])
tf.add_to_collection('enqueue', enqueue_op)
close_op = gpu_queue.close(cancel_pending_enqueues=True)
tf.add_to_collection('close_queue', close_op)
data, labels, epoch_ended = gpu_queue.dequeue()
data_per_replica = tf.split(data, MODEL_REPLICA, axis=1)
last_fully_activations = []
with tf.variable_scope('model_replicas'):
for j in range(MODEL_REPLICA):
last_fully_activations.append(
C3Dmodel.inference(tf.squeeze(data_per_replica[j],
axis=1),
EXAMPLES_PER_GPU,
dropout_placeholder,
is_training_placeholder,
NUM_CLASSES,
collection='network_output',
tensor_to_return='fully'))
tf.get_variable_scope().reuse_variables()
# concatenate replica's last fully activations and add fully_connected output-layer!
concatenated_tensor = tf.concat(last_fully_activations, axis=1)
with tf.variable_scope('out'):
wout = C3Dmodel.model_variable(
'wout', [MODEL_REPLICA * 4096, NUM_CLASSES],
C3Dmodel.weight_init, C3Dmodel.WEIGHT_DECAY)()
bout = C3Dmodel.model_variable('bout', [NUM_CLASSES],
C3Dmodel.bias_init)()
out = tf.matmul(concatenated_tensor, wout) + bout
# TODO: capture current adam learning rate
dropout_placeholder = tf.placeholder(tf.float32, name='dropout_placeholder')
is_training_placeholder = tf.placeholder(tf.bool, name='is_training_placeholder')
tf.add_to_collection("dropout", dropout_placeholder)
tf.add_to_collection("training", is_training_placeholder)
with tf.variable_scope(tf.get_variable_scope()):
for i in range(NUM_GPUS):
with tf.device('/gpu:%d'%i), tf.name_scope('Tower%d'%i) as scope:
input_placeholder, output_placeholder, epoch_ended_placeholder = queue_input_placeholders()
with tf.variable_scope('model_replicas'):
network_output = C3Dmodel.inference(
input_placeholder, EXAMPLES_PER_GPU, dropout_placeholder, is_training_placeholder, NUM_CLASSES,
collection='network_output')
xentropy_loss, regularization_loss = C3Dmodel.loss(network_output, output_placeholder, collection='xentropy_loss', scope=scope)
# train_step = C3Dmodel.train(xentropy_loss, 1e-04, global_step, collection='train_step')
train_step = optimizer.minimize(xentropy_loss, global_step=global_step)
def run_training():
with tf.Session(graph=my_graph, config=tf.ConfigProto(log_device_placement=True, allow_soft_placement=True)) as sess:
# with tf.Session(graph=my_graph, config=tf.ConfigProto(allow_soft_placement=True)) as sess:
assert(tf.get_default_graph() == my_graph)
sess.run(tf.global_variables_initializer())
my_graph.finalize()
starttime = time.time()
enqueue_op = gpu_queue.enqueue([
input_placeholder, output_placeholder,
epoch_ended_placeholder
])
tf.add_to_collection('enqueue', enqueue_op)
close_op = gpu_queue.close(cancel_pending_enqueues=True)
tf.add_to_collection('close_queue', close_op)
data, labels, epoch_ended = gpu_queue.dequeue()
with tf.variable_scope('model_replicas'):
network_output = C3Dmodel.inference(
data,
EXAMPLES_PER_GPU,
dropout_placeholder,
is_training_placeholder,
NUM_CLASSES,
collection='network_output')
xentropy_loss, regularization_loss = C3Dmodel.loss(
network_output,
labels,
collection='xentropy_loss',
scope=scope)
# train_step = C3Dmodel.train(xentropy_loss, 1e-04, global_step, collection='train_step')
grads = optimizer.compute_gradients(xentropy_loss)
tower_gradients.append(grads)
accuracy_op, accuracy_summary_op = C3Dmodel.accuracy(
network_output, labels, collection='accuracy_op')
CKPT = os.getcwd() + "/tf_checkpoints/run-20170828082324"
MAX_INPUT_LENGTH = 3
EVAL_INDXS = np.arange(0, 165, 10)
OFFSET = 16
input_placeholder = tf.placeholder(
tf.float32,
[None, TEMPORAL_DEPTH, INPUT_HEIGHT, INPUT_WIDTH, INPUT_CHANNELS],
name='input_placeholder')
onehot_label_placeholder = tf.placeholder(tf.float32, [NUM_CLASSES])
with tf.variable_scope('model_replicas'):
output = C3Dmodel.inference(input_placeholder,
-1,
1,
False,
NUM_CLASSES,
collection='network_output')
# softmax_output = tf.nn.softmax(output)
saver = tf.train.Saver()
# mean_prediction = tf.reduce_mean(output,0)
# correctly_classified = tf.equal(tf.argmax(mean_prediction), tf.argmax(onehot_label_placeholder))
with tf.Session() as sess:
# path = tf.train.get_checkpoint_state("./tf_checkpoints")
# graphsaver = tf.train.import_meta_graph(METAGRAPH)
for model_indx in EVAL_INDXS:
saver.restore(sess, CKPT + '/model-{}.ckpt'.format(model_indx))
print('Loaded model {}'.format(model_indx))