def train_DA(epoch):
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)
train_loss = 0
data_loader = ImageDataLoader(train_path,
train_gt_path,
shuffle=True,
gt_downsample=True,
pre_load=False)
best_mae = sys.maxsize
step = -1
train_loss = 0
gt_count = 0
et_count = 0
for blob in data_loader:
step = step + 1
im_data = blob['data']
gt_data = blob['gt_density']
dtype = torch.FloatTensor
# certified input
im_data = torch.from_numpy(im_data).type(dtype)
im_data = im_data.to(device)
im_data = random_mask_batch_one_sample(im_data, keep, reuse_noise=True)
im_data = Variable(im_data)
gt_data = torch.from_numpy(gt_data).type(dtype)
gt_data = gt_data.to(device)
gt_data = Variable(gt_data)
density_map = net(im_data, gt_data)
zzk_loss = net.loss
train_loss += zzk_loss.item()
gt_data = gt_data.data.detach().cpu().numpy()
gt_count = np.sum(gt_data)
density_map = density_map.data.detach().cpu().numpy()
et_count = np.sum(density_map)
print("gt_count: ", gt_count)
print("et_count: ", et_count)
optimizer.zero_grad()
zzk_loss.backward()
optimizer.step()
train_loss = train_loss / data_loader.get_num_samples()
if epoch % 100 == 0:
save_name = os.path.join(
output_dir, '{}_{}_{}.h5'.format(method, dataset_name, epoch))
network.save_net(save_name, net)
return train_loss
def eval_test(net, test_path):
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
output_dir = './output'
model_path = args.Dataset+ '_trained_model.h5'
model_name = os.path.basename(model_path).split('.')[0]
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'dm_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
test_loader = ImageDataLoader(test_path, None, 'test_split', shuffle=False, gt_downsample=True, pre_load=True , Dataset=args.Dataset)
for blob in test_loader:
im_data = blob['data']
net.training = False
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
new_dm= density_map.reshape([ density_map.shape[2], density_map.shape[3] ])
np.savetxt( output_dir + 'output_' + blob['fname'].split('.')[0] +'.csv', new_dm, delimiter=',', fmt='%.6f')
return net
def reloadData():
data_loader_res = ImageDataLoader(data_path_res,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True,
no_gt=True)
data_loader_out = ImageDataLoader(data_path_out,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True,
no_gt=True)
data_loader_class = ImageDataLoader(data_path_class,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True,
no_gt=True)
#Tensorboard config
use_tensorboard = False
save_exp_name = method + '_' + dataset_name + '_' + 'v1'
remove_all_log = False # remove all historical experiments in TensorBoardO
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)
#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()
if exp_name is None:
exp_name = save_exp_name
exp = cc.create_experiment(exp_name)
else:
exp = cc.open_experiment(exp_name)
# training
train_loss = 0
step_cnt = 0
re_cnt = False
t = Timer()
t.tic()
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
for epoch in range(start_step, end_step + 1):
step = -1
train_loss = 0
for blob in list(data_loader):
step = step + 1
im_data = blob['data']
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)
### training
train_loss = 0
t = Timer()
t.tic()
best_mae = sys.maxint
for epoch in range(start_step, end_step+1):
step = 0
train_loss = 0
for blob in data_loader:
step = step + 1
im_data = blob['data']
def test_DA(epoch):
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
data_path_DA = '../ShanghaiTech/part_A_final/test_data/images/'
gt_path_DA = '../ShanghaiTech/part_A_final/test_data/after_ground_truth/'
net.to(device)
net.eval()
mae = 0.0
mse = 0.0
data_loader_DA = ImageDataLoader(data_path_DA,
gt_path_DA,
shuffle=False,
gt_downsample=True,
pre_load=False)
