def main(_):
print 'reading npy...'
data = np.load('../data/1st.npy')
#jpg_list = np.load('64bin.npy')
jpg_list = np.load('../data/nlcd+vae+image64/input_images_64.npy')
test_order = np.load('../data/test.npy')
print 'reading finished'
sess = tf.Session()
print 'building network...'
hg = resnet_test.resnet(is_training=False)
global_step = tf.Variable(0,name='global_step',trainable=False)
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir,sess.graph)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess,FLAGS.checkpoint_path)
print 'restoring from '+FLAGS.checkpoint_path
def test_step():
print 'Testing...'
all_ce_loss = 0
all_output = []
all_label = []
batch_size = 18
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])/128.0
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
input_nlcd = get_data.get_nlcd(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.input_nlcd]=input_nlcd
feed_dict[hg.keep_prob]=1.0
ce_loss,output= sess.run([hg.ce_loss,hg.output],feed_dict)
all_ce_loss += ce_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
loglike = all_ce_loss/(int(len(test_order)/batch_size))
np.save('output.npy',all_output)
np.save('label.npy',all_label)
auc = roc_auc_score(all_label,all_output)
#loglike = log_likelihood(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc,loglike)
print(tempstr)
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
auc_2 = roc_auc_score(all_label,all_output)
print 'ap:'+str(ap)
print 'auc_2:'+str(auc_2)
test_step()
def main(_):
print 'reading npy...'
data = np.load('../data/1st.npy')
jpg_list = np.load('../data/32bin.npy')
test_order = np.load('../data/test.npy')
print 'reading finished'
sess = tf.Session()
print 'building network...'
hg = resnet_test.resnet(is_training=False)
global_step = tf.Variable(0,name='global_step',trainable=False)
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir,sess.graph)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess,FLAGS.checkpoint_path)
print 'restoring from '+FLAGS.checkpoint_path
def test_step():
print 'Testing...'
all_ce_loss = 0
all_output = []
all_label = []
batch_size = 18
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feed_dict[hg.input_label]=input_label
feed_dict[hg.keep_prob]=1.0
ce_loss,output= sess.run([hg.ce_loss,hg.output],feed_dict)
all_ce_loss += ce_loss
for i in output:
all_output.append(i)
for i in input_label:
all_label.append(i)
all_output = np.array(all_output)
all_label = np.array(all_label)
#average_precision = average_precision_score(all_label,all_output)
loglike = all_ce_loss/(int(len(test_order)/batch_size))
np.save('output.npy',all_output)
np.save('label.npy',all_label)
auc = roc_auc_score(all_label,all_output)
#loglike = log_likelihood(all_label,all_output)
time_str = datetime.datetime.now().isoformat()
tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc,loglike)
print(tempstr)
all_output=np.reshape(all_output,(-1))
all_label=np.reshape(all_label,(-1))
ap = average_precision_score(all_label,all_output)
auc_2 = roc_auc_score(all_label,all_output)
print 'ap:'+str(ap)
print 'auc_2:'+str(auc_2)
test_step()
o_path = os.getcwd()
sys.path.append(o_path)
from vgg_net import utils
from IP102.dataset_ip102 import Dataset_IP102, transform
file_dir = 'F:/5.datasets/ip102_v1.1/'
train_dataset = Dataset_IP102(file_dir, train=True, transforms=data_tf)
train_data = t.utils.data.DataLoader(
train_dataset,
batch_size=64, #14
shuffle=True,
drop_last=True)
test_dataset = Dataset_IP102(file_dir, train=False, transforms=data_tf)
test_data = t.utils.data.DataLoader(
test_dataset,
batch_size=64, #14
shuffle=True,
drop_last=True)
net = resnet(3, 102)
#optimizer = t.optim.SGD(net.parameters(),lr=0.001)
#test adam
optimizer = t.optim.Adam(net.parameters(), lr=0.01)
criterion = t.nn.CrossEntropyLoss()
utils.train(net, train_data, test_data, 20, optimizer, criterion, 'resnet')
def main(_):
print 'reading npy...'
