def pre_MCNN():
MCNN_model_path = './saved_models/mcnn_shtechA_58.h5' #MCNN模型路径
net = CrowdCounter()
trained_model = os.path.join(MCNN_model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
return net
def evaluate_model(trained_model, data_loader):
net = CrowdCounter()
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net.forward(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
mae += abs(gt_count - et_count)
mse += ((gt_count - et_count) * (gt_count - et_count))
mae = mae / data_loader.get_num_samples()
mse = np.sqrt(mse / data_loader.get_num_samples())
return mae, mse
if not os.path.isdir(outpath):
os.mkdir(outpath)
model_path = r'result_1001/mcnn_shtechA_348.h5' # result_1001 best model
model_name = os.path.basename(model_path).split('.')[0]
img_name = r''
gt_name = r''
if os.path.isdir(img_name):
image =
analysis_report = os.path.join(outpath, 'Analysis_' + img_name + '.txt')
print('output analysis report path is {}'.format(analysis_report))
net = CrowdCounter()
trained_model = os.path.join(model_path)
# read model's parameter
h5f = h5py.File(trained_model, mode='r')
for k, v in net.state_dict().items():
param = torch.from_numpy(np.asarray(h5f[k]))
v.copy_(param)
net.cuda()
net.eval()
mae, mse = 0, 0
print(net)
#Tensorboard config
use_tensorboard = False
save_exp_name = method + '_' + dataset_name + '_' + 'v1'
remove_all_log = False # remove all historical experiments in TensorBoard
exp_name = None # the previous experiment name in TensorBoard
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
#loadt training and validation data
data_loader = ImageDataLoader(train_path,
train_gt_path,
shuffle=True,
gt_downsample=False,
pre_load=True)
class_wts = data_loader.get_classifier_weights()
data_loader_val = ImageDataLoader(val_path,
val_gt_path,
shuffle=False,
gt_downsample=False,
pre_load=True)
#load net and initialize it
net = CrowdCounter(ce_weights=class_wts)
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
# tensorboard config
use_tensorboard = False
save_exp_name = method + '_' + dataset_name + '_' + 'v1'
remove_all_log = False # remove all historical experiments in tensorboard
exp_name = None # the previous experiment name in tensorboard
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, std=0.01)
net.cuda()
net.train() # sets the module in training mode
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and crayonclient is not None
if use_tensorboard:
if dataset_name == 'shtechA':
lr = 1e-5
if dataset_name == 'shtechB':
lr = 5*1e-6
scaling=8 # output density map is 1/8 size of input image
print 'learning rate %f' % (lr)
### random seed
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
### initialize network
net = CrowdCounter(model=model,pool=pool)
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
### optimizer
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=lr)
### load data
pre_load=True
data_loader = ImageDataLoader(train_path, train_gt_path, shuffle=True, gt_downsample=True, pre_load=pre_load,
batch_size=batch_size,scaling=scaling)
data_loader_val = ImageDataLoader(val_path, val_gt_path, shuffle=False, gt_downsample=True, pre_load=pre_load,
batch_size=1,scaling=scaling)
save_output = True
data_path = './data/original/shanghaitech/part_A_final/test_data/images/'
gt_path = './data/original/shanghaitech/part_A_final/test_data/ground_truth_csv/'
model_path = './saved_models/mcnn_shtechA_430.h5'
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
#load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
if dataset_name == 'shtechA':
lr = 1e-5
if dataset_name == 'shtechB':
lr = 5 * 1e-6
scaling = 8 # output density map is 1/8 size of input image
print 'learning rate %f' % (lr)
### random seed
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
### initialize network
net = CrowdCounter(model=model, pool=pool)
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
### optimizer
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
### load data
pre_load = True
data_loader = ImageDataLoader(train_path,
train_gt_path,
shuffle=True,
#loadt training and validation data
data_loader = ImageDataLoader(train_path,