# 保存图片
if not os.path.exists('./results_DA_ablated'):
os.mkdir('./results_DA_ablated')
if not os.path.exists('./results_DA_ablated/density_map_adv'):
os.mkdir('./results_DA_ablated/density_map_adv')
if not os.path.exists('./results_DA_ablated/images_adv'):
os.mkdir('./results_MCNN_DA/images_adv')
if not os.path.exists('./results_DA_ablated/images_gt'):
os.mkdir('./results_DA_ablated/images_gt')
correct = 0
total = 0
dtype = torch.FloatTensor
# ablated test
for blob in data_loader_DA:
im_data = blob['data']
gt_data = blob['gt_density']
full_imgname = blob['fname']
# certified input
im_data = torch.from_numpy(im_data).type(dtype)
im_data = im_data.to(device)
im_data = random_mask_batch_one_sample(im_data, keep, reuse_noise=True)
im_data = Variable(im_data)
gt_data = torch.from_numpy(gt_data).type(dtype)
gt_data = gt_data.to(device)
gt_data = Variable(gt_data)
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
im_data = im_data.data.cpu().numpy()
gt_data = gt_data.data.cpu().numpy()
tgt_img = gt_data[0][0]
plt.imsave(
'./results_DA_ablated/images_gt/IMG_{}.png'.format(full_imgname),
tgt_img,
format='png',
cmap='gray')
adv_tgt_img = im_data[0][0]
plt.imsave(
'./results_DA_ablated/images_adv/IMG_{}.png'.format(full_imgname),
adv_tgt_img,
format='png',
cmap=plt.cm.jet)
adv_out = density_map[0][0]
plt.imsave('./results_DA_ablated/density_map_adv/IMG_{}.png'.format(
full_imgname),
adv_out,
format='png',
cmap='gray')
et_count = np.sum(density_map)
gt_count = np.sum(gt_data)
bias = abs(et_count - gt_count)
mae += abs(gt_count - et_count)
mse += ((gt_count - et_count) * (gt_count - et_count))
if bias < 10:
correct += 1
total += 1
accuracy = (correct / total) * 100.0
print("correct: ", correct)
print("total: ", total)
mae = mae / data_loader_DA.get_num_samples()
mse = np.sqrt(mse / data_loader_DA.get_num_samples())
print("test_ablated_results: ")
print('\nMAE: %0.2f, MSE: %0.2f' % (mae, mse))
print("test_ablated_accuracy: ", accuracy)
# 保存图片
if not os.path.exists('./results_DA_normal'):
os.mkdir('./results_DA_normal')
if not os.path.exists('./results_DA_normal/density_map_adv'):
os.mkdir('./results_DA_normal/density_map_adv')
if not os.path.exists('./results_DA_normal/images_gt'):
os.mkdir('./results_DA_normal/images_gt')
total = 0
correct = 0
mae = 0.0
mse = 0.0
for blob in data_loader_DA:
im_data = blob['data']
gt_data = blob['gt_density']
full_imgname = blob['fname']
tgt_img = gt_data[0][0]
plt.imsave('./results_DA_normal/images_gt/{}'.format(full_imgname),
tgt_img,
format='png',
cmap='gray')
im_data = torch.from_numpy(im_data).type(dtype)
im_data = im_data.to(device)
gt_data = torch.from_numpy(gt_data).type(dtype)
gt_data = gt_data.to(device)
gt_data = Variable(gt_data)
density_map = net(im_data, gt_data)
density_map = density_map.data.detach().cpu().numpy()
gt_data = gt_data.data.detach().cpu().numpy()
adv_out = density_map[0][0]
plt.imsave(
'./results_DA_normal/density_map_adv/{}'.format(full_imgname),
adv_out,
format='png',
cmap='gray')
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
bias = abs(gt_count - et_count)
mae += abs(gt_count - et_count)
mse += (gt_count - et_count) * (gt_count - et_count)
# ！！！ 具体评判预测成功的指标待定！！！
if bias < 10:
correct += 1
total += 1
accuracy = (correct / total) * 100.0
print("correct: ", correct)
print("total: ", total)
mae = mae / data_loader_1.get_num_samples()
mse = np.sqrt(mse / data_loader_1.get_num_samples())
print("test_normal_result: ")
print('\nMAE: %0.2f, MSE: %0.2f' % (mae, mse))
print("normal_test_accuracy: ", accuracy)
def test_load_data(self):
dicoms, targets = ImageDataLoader('test_data/link.csv', 'test_data/dicoms/', 'test_data/contourfiles/').load_data()
self.assertEqual(len(dicoms), 4, "Should be 4")
