(pred_mask, mask_gt, pred_cls, cls_gt)
all_loss.backward()
epoch_loss = all_loss.item()
m_l = m_loss.item()
c_l = c_loss.item()
s_l = s_loss.item()
i_l = iou_loss.item()
ave_loss += epoch_loss
ave_m_loss += m_l
ave_c_loss += c_l
ave_s_loss += s_l
ave_i_loss += i_l
optimizer.step()
if epoch % log_interval == 0:
ave_loss = ave_loss / log_interval
ave_m_loss = ave_m_loss / log_interval
ave_c_loss = ave_c_loss / log_interval
ave_s_loss = ave_s_loss / log_interval
ave_i_loss = ave_i_loss / log_interval
custom_print(datetime.datetime.now().strftime('%F %T') +
' lr: %e, epoch: [%d/%d], all_loss: [%.4f], m_loss: [%.4f], c_loss: [%.4f], s_loss: [%.4f], i_loss: [%.4f]' %
(lr, epoch, epochs, ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss), log_txt_file, 'a+')
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
if epoch % val_interval == 0:
net.eval()
with torch.no_grad():
custom_print(datetime.datetime.now().strftime('%F %T') +
' now is evaluating the coseg dataset', log_txt_file, 'a+')
ave_p, ave_j = validation(net, val_datapath, device, group_size=5, img_size=224, img_dir_name='image', gt_dir_name='groundtruth',
img_ext=['.jpg', '.jpg', '.jpg', '.jpg'], gt_ext=['.png', '.bmp', '.jpg', '.png'])
if ave_p[0] > best_p:
# follow yourself save condition
best_p = ave_p[0]
best_j = ave_j[0]
torch.save(net.state_dict(), models_train_best)
torch.save(net.state_dict(), models_train_last)
custom_print('-' * 100, log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' DAVIS p: [%.4f], j: [%.4f]' %
(ave_p[0], ave_j[0]), log_txt_file, 'a+')
custom_print('-' * 100, log_txt_file, 'a+')
net.train()
if epoch % lr_de_epoch == 0:
optimizer = Adam(net.parameters(), lr/2, weight_decay=1e-6)
lr = lr / 2
def train_finetune_with_flow(net, data_path,device, bs, log_txt_file, val_datapath, models_train_best, models_train_last, lr=1e-4, lr_de_epoch=25000,
epochs=100000, log_interval=100, val_interval=1000):
optimizer = Adam(net.parameters(), lr, weight_decay=1e-6)
train_loader=DataLoader(VideoDataset(data_path,epochs*bs,use_flow=True), num_workers=4,
batch_size=bs, shuffle=True, drop_last=False,pin_memory=False)
loss = Loss().cuda()
best_p, best_j = 0, 1
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
epoch=0
for data,flow,mask in train_loader:
epoch+=1
data=data.view(-1,data.shape[2],data.shape[3],data.shape[4])
flow=flow.view(-1,flow.shape[2],flow.shape[3],flow.shape[4])
mask=mask.view(-1,mask.shape[2],mask.shape[3])
flow=flow.cuda()
img,cls_gt, mask_gt = data,torch.rand(bs,78),mask
net.zero_grad()
img, cls_gt, mask_gt = img.cuda(), cls_gt.cuda(), mask_gt.cuda()
pred_cls,pred_mask = net(img,flow)
all_loss, m_loss, c_loss, s_loss, iou_loss = loss(pred_mask, mask_gt, pred_cls, cls_gt)
all_loss.backward()
epoch_loss = all_loss.item()
m_l = m_loss.item()
c_l = c_loss.item()
s_l = s_loss.item()
i_l = iou_loss.item()
ave_loss += epoch_loss
ave_m_loss += m_l
ave_c_loss += c_l
ave_s_loss += s_l
ave_i_loss += i_l
optimizer.step()
if epoch % log_interval == 0:
ave_loss = ave_loss / log_interval
ave_m_loss = ave_m_loss / log_interval
ave_c_loss = ave_c_loss / log_interval
ave_s_loss = ave_s_loss / log_interval
ave_i_loss = ave_i_loss / log_interval
custom_print(datetime.datetime.now().strftime('%F %T') +
' lr: %e, epoch: [%d/%d], all_loss: [%.4f], m_loss: [%.4f], c_loss: [%.4f], s_loss: [%.4f], i_loss: [%.4f]' %
(lr, epoch, epochs, ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss), log_txt_file, 'a+')
ave_loss, ave_m_loss, ave_c_loss, ave_s_loss, ave_i_loss = 0, 0, 0, 0, 0
if epoch % val_interval == 0:
net.eval()
with torch.no_grad():
custom_print(datetime.datetime.now().strftime('%F %T') +
' now is evaluating the coseg dataset', log_txt_file, 'a+')
ave_p, ave_j = validation_with_flow(net, val_datapath, device, group_size=5, img_size=224, img_dir_name='image', gt_dir_name='groundtruth',
img_ext=['.jpg', '.jpg', '.jpg', '.jpg'], gt_ext=['.png', '.bmp', '.jpg', '.png'])
if ave_p[0] > best_p:
# follow yourself save condition
best_p = ave_p[0]
best_j = ave_j[0]
torch.save(net.state_dict(), models_train_best)
torch.save(net.state_dict(), models_train_last)
custom_print('-' * 100, log_txt_file, 'a+')
custom_print(datetime.datetime.now().strftime('%F %T') + ' DAVIS p: [%.4f], j: [%.4f]' %
(ave_p[0], ave_j[0]), log_txt_file, 'a+')
custom_print('-' * 100, log_txt_file, 'a+')
net.train()
if epoch % lr_de_epoch == 0:
optimizer = Adam(net.parameters(), lr/2, weight_decay=1e-6)
lr = lr / 2