def main(_):
# Placeholders
x = tf.placeholder(tf.float32, [FLAGS.batch_size, 224, 224, 3])
y = tf.placeholder(tf.float32, [None, FLAGS.num_classes])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
model.inference(x)
# Accuracy of the model
correct_pred = tf.equal(tf.argmax(model.score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver()
test_preprocessor = BatchPreprocessor(dataset_file_path=FLAGS.test_file,
num_classes=FLAGS.num_classes,
output_size=[224, 224])
test_batches_per_epoch = np.floor(
len(test_preprocessor.labels) / FLAGS.batch_size).astype(np.int16)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
saver.restore(sess, FLAGS.ckpt)
test_acc = 0.
test_count = 0
for _ in range(test_batches_per_epoch):
batch_tx, batch_ty = test_preprocessor.next_batch(FLAGS.batch_size)
acc = sess.run(accuracy,
feed_dict={
x: batch_tx,
y: batch_ty,
dropout_keep_prob: 1.
})
test_acc += acc
test_count += 1
test_acc /= test_count
print("{} Test Accuracy = {:.4f}".format(datetime.datetime.now(),
test_acc))
def predict(path, modelpath):
with tf.Graph().as_default():
# Placeholders
x = tf.placeholder(tf.float32, [1, 224, 224, 3])
dropout_keep_prob = tf.placeholder(tf.float32)
imgs = []
# path='/home/ugrad/Shang/animal/1_.jpg'
# image = cv2.imread(path,0)
# cv2.imwrite(path,img)
img = cv2.imread(path)
img = cv2.resize(img, (224, 224))
img = img.astype(np.float32)
imgs.append(img)
# img=Image.open(path)
# img = np.array(img)
# img = tf.cast(img, tf.float32)
# img = tf.reshape(img, [1, 227, 227, 3])
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
logits = model.inference(x)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
# Load the pretrained weights
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, modelpath)
prediction = sess.run(logits,
feed_dict={
x: imgs,
dropout_keep_prob: 1.
})
# print(prediction)
max_index = np.argmax(prediction)
print(max_index)
return max_index
def main(_):
# Placeholders
x = tf.placeholder(tf.float32, [1, 224, 224, 3])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
model.inference(x)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
saver.restore(sess, FLAGS.ckpt)
batch_x = np.ndarray([1, 224, 224, 3])
# Read image and resize it
img = cv2.imread(FLAGS.input_image)
img = cv2.resize(img, (224, 224))
img = img.astype(np.float32)
# Subtract mean color
img -= np.array([132.2766, 139.6506, 146.9702])
batch_x[0] = img
scores = sess.run(model.score,
feed_dict={
x: batch_x,
dropout_keep_prob: 1.
})
print(scores)
def main(_):
# Create training directories
now = datetime.datetime.now()
train_dir_name = now.strftime('vggnet_%Y%m%d_%H%M%S')
train_dir = os.path.join(FLAGS.tensorboard_root_dir, train_dir_name)
checkpoint_dir = os.path.join(train_dir, 'checkpoint')
tensorboard_dir = os.path.join(train_dir, 'tensorboard')
tensorboard_train_dir = os.path.join(tensorboard_dir, 'train')
tensorboard_val_dir = os.path.join(tensorboard_dir, 'val')
if not os.path.isdir(FLAGS.tensorboard_root_dir):
os.mkdir(FLAGS.tensorboard_root_dir)
if not os.path.isdir(train_dir): os.mkdir(train_dir)
if not os.path.isdir(checkpoint_dir): os.mkdir(checkpoint_dir)
if not os.path.isdir(tensorboard_dir): os.mkdir(tensorboard_dir)
if not os.path.isdir(tensorboard_train_dir):
os.mkdir(tensorboard_train_dir)
if not os.path.isdir(tensorboard_val_dir): os.mkdir(tensorboard_val_dir)