self.assertEqual(len(targets), 4, "Should be 4")
self.assertEqual(dicoms[2].shape, (256, 256), "Should be (256, 256")
self.assertEqual(targets[2].shape, (256, 256), "Should be (256, 256")
def main(args):
dicoms, icontours, ocontours = ImageDataLoader(
args.links_filename, args.dicom_path, args.contour_path).load_data()
#dataset = generate_batches_for_training(args.epochs, args.batch_size, args.buffer_size, dicoms, targets)
visualize_images(dicoms, icontours, ocontours)
import tensorflow as tf
import numpy as np
from src.network import LoadNet
from src.data_loader import ImageDataLoader
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
meta_path = './models/crowncnn-14.meta'
model_path = './models/'
val_path = '/local/share/DeepLearning/crowdcount-mcnn/data/formatted_trainval/shanghaitech_part_A_patches_9/val'
val_gt_path = '/local/share/DeepLearning/crowdcount-mcnn/data/formatted_trainval/shanghaitech_part_A_patches_9/val_den'
val_loader = ImageDataLoader(val_path, val_gt_path, shuffle=True, gt_downsample=True, pre_load=True)
ln = LoadNet(meta_path)
graph, sess = ln.load_net(sess, model_path)
im_data = graph.get_tensor_by_name('im_data:0')
density_op = graph.get_collection('density_op')[0]
for blob in val_loader:
data = blob['data']
den = blob['den']
pred_data = sess.run(density_op, feed_dict={im_data: data})
gt_count = np.sum(den)
pred_count = np.sum(pred_data)
print('gt_count is {}, pred_count is {}'.format(gt_count, pred_count))
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
mape = 0.0
u = 0.0
v = 0.0
y_true = []
y_pred = []
y_diff = []
# load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
precise = 0.0
recall = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
y_true.append(gt_count)
y_pred.append(et_count)
y_diff.append(gt_count - et_count)
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,
pre_load=False,
batch_size=1,
scaling=scaling)
mae = 0.0
mse = 0.0
num = 0
for blob in data_loader:
num += 1
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
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()
exp_name = save_exp_name
exp = cc.create_experiment(exp_name)
else:
exp = cc.open_experiment(exp_name)
# training
train_loss = 0
step_cnt = 0
re_cnt = False
t = Timer()
t.tic()
# downsample = True
data_loader = ImageDataLoader(train_path,
train_gt_path,
batch_size=BATCH_SIZE,
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)
data_loader_test = TestImageDataLoader(test_path,
test_gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
# downsample = False
# data_loader = ImageDataLoader(train_path, train_gt_path, shuffle=True, gt_downsample=False, pre_load=True)
# data_loader_val = ImageDataLoader(val_path, val_gt_path, shuffle=False, gt_downsample=True, pre_load=True)
# data_loader_test = ImageDataLoader(test_path, test_gt_path, shuffle=False, gt_downsample=False, pre_load=True)
best_mae = sys.maxsize
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.learning_rate)
if not os.path.exists(args.SAVE_ROOT):
os.mkdir(args.SAVE_ROOT)
# start training
# train, validation/self training and testing model
if args.MODE == 'all' or args.MODE == 'train':
data_loader_train = ImageDataLoader(train_path, train_gt_path,'train_split', shuffle=False, gt_downsample=True, pre_load=True, Dataset=args.Dataset)
net = train(net, data_loader_train,optimizer,args.MAX_EPOCHS)
network.save_net(args.SAVE_ROOT+'/'+args.Dataset+'_trained_model.h5', net)
if args.MODE == 'all' or args.MODE == 'val':
net = val(net,val_path, optimizer,args.VAL_EPOCHS, Dataset=args.Dataset)
network.save_net(args.SAVE_ROOT+'/'+args.Dataset+'_self_trained_model_val.h5', net)
if args.MODE == 'all' or args.MODE == 'test':
net = test(net,test_path, optimizer,args.VAL_EPOCHS, Dataset=args.Dataset)
