def train(args):
batch_size = args.batch_size
epoch = args.epoch
network = args.network
opt = args.opt
train = unpickle(args.train_path)
test = unpickle(args.test_path)
train_data = train[b'data']
test_data = test[b'data']
x_train = train_data.reshape(train_data.shape[0], 3, 32, 32)
x_train = x_train.transpose(0, 2, 3, 1)
y_train = train[b'fine_labels']
x_test = test_data.reshape(test_data.shape[0], 3, 32, 32)
x_test = x_test.transpose(0, 2, 3, 1)
y_test = test[b'fine_labels']
x_train = norm_images(x_train)
x_test = norm_images(x_test)
print('-------------------------------')
print('--train/test len: ', len(train_data), len(test_data))
print('--x_train norm: ', compute_mean_var(x_train))
print('--x_test norm: ', compute_mean_var(x_test))
print('--batch_size: ', batch_size)
print('--epoch: ', epoch)
print('--network: ', network)
print('--opt: ', opt)
print('-------------------------------')
if not os.path.exists('./trans/tran.tfrecords'):
generate_tfrecord(x_train, y_train, './trans/', 'tran.tfrecords')
generate_tfrecord(x_test, y_test, './trans/', 'test.tfrecords')
dataset = tf.data.TFRecordDataset('./trans/tran.tfrecords')
dataset = dataset.map(parse_function)
dataset = dataset.shuffle(buffer_size=50000)
dataset = dataset.batch(batch_size)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
x_input = tf.placeholder(tf.float32, [None, 32, 32, 3])
y_input = tf.placeholder(tf.int64, [None, ])
y_input_one_hot = tf.one_hot(y_input, 100)
lr = tf.placeholder(tf.float32, [])
if network == 'resnet50':
prob = resnet50(x_input, is_training=True, reuse=False, kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnet34':
prob = resnet34(x_input, is_training=True, reuse=False,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer())
elif network == 'resnet18':
prob = resnet18(x_input, is_training=True, reuse=False,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer())
elif network == 'seresnet50':
prob = se_resnet50(x_input, is_training=True, reuse=False, kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnet110':
prob = resnet110(x_input, is_training=True, reuse=False, kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnet110':
prob = se_resnet110(x_input, is_training=True, reuse=False, kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnet152':
prob = se_resnet152(x_input, is_training=True, reuse=False, kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnet152':
prob = resnet152(x_input, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnet_fixed':
prob = get_resnet(x_input, 152, trainable=True, w_init=tf.orthogonal_initializer())
elif network == 'densenet121':
prob = densenet121(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'densenet169':
prob = densenet169(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'densenet201':
prob = densenet201(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'densenet161':
prob = densenet161(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'densenet100bc':
prob = densenet100bc(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'densenet190bc':
prob = densenet190bc(x_input, reuse=False, is_training=True, kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnext50':
prob = resnext50(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnext110':
prob = resnext110(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
elif network == 'resnext152':
prob = resnext152(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnext50':
prob = se_resnext50(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnext110':
prob = se_resnext110(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
elif network == 'seresnext152':
prob = se_resnext152(x_input, reuse=False, is_training=True, cardinality=32,
kernel_initializer=tf.orthogonal_initializer())
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prob, labels=y_input_one_hot))
conv_var = [var for var in tf.trainable_variables() if 'conv' in var.name]
l2_loss = tf.add_n([tf.nn.l2_loss(var) for var in conv_var])
loss = l2_loss * 5e-4 + loss
if opt == 'adam':
opt = tf.train.AdamOptimizer(lr)
elif opt == 'momentum':
opt = tf.train.MomentumOptimizer(lr, 0.9)
elif opt == 'nesterov':
opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = opt.minimize(loss)
logit_softmax = tf.nn.softmax(prob)
acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logit_softmax, 1), y_input), tf.float32))
# -------------------------------Test-----------------------------------------
if not os.path.exists('./trans/tran.tfrecords'):
generate_tfrecord(x_test, y_test, './trans/', 'test.tfrecords')
dataset_test = tf.data.TFRecordDataset('./trans/test.tfrecords')
dataset_test = dataset_test.map(parse_test)
dataset_test = dataset_test.shuffle(buffer_size=10000)
dataset_test = dataset_test.batch(128)
iterator_test = dataset_test.make_initializable_iterator()
next_element_test = iterator_test.get_next()
if network == 'resnet50':
prob_test = resnet50(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'resnet18':