# Write flags to txt
flags_file_path = os.path.join(train_dir, 'flags.txt')
flags_file = open(flags_file_path, 'w')
flags_file.write('learning_rate={}\n'.format(FLAGS.learning_rate))
flags_file.write('dropout_keep_prob={}\n'.format(FLAGS.dropout_keep_prob))
flags_file.write('num_epochs={}\n'.format(FLAGS.num_epochs))
flags_file.write('batch_size={}\n'.format(FLAGS.batch_size))
#flags_file.write('train_layers={}\n'.format(FLAGS.train_layers))
flags_file.write('tensorboard_root_dir={}\n'.format(
FLAGS.tensorboard_root_dir))
flags_file.write('log_step={}\n'.format(FLAGS.log_step))
flags_file.close()
# Placeholders
img_size = 256
x = tf.placeholder(tf.float32, [FLAGS.batch_size, img_size, img_size, 3])
y = tf.placeholder(tf.float32, [None, FLAGS.num_classes])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
#train_layers = FLAGS.train_layers.split(',')
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
loss = model.loss(x, y)
#train_op = model.optimize(FLAGS.learning_rate, train_layers)
train_op = model.optimize(FLAGS.learning_rate)
# Training accuracy of the model
correct_pred = tf.equal(tf.argmax(model.score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Summaries
tf.summary.scalar('train_loss', loss)
tf.summary.scalar('train_accuracy', accuracy)
merged_summary = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(tensorboard_train_dir)
val_writer = tf.summary.FileWriter(tensorboard_val_dir)
saver = tf.train.Saver()
# Batch preprocessors
train_preprocessor = BatchPreprocessor(
dataset_file_path=FLAGS.training_file,
num_classes=FLAGS.num_classes,
output_size=[img_size, img_size],
horizontal_flip=True,
shuffle=True)
val_preprocessor = BatchPreprocessor(dataset_file_path=FLAGS.val_file,
num_classes=FLAGS.num_classes,
output_size=[img_size, img_size])
# Get the number of training/validation steps per epoch
train_batches_per_epoch = np.floor(
len(train_preprocessor.labels) / FLAGS.batch_size).astype(np.int16)
val_batches_per_epoch = np.floor(
len(val_preprocessor.labels) / FLAGS.batch_size).astype(np.int16)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer.add_graph(sess.graph)
# Directly restore (your model should be exactly the same with checkpoint)
# saver.restore(sess, "/Users/dgurkaynak/Projects/marvel-training/alexnet64-fc6/model_epoch10.ckpt")
print("{} Start training...".format(datetime.datetime.now()))
print("{} Open Tensorboard at --logdir {}".format(
datetime.datetime.now(), tensorboard_dir))
for epoch in range(FLAGS.num_epochs):
print("{} Epoch number: {}".format(datetime.datetime.now(),
epoch + 1))
step = 1
# Start training
while step < train_batches_per_epoch:
batch_xs, batch_ys = train_preprocessor.next_batch(
FLAGS.batch_size)
sess.run(train_op,
feed_dict={
x: batch_xs,
y: batch_ys,
dropout_keep_prob: FLAGS.dropout_keep_prob
})
# Logging
if step % FLAGS.log_step == 0:
s = sess.run(merged_summary,
feed_dict={
x: batch_xs,
y: batch_ys,
dropout_keep_prob: 1.
})
train_writer.add_summary(
s, epoch * train_batches_per_epoch + step)
step += 1
# Epoch completed, start validation
print("{} Start validation".format(datetime.datetime.now()))
test_acc = 0.
test_count = 0
for _ in range(val_batches_per_epoch):
batch_tx, batch_ty = val_preprocessor.next_batch(
FLAGS.batch_size, 1)
acc = sess.run(accuracy,
feed_dict={
x: batch_tx,
y: batch_ty,
dropout_keep_prob: 1.