network.save_net(args.SAVE_ROOT+'/'+args.Dataset+'_self_trained_model_test.h5', net)
#if args.MODE == 'eval_all' or args.MODE == 'eval_val':
# eval_val(net, val_path)
if not os.path.exists(save_dir):
os.mkdir(save_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=False, pre_load=True)
for blob in data_loader:
im_data = blob['data']
#gt_data = blob['gt_density']
gt_data = None
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
#gt_count = np.sum(gt_data)
gt_count = 0
et_count = np.sum(density_map)
print(et_count)
mae += abs(gt_count-et_count)
mse += ((gt_count-et_count)*(gt_count-et_count))
if vis:
def test(net,test_path,optimizer, num_epochs, Dataset=args.Dataset):
if Dataset=="fdst":
num_sessions=3
test_len=750
low_limit=451
high_limit=750
else:
num_sessions=8
test_len=2000
low_limit=1201
high_limit=2000
#print(num_sessions)
sessions_list = []
ses_size = 100
for i in range(low_limit, high_limit,ses_size):
sessions_list.append(i)
sessions_list.append(test_len)
#print("test list: ", sessions_list)
for test_inc in range(len(sessions_list)-1):
start_frame = sessions_list[test_inc]
end_frame = sessions_list[test_inc+1]
#print('start:,end:', (start_frame,end_frame))
test_loader = ImageDataLoader_Val_Test(test_path, None,'test_split',start_frame, end_frame, shuffle=False, gt_downsample=True, pre_load=True, Dataset=args.Dataset)
log_file = open(args.SAVE_ROOT+"/"+args.Dataset+"_test.log","w",1)
log_print("test/Self Training ....", color='green', attrs=['bold'])
# training
train_loss = 0
step_cnt = 0
re_cnt = False
t = Timer()
t.tic()
for epoch in range(1,num_epochs+1):
step = -1
train_loss = 0
for blob in test_loader:
step = step + 1
im_data = blob['data']
net.training = False
gt_data = net(im_data)
gt_data = gt_data.cpu().detach().numpy()
net.training = True
density_map = net(im_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)
density_map = density_map.data.cpu().numpy()
et_count = np.sum(density_map)
utils.save_results(im_data,gt_data,density_map, args.SAVE_ROOT)
log_text = 'epoch: %4d, step %4d, Time: %.4fs, gt_cnt: %4.1f, et_cnt: %4.1f' % (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
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
session= str(sessions_list[test_inc])
network.save_net(args.SAVE_ROOT+'/'+args.Dataset+ session +'_self_trained_model_test.h5', net)
output_dir = './densitymaps/' + args.Dataset + session
net.cuda()
net.eval()
all_test_loader = ImageDataLoader(test_path, None, 'test_split', shuffle=False, gt_downsample=True, pre_load=True , Dataset=args.Dataset)
for blob in all_test_loader:
im_data = blob['data']
net.training = False
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
new_dm= density_map.reshape([ density_map.shape[2], density_map.shape[3] ])
np.savetxt(output_dir + '_output_' + blob['fname'].split('.')[0] +'.csv', new_dm, delimiter=',', fmt='%.6f')
return net
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
train_loss = 0
step_cnt = 0
re_cnt = False
t = Timer()
t.tic()
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
for epoch in range(start_step, end_step+1):
step = -1
train_loss = 0
for blob in data_loader:
step = step + 1
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net.forward(im_data, gt_data)
loss = net.loss
train_loss += loss.item()
step_cnt += 1
optimizer.zero_grad()
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)
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
# print('gt_data: ',gt_data)
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
# print('density_map: ',density_map)
gt_count = np.sum(gt_data)
# print('gt_count: ',gt_count)
et_count = np.sum(density_map)
# print('et_count: ',et_count)
mae += abs(gt_count - et_count)
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)
