def main(opt):
if not os.path.exists(opt.resume):
os.makedirs(opt.resume)
if not os.path.exists(opt.logroot):
os.makedirs(opt.logroot)
#log_dir_name = 'split'+opt.split + '/'
log_dir_name = 'split'+opt.split + '/'+str(opt.manualSeed)+'/'
opt.resume = os.path.join(opt.resume,log_dir_name)
log_path = os.path.join(opt.logroot,log_dir_name)
if not os.path.exists(log_path):
os.makedirs(log_path)
#log_file_name = log_path + 'ucf_log_st.txt'
log_file_name = log_path + 'ucf_log_v5.0_st_'+str(opt.manualSeed)+'.txt'
with open(log_file_name,'a+') as file:
file.write('manualSeed is %d \n' % opt.manualSeed)
file.write('state_dim is %d \n' % opt.state_dim)
file.write('num_bottleneck is %d \n' % opt.num_bottleneck)
paths = config.Paths()
train_datalist = '/home/mcislab/wangruiqi/IJCV2019/data/ucf101Vid_train_lin_split'+opt.split+'.txt'
test_datalist = '/home/mcislab/wangruiqi/IJCV2019/data/ucf101Vid_val_lin_split'+opt.split+'.txt'
#train_datalist = '/home/mcislab/wangruiqi/IJCV2019/data/test.txt'
#test_datalist = '/home/mcislab/wangruiqi/IJCV2019/data/test.txt'
#train_dataset = dataset(train_datalist, paths.detect_root_ucf_mmdet, paths.img_root_ucf,paths.rgb_res18_ucf,paths.rgb_res18_ucf, opt)
train_dataset = dataset(train_datalist, paths.bninception_ucf,opt)####zhao changed ###paths.resnet50_ucf_rgbflow_same
train_dataloader = DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.workers, drop_last=False)
#test_dataset = dataset(test_datalist, paths.detect_root_ucf_mmdet, paths.img_root_ucf,paths.rgb_res18_ucf,paths.rgb_res18_ucf, opt)
test_dataset = dataset(test_datalist, paths.bninception_ucf,opt)###paths.resnet50_ucf_rgbflow_same
test_dataloader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers, drop_last=False)
model = ucf_main_without_systhesizing_model.Model(opt)
#optimizer = optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
optimizer = optim.SGD(model.parameters(), lr=opt.lr,momentum=opt.momentum)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.9)
criterion1 = nn.CrossEntropyLoss()
criterion2 = nn.NLLLoss()
if opt.cuda:
model.cuda()
#criterion.cuda(opt.device_id)
criterion1.cuda()
criterion2.cuda()
'''
if opt.epoch != 0:
if os.path.exists('./models/hmdb_split1/'+checkpoint_model_name):
model.load_state_dict(torch.load('./models/hmdb_split1/' + checkpoint_model_name))
else:
print('model not found')
exit()
'''
#Lin commented on Sept. 2nd
#model.double()
writer = SummaryWriter(log_dir=log_path+'runs/')
# For training
sum_test_acc = []
best_acc = 0.
epoch_errors = list()
avg_epoch_error = np.inf
best_epoch_error = np.inf
'''
#haha, output Acc for each class
model.load_state_dict(torch.load('/home/mcislab/linhanxi/ucf101_NewFeat_RGBtuned/ckpnothresh/ours/model_best.pth')['state_dict'])
test_acc, output = test(0,test_dataloader, model, criterion1, criterion2, opt, writer, log_file_name, is_test=True)
exit()
#load last experiment best model
print("load last experiment best model")
model.load_state_dict(torch.load('/home/mcislab/zhaojw/AAAI/prediction2020/models/ucf101_res50/split1/1050/model_best.pth')['state_dict'])
test_acc, output = test(0,test_dataloader, model, criterion1, criterion2, opt, writer, log_file_name, is_test=True)
'''
print ("Test once for a baseline.")
loaded_checkpoint =utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
#opt, model, optimizer = loaded_checkpoint
opt, model, __ = loaded_checkpoint
test_acc, output = test(1,test_dataloader, model, criterion1, criterion2, opt, writer, log_file_name, is_test=True)
tmp_test_acc = np.mean(test_acc)
if tmp_test_acc > best_acc:
best_acc = tmp_test_acc
print ("Start to train.....")