})
test_acc += acc
test_count += 1
test_acc /= test_count
s = tf.Summary(value=[
tf.Summary.Value(tag="validation_accuracy",
simple_value=test_acc)
])
val_writer.add_summary(s, epoch + 1)
print("{} Validation Accuracy = {:.4f}".format(
datetime.datetime.now(), test_acc))
# Reset the dataset pointers
val_preprocessor.reset_pointer()
train_preprocessor.reset_pointer()
print("{} Saving checkpoint of model...".format(
datetime.datetime.now()))
#save checkpoint of the model
checkpoint_path = os.path.join(
checkpoint_dir, 'model_epoch' + str(epoch + 1) + '.ckpt')
save_path = saver.save(sess, checkpoint_path)
print("{} Model checkpoint saved at {}".format(
datetime.datetime.now(), checkpoint_path))
def main(_):
# Create training directories
now = datetime.datetime.now()
train_dir_name = now.strftime('vggnet_%Y%m%d_%H%M%S')
train_dir = os.path.join(FLAGS.tensorboard_root_dir, train_dir_name)
checkpoint_dir = os.path.join(train_dir, 'checkpoint')
tensorboard_dir = os.path.join(train_dir, 'tensorboard')
tensorboard_train_dir = os.path.join(tensorboard_dir, 'train')
tensorboard_val_dir = os.path.join(tensorboard_dir, 'val')
if not os.path.isdir(FLAGS.tensorboard_root_dir):
os.mkdir(FLAGS.tensorboard_root_dir)
if not os.path.isdir(train_dir): os.mkdir(train_dir)
if not os.path.isdir(checkpoint_dir): os.mkdir(checkpoint_dir)
if not os.path.isdir(tensorboard_dir): os.mkdir(tensorboard_dir)
if not os.path.isdir(tensorboard_train_dir):
os.mkdir(tensorboard_train_dir)
if not os.path.isdir(tensorboard_val_dir): os.mkdir(tensorboard_val_dir)
# Placeholders
img_size = 256
x = tf.placeholder(tf.float32, [FLAGS.batch_size, img_size, img_size, 3])
y = tf.placeholder(tf.float32, [None, FLAGS.num_classes])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
#train_layers = FLAGS.train_layers.split(',')
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
loss = model.loss(x, y)
#train_op = model.optimize(FLAGS.learning_rate, train_layers)
train_op = model.optimize(FLAGS.learning_rate)
# Training accuracy of the model
correct_pred = tf.equal(tf.argmax(model.score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Summaries
tf.summary.scalar('train_loss', loss)
tf.summary.scalar('train_accuracy', accuracy)
merged_summary = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(tensorboard_train_dir)
val_writer = tf.summary.FileWriter(tensorboard_val_dir)
saver = tf.train.Saver()
# Batch preprocessors
val_preprocessor = BatchPreprocessor(dataset_file_path=FLAGS.val_file,
num_classes=FLAGS.num_classes,
output_size=[img_size, img_size])
# Get the number of training/validation steps per epoch
val_batches_per_epoch = np.floor(
len(val_preprocessor.labels) / FLAGS.batch_size).astype(np.int16)
test_accuracy = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer.add_graph(sess.graph)
# Directly restore (your model should be exactly the same with checkpoint)
saver.restore(sess, FLAGS.ckpt_path)
print("{} Start training...".format(datetime.datetime.now()))
print("{} Open Tensorboard at --logdir {}".format(
datetime.datetime.now(), tensorboard_dir))
for epoch in range(FLAGS.num_epochs):
print("{} Epoch number: {}".format(datetime.datetime.now(),
epoch + 1))
step = 1
# Epoch completed, start validation
print("{} Start Test".format(datetime.datetime.now()))
test_acc = 0.
test_count = 0
for _ in range(val_batches_per_epoch):
batch_tx, batch_ty = val_preprocessor.next_batch(
FLAGS.batch_size, 1)
acc = sess.run(accuracy,
feed_dict={
x: batch_tx,
y: batch_ty,
dropout_keep_prob: 1.
})
test_acc += acc
test_count += 1
test_acc /= test_count
print("{} Test Accuracy = {:.4f}".format(datetime.datetime.now(),
test_acc))
test_accuracy = test_acc
# Reset the dataset pointers
val_preprocessor.reset_pointer()
# Write flags to txt
flags_file_path = os.path.join(train_dir, 'flags.txt')
flags_file = open(flags_file_path, 'w')
flags_file.write('batch_size={}\n'.format(FLAGS.batch_size))
flags_file.write('log_step={}\n'.format(FLAGS.log_step))
flags_file.write('checkpoint_path={}\n'.format(FLAGS.ckpt_path))
flags_file.write('test_accuracy={}'.format(test_accuracy))
flags_file.close()
image = np.ndarray([len(paths), 224, 224, 3])
for i in range(len(paths)):
img = cv2.imread(paths[i])
img = cv2.resize(img, (224, 224))
img = img.astype(np.float32)
img -= np.array(mean_color)
image[i] = img
return image
img_input = tf.placeholder(tf.float32, shape=[None, 224, 224, 3])
score = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=1).inference(img_input,
False) #get score
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, ckpt_filename)
iternum = np.floor(len(images) / FLAGS.batch_size).astype(np.int16)
print(iternum)
def savecsv(result):
df = pd.DataFrame({