#trainlist, labels = read_csv.train_data()
#jpg_list = np.load('../jpg_1st.npy')
data = np.load('../1st.npy')
jpg_list = []
for i in range(len(data)):
jpg_list.append(str(i) + '.jpg')
#train_order = np.load('../train.npy')
#test_order = np.load('../test.npy')
test_order = []
for i in range(len(data)):
test_order.append(i)
sess = tf.Session()
arg_scope = resnet_v2.resnet_arg_scope()
print 'building network...'
with slim.arg_scope(arg_scope):
hg = resnet_test.resnet(is_training=False)
init_fn = _get_init_fn()
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(max_to_keep=None)
init_fn(sess)
print 'building finished'
def test_step():
print 'testing...'
# all_ce_loss = 0
# all_l2_loss = 0
# all_total_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size = 17 * 3
for i in range(int(len(test_order) / batch_size)):
input_image = get_data.get_jpg_test(
jpg_list, test_order[batch_size * i:batch_size * (i + 1)])
input_label = get_data.get_label(
data, test_order[batch_size * i:batch_size * (i + 1)])
feed_dict = {}
feed_dict[hg.input_image] = input_image
feature = sess.run(hg.feature, feed_dict)
for i in feature:
all_feature.append(i)
# for i in input_label:
# all_label.append(i)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
# #average_precision = average_precision_score(all_label,all_output)
# np.save('output.npy',all_output)
# np.save('label.npy',all_label)
# auc = roc_auc_score(all_label,all_output)
# loglike = log_likelihood(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc, loglike)
# print(tempstr)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# auc_2 = roc_auc_score(all_label,all_output)
# print 'ap:'+str(ap)
# print 'auc_2:'+str(auc_2)
np.save('resnet50_feature.npy', all_feature)
test_step()
def main(_):
print 'reading npy...'
#trainlist, labels = read_csv.train_data()
#jpg_list = np.load('../jpg_1st.npy')
data = np.load('../1st.npy')
jpg_list=[]
for i in range(len(data)):
jpg_list.append(str(i)+'.jpg')
#train_order = np.load('../train.npy')
#test_order = np.load('../test.npy')
test_order = []
for i in range(len(data)):
test_order.append(i)
sess = tf.Session()
arg_scope = resnet_v2.resnet_arg_scope()
print 'building network...'
with slim.arg_scope(arg_scope):
hg = resnet_test.resnet(is_training=False)
init_fn = _get_init_fn()
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir,sess.graph)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(max_to_keep=None)
init_fn(sess)
print 'building finished'
def test_step():
print 'testing...'
# all_ce_loss = 0
# all_l2_loss = 0
# all_total_loss = 0
# all_output = []
# all_label = []
all_feature = []
batch_size=17*3
for i in range(int(len(test_order)/batch_size)):
input_image = get_data.get_jpg_test(jpg_list,test_order[batch_size*i:batch_size*(i+1)])
input_label = get_data.get_label(data,test_order[batch_size*i:batch_size*(i+1)])
feed_dict={}
feed_dict[hg.input_image]=input_image
feature = sess.run(hg.feature,feed_dict)
for i in feature:
all_feature.append(i)
# for i in input_label:
# all_label.append(i)
# all_output = np.array(all_output)
# all_label = np.array(all_label)
# #average_precision = average_precision_score(all_label,all_output)
# np.save('output.npy',all_output)
# np.save('label.npy',all_label)
# auc = roc_auc_score(all_label,all_output)
# loglike = log_likelihood(all_label,all_output)
# time_str = datetime.datetime.now().isoformat()
# tempstr = "{}: auc {:g}, log_likelihood {:g}".format(time_str, auc, loglike)
# print(tempstr)
# all_output=np.reshape(all_output,(-1))
# all_label=np.reshape(all_label,(-1))
# ap = average_precision_score(all_label,all_output)
# auc_2 = roc_auc_score(all_label,all_output)
# print 'ap:'+str(ap)
# print 'auc_2:'+str(auc_2)
np.save('resnet50_feature.npy',all_feature)
test_step()