#model.load_state_dict(torch.load('/home/mcislab/linhanxi/ucf101_flowOnly/ckpnothresh/ours/checkpoint.pth')['state_dict'])
for epoch_i in range(opt.epoch, opt.niter):
scheduler.step()
train(epoch_i, train_dataloader, model, criterion1, criterion2, optimizer, opt, writer, log_file_name)
#val_acc, val_out, val_error =test(valid_loader, model, criterion1,criterion2, opt, log_file_name, is_test=False)
# Lin changed according to 'sth_pre_abl1' on Sept. 3rd
test_acc, output = test(epoch_i,test_dataloader, model, criterion1, criterion2, opt, writer, log_file_name, is_test=True)
#test_acc,_ = test(test_dataloader, model, criterion1, criterion2, opt, log_file_name, is_test=True)
tmp_test_acc = np.mean(test_acc)
sum_test_acc.append(test_acc)
if tmp_test_acc > best_acc:
is_best = True
best_acc = tmp_test_acc
else:
is_best = False
utils.save_checkpoint({'epoch': epoch_i , 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()},
is_best=is_best, directory=opt.resume)
print ("A training epoch finished!")
# For testing
print ("Training finished.Start to test.")
loaded_checkpoint = utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
opt, model, __ = loaded_checkpoint
# Lin changed according to 'sth_pre_abl1' on Sept. 3rd
test_acc,output = test(epoch_i,test_dataloader, model, criterion1, criterion2, opt, writer, log_file_name, is_test=True)
#test_acc,output = test(test_dataloader, model, criterion1,criterion2, opt, log_file_name, is_test=True)
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print ("ratio=0.1, test Accuracy: %.2f " % (100. * test_acc[0][0]))
print ("ratio=0.2, test Accuracy: %.2f " % (100. * test_acc[0][1]))
print ("ratio=0.3, test Accuracy: %.2f " % (100. * test_acc[0][2]))
print ("ratio=0.4, test Accuracy: %.2f " % (100. * test_acc[0][3]))
print ("ratio=0.5, test Accuracy: %.2f " % (100. * test_acc[0][4]))
print ("ratio=0.6, test Accuracy: %.2f " % (100. * test_acc[0][5]))
print ("ratio=0.7, test Accuracy: %.2f " % (100. * test_acc[0][6]))
print ("ratio=0.8, test Accuracy: %.2f " % (100. * test_acc[0][7]))
print ("ratio=0.9, test Accuracy: %.2f " % (100. * test_acc[0][8]))
print ("ratio=1.0, test Accuracy: %.2f " % (100. * test_acc[0][9]))
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
def main(opt):
if not os.path.exists(opt.resume):
os.makedirs(opt.resume)
if not os.path.exists(opt.logroot):
os.makedirs(opt.logroot)
log_dir_name = str(opt.manualSeed) + '/'
log_path = os.path.join(opt.logroot, log_dir_name)
opt.resume = os.path.join(opt.resume, log_dir_name)
if not os.path.exists(log_path):
os.makedirs(log_path)
#log_file_name = log_path + 'ucf_log_st.txt'
#log_file_name = opt.logroot + 'ucf_log_st_'+str(opt.manualSeed)+'.txt'
log_file_name = opt.logroot + 'something_log_v4.1_' + str(
opt.manualSeed) + '.txt'
with open(log_file_name, 'a+') as file:
file.write('manualSeed is %d \n' % opt.manualSeed)
paths = config.Paths()
train_datalist = "/home/mcislab/zhaojw/AAAI/sth_train_list.txt"
val_datalist = "/home/mcislab/zhaojw/AAAI/sth_val_list.txt"
test_datalist = "/home/mcislab/zhaojw/AAAI/sth_test_list.txt"
#test_datalist = '/home/mcislab/wangruiqi/IJCV2019/data/newsomething-check.txt'
#opt.resume = os.path.join(opt.resume,log_dir_name)
train_dataset = dataset(train_datalist, paths.sthv2_final, opt)
train_dataloader = DataLoader(train_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.workers,
drop_last=False)
val_dataset = dataset(val_datalist, paths.sthv2_final, opt)
val_dataloader = DataLoader(val_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.workers,
drop_last=False)
test_dataset = dataset(test_datalist, paths.sthv2_final, opt)
test_dataloader = DataLoader(test_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.workers,
drop_last=False)
model = sthv2_model.Model(opt)
'''
if opt.show:
show(model)
exit()
'''
optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.momentum)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=100,
gamma=0.9)
criterion1 = nn.CrossEntropyLoss()
criterion2 = nn.NLLLoss()
if opt.cuda:
model.cuda()
#criterion.cuda(opt.device_id)
criterion1.cuda()
criterion2.cuda()
'''
if opt.epoch != 0:
if os.path.exists('./models/hmdb_split1/'+checkpoint_model_name):
model.load_state_dict(torch.load('./models/hmdb_split1/' + checkpoint_model_name))
else:
print('model not found')
exit()
'''
#Lin commented on Sept. 2nd
#model.double()
writer = SummaryWriter(log_dir=os.path.join(log_path, 'runs/'))
# For training
sum_test_acc = []
best_acc = 0.
