def test(frame, opt, rgb, transform_test, num_workers, label_indice,
model_path):
img = Image.fromarray(frame)
testset = Countmap_Dataset(img,rgb,transform=transform_test,\
if_test=True, IF_loadmem=opt['IF_savemem_test'])
testloader = DataLoader(testset,
batch_size=opt['test_batch_size'],
shuffle=False,
num_workers=num_workers)
# init networks
label_indice = torch.Tensor(label_indice)
class_num = len(label_indice) + 1
div_times = opt['div_times']
net = SSDCNet_classify(class_num,label_indice,div_times=div_times,\
frontend_name='VGG16',block_num=5,\
IF_pre_bn=False,IF_freeze_bn=False,load_weights=True,\
psize=opt['psize'],pstride = opt['pstride'],parse_method ='maxp').cuda()
# test the min epoch
mod_path = 'best_epoch.pth'
mod_path = os.path.join(opt['model_path'], mod_path)
if os.path.exists(mod_path):
all_state_dict = torch.load(mod_path)
net.load_state_dict(all_state_dict['net_state_dict'])
tmp_epoch_num = all_state_dict['tmp_epoch_num']
log_save_path = os.path.join(
model_path, 'log-epoch-min[%d]-%s.txt' %
(tmp_epoch_num + 1, opt['parse_method']))
# test
test_log, count = test_phase(opt,
net,
testloader,
log_save_path=log_save_path)
return count
def main(opt):
# =============================================================================
# inital setting
# =============================================================================
# 1.Initial setting
# --1.1 dataset setting
dataset = opt['dataset']
root_dir = opt['root_dir']
num_workers = opt['num_workers']
img_subsubdir = 'images'; tar_subsubdir = 'gtdens'
dataset_transform = ToTensor()
# --1.2 use initial setting to generate
# set label_indice
if opt['partition'] =='one_linear':
label_indice = np.arange(opt['step'],opt['max_num']+opt['step']/2,opt['step'])
add = np.array([1e-6])
label_indice = np.concatenate( (add,label_indice) )
elif opt['partition'] =='two_linear':
label_indice = np.arange(opt['step'],opt['max_num']+opt['step']/2,opt['step'])
add = np.array([1e-6,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45])
label_indice = np.concatenate( (add,label_indice) )
# print(label_indice)
opt['label_indice'] = label_indice
opt['class_num'] = label_indice.size+1
#test settings
img_dir = os.path.join(root_dir,'test',img_subsubdir)
tar_dir = os.path.join(root_dir,'test',tar_subsubdir)
rgb_dir = os.path.join(root_dir,'rgbstate.mat')
testset = myDataset(img_dir,tar_dir,rgb_dir,transform=dataset_transform,\
if_test=True, IF_loadmem=opt['IF_savemem_test'])
testloader = DataLoader(testset, batch_size=opt['test_batch_size'],
shuffle=False, num_workers=num_workers)
# init networks
label_indice = torch.Tensor(label_indice)
class_num = len(label_indice)+1
div_times = 2
net = SDCNet_VGG16_classify(class_num,label_indice,psize=opt['psize'],\
pstride = opt['pstride'],div_times=div_times,load_weights=True).cuda()
# test the exist trained model
mod_path='best_epoch.pth'
mod_path=os.path.join(opt['trained_model_path'],mod_path)
if os.path.exists(mod_path):
all_state_dict = torch.load(mod_path)
net.load_state_dict(all_state_dict['net_state_dict'])
log_save_path = os.path.join(opt['trained_model_path'],'log-trained-model.txt')
# test
mae,rmse,me = test_phase(opt,net,testloader,log_save_path=log_save_path)
log_str = '%10s\t %8s\t &%8s\t &%8s\t\\\\' % (' ','mae','rmse','me')+'\n'
log_str+= '%-10s\t %8.3f\t %8.3f\t %8.3f\t' % ( 'test',mae,rmse,me ) + '\n'
print(log_str)
def main(opt):
# save folder
save_folder = opt['model_path']
# =============================================================================
# inital setting
# =============================================================================
# 1.Initial setting
# --1.1 dataset setting
dataset = opt['dataset']
root_dir = opt['root_dir']
num_workers = opt['num_workers']
img_subsubdir = 'images'
tar_subsubdir = 'gtdens'
transform_test = []
# --1.3 use initial setting to generate
# set label_indice
label_indice = np.arange(opt['step'], opt['max_num'] + opt['step'],
opt['step'])
add = np.array(
[1e-6, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45])
label_indice = np.concatenate((add, label_indice))
print(label_indice)
opt['label_indice'] = label_indice
opt['class_num'] = label_indice.size + 1
#test settings
img_dir = os.path.join(root_dir, 'test', img_subsubdir)
tar_dir = os.path.join(root_dir, 'test', tar_subsubdir)
rgb_dir = os.path.join(root_dir, 'rgbstate.mat')
testset = Countmap_Dataset(img_dir,tar_dir,rgb_dir,transform=transform_test,\
if_test=True, IF_loadmem=opt['IF_savemem_test'])
testloader = DataLoader(testset,
batch_size=opt['test_batch_size'],
shuffle=False,
num_workers=num_workers)
# init networks
label_indice = torch.Tensor(label_indice)
class_num = len(label_indice) + 1
div_times = opt['div_times']
net = SSDCNet_classify(class_num,label_indice,div_times=div_times,\
frontend_name='VGG16',block_num=5,\
IF_pre_bn=False,IF_freeze_bn=False,load_weights=True,\
psize=opt['psize'],pstride = opt['pstride'],parse_method ='maxp').cuda()
# test the min epoch
mod_path = 'best_epoch.pth'
mod_path = os.path.join(opt['model_path'], mod_path)
if os.path.exists(mod_path):
all_state_dict = torch.load(mod_path)
net.load_state_dict(all_state_dict['net_state_dict'])
tmp_epoch_num = all_state_dict['tmp_epoch_num']
log_save_path = os.path.join(save_folder,'log-epoch-min[%d]-%s.txt' \
%(tmp_epoch_num+1,opt['parse_method']) )
# test
test_log = test_phase(opt,
net,
testloader,
log_save_path=log_save_path)