def evaluate_10crop_3():
val_filenames, val_labels = scene_input.list_images('validation')
with tf.Graph().as_default() as g:
keep_prob = tf.placeholder(tf.float32)
images_10crop_batched, label, val_data_init_op \
= scene_input.get_dataset_10crop_eval(val_filenames, val_labels)
with tf.name_scope('inference'):
conv_net = vgg.Vgg16()
conv_net.build(images_10crop_batched, keep_prob,
scene_input.num_classes)
prob_10crop = tf.reduce_max(conv_net.get_softmax(), axis=0)
# Calculate predictions.
print("prob_10crop.shape: {}".format(prob_10crop.shape))
print("label.shape: {}".format(label.shape))
prob_10crop = tf.reshape(prob_10crop, [-1, scene_input.num_classes])
label = tf.reshape(label, [-1])
print("prob_10crop.shape: {}".format(prob_10crop.shape))
print("label.shape: {}".format(label.shape))
top_1_op = tf.nn.in_top_k(prob_10crop, label, 1)
top_3_op = tf.nn.in_top_k(prob_10crop, label, 3)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
scene.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
# global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
raise ValueError(
"Cannot find checkpoint data in {}".format(checkpoint_dir))
# accuracy on validation set.
acc_top1, acc_top3 = get_accuracy_10crop(sess, top_1_op, top_3_op,
val_data_init_op,
keep_prob)
print('Val: accuracy (top1){0:.4f} (top3){1:.4f}'.format(
acc_top1, acc_top3))
def evaluate():
# Get the list of filenames and corresponding list of labels for training et validation
train_filenames, train_labels = scene_input.list_images('train')
val_filenames, val_labels = scene_input.list_images('validation')
with tf.Graph().as_default() as g:
keep_prob = tf.placeholder(tf.float32)
images, labels, train_data_init_op, val_data_init_op \
= scene_input.get_dataset(train_filenames, train_labels,
val_filenames, val_labels, batch_size)
with tf.name_scope('inference'):
conv_net = vgg.Vgg16()
conv_net.build(images, keep_prob, scene_input.num_classes)
logits = conv_net.get_softmax_linear()
# Calculate predictions.
top_1_op = tf.nn.in_top_k(logits, labels, 1)
top_3_op = tf.nn.in_top_k(logits, labels, 3)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
scene.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
# global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
raise ValueError(
"Cannot find checkpoint data in {}".format(checkpoint_dir))
# accuracy on validation set.
acc_top1, acc_top3 = get_accuracy(sess, top_1_op, top_3_op,
val_data_init_op, keep_prob)
print('Val: accuracy (top1){0:.4f} (top3){1:.4f}'.format(
acc_top1, acc_top3))
def main():
# Get the list of filenames and corresponding list of labels for training et validation
train_filenames, train_labels = scene_input.list_images('train')
val_filenames, val_labels = scene_input.list_images('validation')
# --------------------------------------------------------------------------
# In TensorFlow, you first want to define the computation graph with all the
# necessary operations: loss, training op, accuracy...
# Any tensor created in the `graph.as_default()` scope will be part of `graph`
graph = tf.Graph()
with graph.as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
keep_prob = tf.placeholder(tf.float32)
images, labels, train_data_init_op, val_data_init_op \
= scene_input.get_dataset_with_color_augmentation(train_filenames, train_labels,
val_filenames, val_labels, batch_size)
with tf.name_scope('inference'):
conv_net = vgg.Vgg16()
conv_net.build(images, keep_prob, scene_input.num_classes)
logits = conv_net.get_softmax_linear()
with tf.name_scope('loss'):
# Calculate the average cross entropy loss across the batch.
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits, name='cross_entropy_per_example')
loss = tf.reduce_mean(cross_entropy, name='cross_entropy_loss')
with tf.name_scope('train'):
full_train_op = train_momentum_sgd(loss, global_step)
with tf.name_scope('evaluation'):
# Evaluation metrics
prediction = tf.to_int32(tf.argmax(logits, 1))
labels = tf.to_int32(labels)
correct_prediction = tf.equal(prediction, labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
correct_prediction_top3 = tf.nn.in_top_k(logits, labels, 3)
accuracy_top3 = tf.reduce_mean(tf.cast(correct_prediction_top3, tf.float32))
init_var_op = tf.global_variables_initializer()
merged_summary = tf.summary.merge_all()
saver = tf.train.Saver()
tf.get_default_graph().finalize()
# --------------------------------------------------------------------------
# Now that we have built the graph and finalized it, we define the session.
# The session is the interface to *run* the computational graph.
# We can call our training operations with `sess.run(train_op)` for instance
with tf.Session(graph=graph) as sess:
sess.run(init_var_op)
# tensorboard writer.
writer = tf.summary.FileWriter(log_dir, graph)
# check model_dir for checkpoint file.
restore_epoch = None
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/model/model-10.xxx,
# extract epoch from it.
restore_epoch = ckpt.model_checkpoint_path.split('/')[-1].split('-')[2]
sess.run(global_step.assign((restore_epoch - 1) * steps_per_epoch))
print("restore check point from: {}".format(ckpt.model_checkpoint_path))
print("get epoch: {} step: {}".format(restore_epoch, (restore_epoch - 1) * steps_per_epoch))
else:
print('training whole conv net from scratch.')
start_time = time.time()
# Train the entire model for a few more epochs, continuing with the *same* weights.
for epoch in range(num_epochs):
if restore_epoch is not None and epoch < restore_epoch:
continue
else:
print('epoch {} / {}'.format(epoch + 1, num_epochs))
tick = time.time()
sess.run(train_data_init_op)
for i in tqdm.tqdm(range(steps_per_epoch)):
try:
if tensorboard_write_frq > 0 and i % tensorboard_write_frq == 0:
_, summary = sess.run([full_train_op, merged_summary], {keep_prob: dropout_keep_prob})
writer.add_summary(summary, epoch * steps_per_epoch + i)
else:
_ = sess.run(full_train_op, {keep_prob: dropout_keep_prob})
except tf.errors.OutOfRangeError:
break
tock = time.time()
print(check_time(tock - start_time, steps_per_epoch, tock - tick))
# check point
if (epoch + 1) % checkpoint_freq == 0:
saver.save(sess, os.path.join(checkpoint_dir, 'scene'), global_step=epoch + 1)
# Check on the train and val sets every epoch.
train_acc, train_loss = check_train(sess, correct_prediction, keep_prob,
train_data_init_op, n_batch=100, loss=loss)
print('Train: accuracy {0:.4f} loss {1:.4f}'.format(train_acc, train_loss))
val_acc, val_acc_top3 = check_val(sess, correct_prediction, keep_prob, val_data_init_op,
correct_prediction_top3=correct_prediction_top3)
print('Val: accuracy (top1){0:.4f} (top3){1:.4f}'.format(val_acc, val_acc_top3))