#epoch_errors = list()
avg_epoch_error = np.inf
best_epoch_error = np.inf
'''
#haha, output Acc for each class
test_load_dir = opt.resume
#test_load_dir = '/home/mcislab/linhanxi/IJCV19_Experiments/sth_scale/something_scale5_M/ckpnothresh/ours'
model.load_state_dict(torch.load(os.path.join(test_load_dir, 'model_best.pth'))['state_dict'])
if opt.featdir:
model.feat_mode()
test_acc, output = test(0,test_dataloader, model, criterion1, criterion2, opt, writer, test_load_dir, is_test=True)
exit()
'''
print("Test once to get a baseline.")
loaded_checkpoint = utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
opt, model, optimizer = loaded_checkpoint
test_acc, output = test(51,
test_dataloader,
model,
criterion1,
criterion2,
opt,
writer,
log_file_name,
is_test=True)
tmp_test_acc = np.mean(test_acc)
if tmp_test_acc > best_acc:
best_acc = tmp_test_acc
print("Start to train.....")
for epoch_i in range(opt.epoch, opt.niter):
scheduler.step()
train(epoch_i, train_dataloader, model, criterion1, criterion2,
optimizer, opt, writer, log_file_name)
#val_acc, val_out, val_error =test(valid_loader, model, criterion1,criterion2, opt, log_file_name, is_test=False)
# Lin changed according to 'sth_pre_abl1' on Sept. 3rd
test_acc, output = val(epoch_i,
val_dataloader,
model,
criterion1,
criterion2,
opt,
writer,
log_file_name,
is_test=True)
#test_acc,_ = test(test_dataloader, model, criterion1, criterion2, opt, log_file_name, is_test=True)
tmp_test_acc = np.mean(test_acc)
sum_test_acc.append(test_acc)
if tmp_test_acc > best_acc:
is_best = True
best_acc = tmp_test_acc
else:
is_best = False
utils.save_checkpoint(
{
'epoch': epoch_i,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
},
is_best=is_best,
directory=opt.resume)
print("A training epoch finished!")
#epoch_i =33
# For testing
print("Training finished.Start to test.")
loaded_checkpoint = utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
opt, model, optimizer = loaded_checkpoint
# Lin changed according to 'sth_pre_abl1' on Sept. 3rd
test_acc, output = test(epoch_i,
test_dataloader,
model,
criterion1,
criterion2,
opt,
writer,
log_file_name,
is_test=True)
#test_acc,output = test(test_dataloader, model, criterion1,criterion2, opt, log_file_name, is_test=True)
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print("ratio=0.1, test Accuracy: %.2f " % (100. * test_acc[0][0]))
print("ratio=0.2, test Accuracy: %.2f " % (100. * test_acc[0][1]))
print("ratio=0.3, test Accuracy: %.2f " % (100. * test_acc[0][2]))
print("ratio=0.4, test Accuracy: %.2f " % (100. * test_acc[0][3]))
print("ratio=0.5, test Accuracy: %.2f " % (100. * test_acc[0][4]))
print("ratio=0.6, test Accuracy: %.2f " % (100. * test_acc[0][5]))
print("ratio=0.7, test Accuracy: %.2f " % (100. * test_acc[0][6]))
print("ratio=0.8, test Accuracy: %.2f " % (100. * test_acc[0][7]))
print("ratio=0.9, test Accuracy: %.2f " % (100. * test_acc[0][8]))
print("ratio=1.0, test Accuracy: %.2f " % (100. * test_acc[0][9]))
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
def main(opt):
if not os.path.exists(opt.resume):
os.makedirs(opt.resume)
if not os.path.exists(opt.logroot):
os.makedirs(opt.logroot)
paths = cad120_config.Paths()
#print (paths.tmp_root)
subject_ids = pickle.load(open(os.path.join(paths.tmp_root, 'cad120_data_list.p'), 'rb'))
data_path = os.path.join(paths.tmp_root, 'cad120_data_pred.p')
#data_path = '/media/mcislab/wrq/CAD120/pred-feature/cad120_data_pred.p'