train_gt_path,
shuffle=True,
gt_downsample=False,
pre_load=True)
class_wts = data_loader.get_classifier_weights()
data_loader_val = ImageDataLoader(val_path,
val_gt_path,
shuffle=False,
gt_downsample=False,
pre_load=True)
#load net and initialize it
net = CrowdCounter(ce_weights=class_wts)
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
save_output = True
data_path = './data-oilpalm/oilpalm-test/IMG_Palm/'
gt_path = './data-oilpalm/oilpalm-test/ground_truth_csv/'
model_path = './oilpalm_saved_models/mcnn_oilpalm_70.h5'
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
rmse = 0.0
mrmse = 0.0
# load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
save_output = True
# data_path = './data/original/shanghaitech/part_B_final/test_data/images/'
# gt_path = './data/original/shanghaitech/part_B_final/test_data/ground_truth_csv/'
model_path = './final_models/mcnn_shtechA_660.h5'
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
net.training = False
im_data = cv2.imread(
'/home/haoxue/Pictures/Screenshot from 2018-11-05 15-26-25.png', 0)
im_data = im_data.reshape((1, 1, im_data.shape[0], im_data.shape[1]))
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
et_count = np.sum(density_map)
if vis:
utils.display_density(im_data, density_map)
save_output = False
image_path = 'othertest/test.1.jpg'
model_path = 'saved_models/mcnn_SH_B_95.h5'
# output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
# file_results = os.path.join(output_dir,'results_' + model_name + '_.txt')
# if not os.path.exists(output_dir):
# os.mkdir(output_dir)
# output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
# if not os.path.exists(output_dir):
# os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
# net.cuda()
# net.eval()
mae = 0.0
mse = 0.0
#load test data
# data_loader = ImageDataLoader(data_path, gt_path, shuffle=False, gt_downsample=True, pre_load=True)
img = cv2.imread(image_path, 0)
img = img.astype(np.float32, copy=False)
ht = img.shape[0]
wd = img.shape[1]
# tensorboard config
use_tensorboard = False
save_exp_name = method + '_' + dataset_name + '_' + 'v1'
remove_all_log = False # remove all historical experiments in tensorboard
exp_name = None # the previous experiment name in tensorboard
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, std=0.01)
net.cuda()
net.train() # sets the module in training mode
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and crayonclient is not None
if use_tensorboard:
cc = crayonclient(hostname='127.0.0.1')
if remove_all_log:
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
vis = False
save_output = True
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter(model, pool)
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
if model in ['base', 'wide']:
scaling = 4
if model == 'deep':
scaling = 8
#load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
f.write('MAE: %0.2f, MSE: %0.2f' % (mae,mse))
f.close()
if __name__ == '__main__':
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
# data_path = './data/original/shanghaitech/part_B_final/test_data/images/'
# gt_path = './data/original/shanghaitech/part_B_final/test_data/ground_truth_csv/'
data_path = './data/original/shanghaitech/part_A_final/test_data/images/'
gt_path = './data/original/shanghaitech/part_A_final/test_data/ground_truth_csv/'
model_path = './Shanghai_A_Retrain_eps_1_5/saved_models//MCNN_Shanghai_A.h5'
model = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, model)
model.to(device)
model.eval()
data_loader = ImageDataLoader(data_path, gt_path, shuffle=False, gt_downsample=True, pre_load=True)
eps_list = [0, 0.25, 0.5, 0.75, 1]
print(model_path)
epoch_robust_bound(data_loader, model, device, epsilon_try=0.25)
def main():
# define output folder
output_dir = './saved_models/'
log_dir = './mae_mse/'
checkpoint_dir = './checkpoint/'
train_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/train/images'
train_gt_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/train/ground_truth'
val_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/val/images'
val_gt_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/val/ground_truth'