prob_test = resnet18(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'resnet34':
prob_test = resnet34(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'seresnet50':
prob_test = se_resnet50(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'resnet110':
prob_test = resnet110(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'seresnet110':
prob_test = se_resnet110(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'seresnet152':
prob_test = se_resnet152(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'resnet152':
prob_test = resnet152(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'seresnet_fixed':
prob_test = get_resnet(x_input, 152, type='se_ir', trainable=False, reuse=True)
elif network == 'densenet121':
prob_test = densenet121(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'densenet169':
prob_test = densenet169(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'densenet201':
prob_test = densenet201(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'densenet161':
prob_test = densenet161(x_input, is_training=False, reuse=True, kernel_initializer=None)
elif network == 'densenet100bc':
prob_test = densenet100bc(x_input, reuse=True, is_training=False, kernel_initializer=None)
elif network == 'densenet190bc':
prob_test = densenet190bc(x_input, reuse=True, is_training=False, kernel_initializer=None)
elif network == 'resnext50':
prob_test = resnext50(x_input, is_training=False, reuse=True, cardinality=32, kernel_initializer=None)
elif network == 'resnext110':
prob_test = resnext110(x_input, is_training=False, reuse=True, cardinality=32, kernel_initializer=None)
elif network == 'resnext152':
prob_test = resnext152(x_input, is_training=False, reuse=True, cardinality=32, kernel_initializer=None)
elif network == 'seresnext50':
prob_test = se_resnext50(x_input, reuse=True, is_training=False, cardinality=32, kernel_initializer=None)
elif network == 'seresnext110':
prob_test = se_resnext110(x_input, reuse=True, is_training=False, cardinality=32, kernel_initializer=None)
elif network == 'seresnext152':
prob_test = se_resnext152(x_input, reuse=True, is_training=False, cardinality=32, kernel_initializer=None)
logit_softmax_test = tf.nn.softmax(prob_test)
acc_test = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(logit_softmax_test, 1), y_input), tf.float32))
# ----------------------------------------------------------------------------
saver = tf.train.Saver(max_to_keep=1, var_list=tf.global_variables())
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
now_lr = 0.001 # Warm Up
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
counter = 0
max_test_acc = -1
for i in range(epoch):
sess.run(iterator.initializer)
while True:
try:
batch_train, label_train = sess.run(next_element)
_, loss_val, acc_val, lr_val = sess.run([train_op, loss, acc, lr],
feed_dict={x_input: batch_train, y_input: label_train,
lr: now_lr})
counter += 1
if counter % 100 == 0:
print('counter: ', counter, 'loss_val', loss_val, 'acc: ', acc_val)
if counter % 1000 == 0:
print('start test ')
sess.run(iterator_test.initializer)
avg_acc = []
while True:
try:
batch_test, label_test = sess.run(next_element_test)
acc_test_val = sess.run(acc_test, feed_dict={x_input: batch_test, y_input: label_test})
avg_acc.append(acc_test_val)
except tf.errors.OutOfRangeError:
print('end test ', np.sum(avg_acc) / len(y_test))
now_test_acc = np.sum(avg_acc) / len(y_test)
if now_test_acc > max_test_acc:
print('***** Max test changed: ', now_test_acc)
max_test_acc = now_test_acc
filename = 'params/distinct/' + network + '_{}.ckpt'.format(counter)
# saver.save(sess, filename)
break
except tf.errors.OutOfRangeError:
print('end epoch %d/%d , lr: %f' % (i, epoch, lr_val))
now_lr = lr_schedule(i, args.epoch)
break
train_cache,
flip=True,
mask=True)
focus_dl = DataLoader(dataset=focus_ds,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=4)
train_dl = DataLoader(dataset=train_ds,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=4)
model = densenet161((1, 128, 128))
model.cuda()
criterion = BCEWithLogitsLoss().cuda()
optimizer = Adam(model.parameters(), lr=1e-3)
scheduler = MultiStepLR(optimizer, milestones=[30, 45, 60], gamma=0.1)
earlystopper = EarlyStopping(mode='max',
min_delta=0.0001,
patience=15,
percentage=False)
best_score = 0
model_state_path = os.path.join(
Path.MODEL_DIR, 'densenet161_noisy_gpu_{}.tar'.format(args.gpu))
for epoch in range(MAX_ITERATIONS):
model.train()
for idx, ((x1, y1), (x2, y2)) in enumerate(zip(focus_dl, train_dl)):
def test(args):
# train = unpickle('/data/ChuyuanXiong/up/cifar-100-python/train')
# train_data = train[b'data']
# x_train = train_data.reshape(train_data.shape[0], 3, 32, 32)
# x_train = x_train.transpose(0, 2, 3, 1)
test = unpickle(args.test_path)
test_data = test[b'data']
x_test = test_data.reshape(test_data.shape[0], 3, 32, 32)
x_test = x_test.transpose(0, 2, 3, 1)
y_test = test[b'fine_labels']
x_test = norm_images(x_test)