test_acc_final = np.zeros([4, opt.seq_size])
sub_index = 0
resume_root = os.path.join(opt.resume,str(opt.manualSeed))
for sub, seqs in subject_ids.items(): # cross-validation for each subject
#sub='Subject'+str(opt.subject)
log_file_name = opt.logroot+'cad120_log_sub'+sub+'.txt'
with open(log_file_name,'a+') as file:
file.write('manualSeed is %d \n' % opt.manualSeed)
opt.resume = resume_root+ sub + '/'
training_subject = pickle.load(open(os.path.join(paths.tmp_root, 'cad120_data_list.p'), 'rb')) # if not reload it will delete both in subject_ids and training_sub
testing_subject = dict()
testing_subject[sub] = seqs
del training_subject[sub]
#print training_subject
#print testing_subject
training_set = CAD120(data_path, training_subject)
testing_set = CAD120(data_path, testing_subject)
#testing_set = CAD120(data_path, sequence_ids[-test_num:])
train_loader = torch.utils.data.DataLoader(training_set, collate_fn=utils.collate_fn_cad,batch_size=opt.batch_size,
num_workers=opt.workers, shuffle=True, pin_memory=True)
#valid_loader = torch.utils.data.DataLoader(valid_set, collate_fn=utils.collate_fn_cad,
# batch_size=opt.batch_size,
# num_workers=opt.workers, shuffle=False, pin_memory=True)
test_loader = torch.utils.data.DataLoader(testing_set,collate_fn=utils.collate_fn_cad,batch_size=opt.batch_size,
num_workers=opt.workers, shuffle=False, pin_memory=True)
model = models.Model(opt)
optimizer = optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.9)
criterion1 = nn.CrossEntropyLoss()
criterion2 = nn.NLLLoss()
if opt.cuda:
model.cuda()
#criterion.cuda(opt.device_id)
criterion1.cuda()
criterion2.cuda()
loaded_checkpoint = utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
opt, model, optimizer = loaded_checkpoint
'''
if opt.epoch != 0:
if os.path.exists('./models/hmdb_split1/'+checkpoint_model_name):
model.load_state_dict(torch.load('./models/hmdb_split1/' + checkpoint_model_name))
else:
print('model not found')
exit()
'''
#model.double()
writer = SummaryWriter(log_dir=opt.logroot+'runs/'+sub+'/')
# For training
sum_test_acc = []
best_acc = 0.
epoch_errors = list()
avg_epoch_error = np.inf
best_epoch_error = np.inf
for epoch_i in range(opt.epoch, opt.niter):
scheduler.step()
train(epoch_i, train_loader, model, criterion1,criterion2, optimizer, opt, writer, log_file_name)
#val_acc, val_out, val_error =test(valid_loader, model, criterion1,criterion2, opt, log_file_name, is_test=False)
test_acc,output = test(epoch_i,test_loader, model, criterion1, criterion2, opt,writer, log_file_name)
tmp_test_acc = np.mean(test_acc)
sum_test_acc.append(test_acc)
if tmp_test_acc > best_acc:
is_best = True
best_acc = tmp_test_acc
else:
is_best = False
utils.save_checkpoint({'epoch': epoch_i + 1, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()},
is_best=is_best, directory=opt.resume)
# For testing
loaded_checkpoint = utils.load_best_checkpoint(opt, model, optimizer)
if loaded_checkpoint:
opt, model, optimizer = loaded_checkpoint
test_acc,output = test(epoch_i,test_loader, model, criterion1, criterion2, opt,writer, log_file_name)
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print ("ratio=0.1, test Accuracy: %.2f " % (100. * test_acc[0][0]))
print ("ratio=0.2, test Accuracy: %.2f " % (100. * test_acc[0][1]))
print ("ratio=0.3, test Accuracy: %.2f " % (100. * test_acc[0][2]))
print ("ratio=0.4, test Accuracy: %.2f " % (100. * test_acc[0][3]))