# last checkpoint
checkpointfile = os.path.join(checkpoint_dir, 'checkpoint.94.pth.tar')
# some description
method = 'mcnn'
dataset_name = 'SH_B'
# log file
f_train_loss = open(os.path.join(log_dir, "train_loss.csv"), "a+")
f_val_loss = open(os.path.join(log_dir, "val_loss.csv"), "a+")
# Training configuration
start_epoch = 0
end_epoch = 97
lr = 0.00001
# momentum = 0.9
disp_interval = 1000
# log_interval = 250
# Flag
CONTINUE_TRAIN = True
# Tensorboard config
# use_tensorboard = False
# save_exp_name = method + '_' + dataset_name + '_' + 'v1'
# remove_all_log = False # remove all historical experiments in TensorBoard
# exp_name = None # the previous experiment name in TensorBoard
# -----------------------------------------------------------------------------------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# Define network
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
# net.cuda()
net.train()
# params = list(net.parameters())
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, net.parameters()), lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# # tensorboad
# use_tensorboard = use_tensorboard and CrayonClient is not None
# if use_tensorboard:
# cc = CrayonClient(hostname='127.0.0.1')
# if remove_all_log:
# cc.remove_all_experiments()
# if exp_name is None:
# exp_name = save_exp_name
# exp = cc.create_experiment(exp_name)
# else:
# exp = cc.open_experiment(exp_name)
# training param
if CONTINUE_TRAIN:
net, optimizer, start_epoch = utils.load_checkpoint(
net, optimizer, filename=checkpointfile)
train_loss = 0
step_cnt = 0
re_cnt = False
t = Timer()
t.tic()
# Load data
data_loader = ImageDataLoader(
train_path, train_gt_path, shuffle=True, gt_downsample=True, pre_load=True)
data_loader_val = ImageDataLoader(
val_path, val_gt_path, shuffle=False, gt_downsample=True, pre_load=True)
best_mae = sys.maxsize
# Start training
for this_epoch in range(start_epoch, end_epoch-1):
step = -1
train_loss = 0
for blob in data_loader:
step += 1
img_data = blob['data']
gt_data = blob['gt_density']
et_data = net(img_data, gt_data)
loss = net.loss
train_loss += loss.data
step_cnt += 1
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % disp_interval == 0:
duration = t.toc(average=False)
fps = step_cnt / duration
gt_count = np.sum(gt_data)
et_data = et_data.data.cpu().numpy()
et_count = np.sum(et_data)
utils.save_results(img_data, gt_data, et_data, output_dir,
fname="{}.{}.png".format(this_epoch, step))
log_text = 'epoch: %4d, step %4d, Time: %.4fs, gt_cnt: %4.1f, et_cnt: %4.1f' % (this_epoch,
step, 1./fps, gt_count, et_count)
log_print(log_text, color='green', attrs=['bold'])
re_cnt = True
if re_cnt:
t.tic()
re_cnt = False
# Save checkpoint
state = {'epoch': this_epoch, 'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict()}
cp_filename = "checkpoint.{}.pth.tar".format(this_epoch)
torch.save(state, os.path.join(checkpoint_dir, cp_filename))
# ========================== END 1 EPOCH==================================================================================
train_mae, train_mse = evaluate_network(net, data_loader)
f_train_loss.write("{},{}\n".format(train_mae, train_mse))
log_text = 'TRAINING - EPOCH: %d, MAE: %.1f, MSE: %0.1f' % (
this_epoch, train_mae, train_mse)
log_print(log_text, color='green', attrs=['bold'])
# =====================================================VALIDATION=========================================================
# calculate error on the validation dataset
val_mae, val_mse = evaluate_network(net, data_loader_val)
f_val_loss.write("{},{}\n".format(val_mae, val_mse))
log_text = 'VALIDATION - EPOCH: %d, MAE: %.1f, MSE: %0.1f' % (
this_epoch, val_mae, val_mse)
log_print(log_text, color='green', attrs=['bold'])
# SAVE model
is_save = False
if val_mae <= best_mae:
if val_mae < best_mae:
is_save = True
best_mae = val_mae
best_mse = val_mse
else:
if val_mse < best_mse:
is_save = True
best_mse = val_mse
if is_save:
save_name = os.path.join(output_dir, '{}_{}_{}.h5'.format(
method, dataset_name, this_epoch))
network.save_net(save_name, net)
best_model = '{}_{}_{}.h5'.format(method, dataset_name, this_epoch)