# x_test = norm_images_using_mean_var(x_test, *compute_mean_var(x_train))
network = args.network
ckpt = args.ckpt
x_input = tf.placeholder(tf.float32, [None, 32, 32, 3])
y_input = tf.placeholder(tf.int64, [None, ])
# -------------------------------Test-----------------------------------------
if not os.path.exists('./trans/test.tfrecords'):
generate_tfrecord(x_test, y_test, './trans/', 'test.tfrecords')
dataset_test = tf.data.TFRecordDataset('./trans/test.tfrecords')
dataset_test = dataset_test.map(parse_test)
dataset_test = dataset_test.shuffle(buffer_size=10000)
dataset_test = dataset_test.batch(128)
iterator_test = dataset_test.make_initializable_iterator()
next_element_test = iterator_test.get_next()
if network == 'resnet50':
prob_test = resnet50(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'resnet18':
prob_test = resnet18(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'resnet34':
prob_test = resnet34(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'seresnet50':
prob_test = se_resnet50(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'resnet110':
prob_test = resnet110(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'seresnet110':
prob_test = se_resnet110(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'seresnet152':
prob_test = se_resnet152(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'resnet152':
prob_test = resnet152(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'seresnet_fixed':
prob_test = get_resnet(x_input, 152, type='se_ir', trainable=False, reuse=True)
elif network == 'densenet121':
prob_test = densenet121(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'densenet169':
prob_test = densenet169(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'densenet201':
prob_test = densenet201(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'densenet161':
prob_test = densenet161(x_input, is_training=False, reuse=False, kernel_initializer=None)
elif network == 'densenet100bc':
prob_test = densenet100bc(x_input, reuse=False, is_training=False, kernel_initializer=None)
elif network == 'densenet190bc':
prob_test = densenet190bc(x_input, reuse=False, is_training=False, kernel_initializer=None)
elif network == 'resnext50':
prob_test = resnext50(x_input, is_training=False, reuse=False, cardinality=32, kernel_initializer=None)
elif network == 'resnext110':
prob_test = resnext110(x_input, is_training=False, reuse=False, cardinality=32, kernel_initializer=None)
elif network == 'resnext152':
prob_test = resnext152(x_input, is_training=False, reuse=False, cardinality=32, kernel_initializer=None)
elif network == 'seresnext50':
prob_test = se_resnext50(x_input, reuse=False, is_training=False, cardinality=32, kernel_initializer=None)
elif network == 'seresnext110':
prob_test = se_resnext110(x_input, reuse=False, is_training=False, cardinality=32, kernel_initializer=None)
elif network == 'seresnext152':
prob_test = se_resnext152(x_input, reuse=False, is_training=False, cardinality=32, kernel_initializer=None)
# prob_test = tf.layers.dense(prob_test, 100, reuse=True, name='before_softmax')
logit_softmax_test = tf.nn.softmax(prob_test)
acc_test = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(logit_softmax_test, 1), y_input), tf.float32))
var_list = tf.trainable_variables()
g_list = tf.global_variables()
bn_moving_vars = [g for g in g_list if 'moving_mean' in g.name]
bn_moving_vars += [g for g in g_list if 'moving_variance' in g.name]
var_list += bn_moving_vars
saver = tf.train.Saver(var_list=var_list)
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver.restore(sess, ckpt)
sess.run(iterator_test.initializer)
avg_acc = []
while True:
try:
batch_test, label_test = sess.run(next_element_test)
acc_test_val = sess.run(acc_test, feed_dict={x_input: batch_test, y_input: label_test})
avg_acc.append(acc_test_val)
except tf.errors.OutOfRangeError:
print('end test ', np.sum(avg_acc) / len(y_test))
break
import numpy as np
from model import densenet161
import tensorflow as tf
val_id = [7,8,11,12,13,17,19,20,21,22,23,24,25,26,27,28,31,32,33,3]
trainId_to_id = {v:val_id[v] for v in range(20)}
id_to_trainId_map_func = np.vectorize(trainId_to_id.get)
if __name__ == "__main__":
img_dir = "./imgs"
imgs = os.listdir(img_dir)
input = tf.placeholder(dtype=tf.float32,shape=(1,1024,2048,3))
model = densenet161(input, num_classes=19, output_stride=8, is_training=False, reuse=False)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
tf.train.Saver().restore(sess, "./weight/model0.ckpt")
for file in imgs:
img_path = img_dir+"/"+file
img = cv2.imread(img_path)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
max = img.max()
min = img.min()
img = (img - min) / (max - min)
image = (img - np.array([0.290101, 0.328081, 0.286964])) / np.array([0.182954, 0.186566, 0.184475])
img = image[np.newaxis,:].astype(np.float32)
cla = sess.run(model,feed_dict={input:img})
tt = np.argmax(cla[0], axis=-1)
pre = np.asanyarray(tt).astype(np.uint8)