print ("ratio=0.5, test Accuracy: %.2f " % (100. * test_acc[0][4]))
print ("ratio=0.6, test Accuracy: %.2f " % (100. * test_acc[0][5]))
print ("ratio=0.7, test Accuracy: %.2f " % (100. * test_acc[0][6]))
print ("ratio=0.8, test Accuracy: %.2f " % (100. * test_acc[0][7]))
print ("ratio=0.9, test Accuracy: %.2f " % (100. * test_acc[0][8]))
print ("ratio=1.0, test Accuracy: %.2f " % (100. * test_acc[0][9]))
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
sum_test_acc = np.array(sum_test_acc)
sum_test_acc=sum_test_acc.reshape(opt.niter, opt.seq_size)
scio.savemat(opt.logroot+sub+'_result.mat',{'test_acc':sum_test_acc})
scio.savemat(opt.logroot+sub+'_output.mat',{'test_out':output})
test_acc_final[sub_index, :] = test_acc
with open(log_file_name, 'a+') as file:
file.write("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
file.write("ratio=0.1, test Accuracy: %.2f \n" % (100. * test_acc[0][0]))
file.write("ratio=0.2, test Accuracy: %.2f \n" % (100. * test_acc[0][1]))
file.write("ratio=0.3, test Accuracy: %.2f \n" % (100. * test_acc[0][2]))
file.write("ratio=0.4, test Accuracy: %.2f \n" % (100. * test_acc[0][3]))
file.write("ratio=0.5, test Accuracy: %.2f \n" % (100. * test_acc[0][4]))
file.write("ratio=0.6, test Accuracy: %.2f \n" % (100. * test_acc[0][5]))
file.write("ratio=0.7, test Accuracy: %.2f \n" % (100. * test_acc[0][6]))
file.write("ratio=0.8, test Accuracy: %.2f \n" % (100. * test_acc[0][7]))
file.write("ratio=0.9, test Accuracy: %.2f \n" % (100. * test_acc[0][8]))
file.write("ratio=1.0, test Accuracy: %.2f \n" % (100. * test_acc[0][9]))
sub_index = sub_index + 1
writer.close()
test_final = np.mean(test_acc_final,0)
#print type(test_final)
#print test_final
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print ("ratio=0.1, test Accuracy: %.2f " % (100. * test_final[0]))
print ("ratio=0.2, test Accuracy: %.2f " % (100. * test_final[1]))
print ("ratio=0.3, test Accuracy: %.2f " % (100. * test_final[2]))
print ("ratio=0.4, test Accuracy: %.2f " % (100. * test_final[3]))
print ("ratio=0.5, test Accuracy: %.2f " % (100. * test_final[4]))
print ("ratio=0.6, test Accuracy: %.2f " % (100. * test_final[5]))
print ("ratio=0.7, test Accuracy: %.2f " % (100. * test_final[6]))
print ("ratio=0.8, test Accuracy: %.2f " % (100. * test_final[7]))
print ("ratio=0.9, test Accuracy: %.2f " % (100. * test_final[8]))
print ("ratio=1.0, test Accuracy: %.2f " % (100. * test_final[9]))
print ("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
with open(log_file_name, 'a+') as file:
file.write("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
file.write("Cross-subject performance is:\n")
file.write("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
file.write("ratio=0.1, test Accuracy: %.2f \n" % (100. * test_final[0]))
file.write("ratio=0.2, test Accuracy: %.2f \n" % (100. * test_final[1]))
file.write("ratio=0.3, test Accuracy: %.2f \n" % (100. * test_final[2]))
file.write("ratio=0.4, test Accuracy: %.2f \n" % (100. * test_final[3]))
file.write("ratio=0.5, test Accuracy: %.2f \n" % (100. * test_final[4]))
file.write("ratio=0.6, test Accuracy: %.2f \n" % (100. * test_final[5]))
file.write("ratio=0.7, test Accuracy: %.2f \n" % (100. * test_final[6]))
file.write("ratio=0.8, test Accuracy: %.2f \n" % (100. * test_final[7]))
file.write("ratio=0.9, test Accuracy: %.2f \n" % (100. * test_final[8]))
file.write("ratio=1.0, test Accuracy: %.2f \n" % (100. * test_final[9]))