log_text = 'BEST MAE: %0.1f, BEST MSE: %0.1f, BEST MODEL: %s' % (
best_mae, best_mse, best_model)
log_print(log_text, color='green', attrs=['bold'])
# if use_tensorboard:
# exp.add_scalar_value('MAE', mae, step=epoch)
# exp.add_scalar_value('MSE', mse, step=epoch)
# exp.add_scalar_value('train_loss', train_loss /
# data_loader.get_num_samples(), step=epoch)
f_train_loss.close()
f_val_loss.close()
def testimage(modelname, camname):
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
vis = False
save_output = False
#test data and model file path
if camname == 0:
data_path = '../data/test/images/'
else:
data_path = '../data/test/images2/'
if modelname == 'A':
model_path = './final_models/cmtl_shtechA_204.h5'
else:
model_path = './final_models/cmtl_shtechB_768.h5'
print("Model name:", modelname, " Camname: ", camname)
gt_flag = False
if gt_flag:
gt_path = '../dataset/ShanghaiTech/part_A/test_data/ground_truth/'
# =============================================================================
# for i in range(1, 4):
# gt_name = os.path.join(gt_path,'img_' + format(i, '04') + '_ann.mat')
# print(gt_name)
# x = loadmat(gt_name)
# print (len(x['annPoints']))
#
# =============================================================================
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir, 'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
#load test data
data_loader = ImageDataLoader(data_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
i = 1
#df = pd.read_csv("../etcount.csv")
#df = df.set_index('IMG_NAME')
#df['GROUND_TRUTH'] = 0.0
#df['MTL-v4-A10'] = 0.0
for blob in data_loader:
if gt_flag:
gt_name = os.path.join(
gt_path, 'GT_' + format(blob['fname'].split('.')[0]) + '.mat')
x = loadmat(gt_name)
#gt_count = len(x['image_info'][0][0][0][0][0])
#df.at[blob['fname'].split('.')[0], 'GROUND_TRUTH'] = gt_count
i += 1
im_data = blob['data']
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
x = len(density_map[0][0])
y = len(density_map[0][0][0])
half = (int)(x / 2)
density_map1 = density_map[0][0][0:half][:]
density_map2 = density_map[0][0][half:][:]
print(x, y)
et_c1 = np.sum(density_map1)
et_c2 = np.sum(density_map2)
side = 'none'
if et_c1 > et_c2:
side = 'right'
else:
side = 'left'
print(et_c1, et_c2)
et_count = np.sum(density_map)
print(blob['fname'].split('.')[0], ' Model Estimated count : ',
et_count)
#df.at[blob['fname'].split('.')[0], 'MTL-v4-A'] = et_count
if vis:
utils.display_results(im_data, density_map)
if save_output:
utils.save_density_map(
density_map, output_dir,
'output_' + blob['fname'].split('.')[0] + '.png')
return (et_count, side)
#df.to_csv('../etcount.csv')
#testimage('A', 1)
#training configuration
disp_interval = 500
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
trained_model= 'pretrained.h5'
if args.MODE == 'val':
trained_model= args.SAVE_ROOT+'/'+args.Dataset+'_trained_model.h5'
print('loading supervised trained model weights')
if args.MODE == 'test':
trained_model= args.SAVE_ROOT+'/'+args.Dataset+'_self_trained_model_val.h5'
print('loading self learned val model weights')
#Tensorboard config
use_tensorboard = False
save_exp_name = method + '_' + dataset_name + '_' + 'v1'
remove_all_log = False # remove all historical experiments in TensorBoard
exp_name = None # the previous experiment name in TensorBoard
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
cc = CrayonClient(hostname='127.0.0.1')
if remove_all_log:
train_gt_path = './data/shanghaiA_100patches/train_den/'
# end_step = 1000
start_epoch = 0
lr = 0.00001
momentum = 0.9
# disp_interval = 5000
# log_interval = 250
pretrained_vgg16 = False
fine_tune = False
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
if pretrained_vgg16:
vgg16_model = models.vgg16(pretrained=True)
# vgg16_model.cuda()
net.DA_Net.copy_params_from_vgg16(vgg16_model)
net.to(device)
Loss_list = []
for epoch in range(start_epoch, start_epoch + 2):
train_loss = train_DA(epoch)
Loss_list.append(train_loss)
if epoch == 1:
test_DA(epoch)
# 记录train_loss
def open_model(model_path):
model = CrowdCounter()
network.load_net(model_path, model)
model.eval()
return model
