def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir)
a = bias_variable([3, 3])
b = tf.constant(0.2, shape=[3, 3])
c = tf.constant(10.0, shape=[3, 3])
d = a + b
e = tf.multiply(d, c)
relu1 = tf.nn.relu(e, name='relu1')
train_relu1 = tf.train.AdamOptimizer(1e-4).minimize(relu1)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
from tensorflow.python.profiler import model_analyzer
from tensorflow.python.profiler import option_builder
with tf.Session(config=get_sess_config()) as sess:
many_runs_timeline = TimeLiner()
sess.graph.get_operation_by_name(
'adam_optimizer/gradients/pool1/MaxPool_grad/MaxPoolGrad'
)._set_attr(
'_swap_to_host',
attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue(
i=[0, 1])))
sess.graph.get_operation_by_name(
'adam_optimizer/gradients/conv1/Relu_grad/ReluGrad')._set_attr(
'_swap_to_host', attr_value_pb2.AttrValue(i=1))
sess.graph.get_operation_by_name(
'adam_optimizer/gradients/pool2/MaxPool_grad/MaxPoolGrad'
)._set_attr(
'_swap_to_host',
attr_value_pb2.AttrValue(list=attr_value_pb2.AttrValue.ListValue(
i=[0, 1])))
sess.graph.get_operation_by_name(
'adam_optimizer/gradients/conv2/Relu_grad/ReluGrad')._set_attr(
'_swap_to_host', attr_value_pb2.AttrValue(i=1))
sess.graph.get_operation_by_name(
'adam_optimizer/gradients/conv2/Conv2D_grad/Conv2DBackpropInput'
)._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=2))
#sess.graph.get_operation_by_name('pool1/MaxPool')._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=0))
#gradient_ops = sess.graph.get_operation_by_name('adam_optimizer/gradients/conv2/Conv2D_grad/ShapeN')
#gradient_ops._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=0))
#gradient_ops._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=1))
sess.run(tf.global_variables_initializer())
profiler = model_analyzer.Profiler(sess.graph)
#for i in range(20000):
for i in range(FLAGS.iteration_count):
batch = mnist.train.next_batch(FLAGS.batch_size)
run_metadata = tf.RunMetadata()
sess.run(
train_step,
feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
#sess.run(train_relu1, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), run_metadata=run_metadata)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
chrome_trace = trace.generate_chrome_trace_format(
show_dataflow=True, show_memory=True)
many_runs_timeline.update_timeline(chrome_trace)
profiler.add_step(i, run_metadata)
# profile the timing of your model operations.
#opts = (tf.profiler.ProfileOptionBuilder(
# option_builder.ProfileOptionBuilder.time_and_memory())
# .select(['micros', 'bytes', 'occurrence', 'peak_bytes', 'residual_bytes', 'output_bytes'])
# .order_by('name').build())
#profiler.profile_operations(options=opts)
# can generate a timeline:
opts = (option_builder.ProfileOptionBuilder(
option_builder.ProfileOptionBuilder.time_and_memory()
).with_step(i).with_timeline_output(
"./timeline_output/step_" + FLAGS.mem_opt +
str(FLAGS.batch_size) + str(FLAGS.iteration_count)).build())
profiler.profile_graph(options=opts)
chrome_trace_filename = str(FLAGS.batch_size) + str(FLAGS.mem_opt) + "new"
graph_location = str(FLAGS.batch_size) + str(
FLAGS.mem_opt) + "_swap_test.pbtxt"
print('Saving graph to: %s' % graph_location)
tf.train.write_graph(sess.graph_def, '.', graph_location, as_text=True)
many_runs_timeline.save(chrome_trace_filename + '.ctf.json')
def main():
args = get_arguments()
try:
directories = validate_directories(args)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
with open(args.wavenet_params, 'r') as f:
wavenet_params = json.load(f)
# Create coordinator.
coord = tf.train.Coordinator()
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = args.silence_threshold if args.silence_threshold > \
EPSILON else None
gc_enabled = args.gc_channels is not None
lc_enabled = args.lc_channels is not None
lc_channels = args.lc_channels
reader = AudioReader(
args.data_dir,
coord,
sample_rate=wavenet_params['sample_rate'],
gc_enabled=gc_enabled,
lc_enabled=lc_enabled,
lc_channels=lc_channels,
receptive_field=WaveNetModel.calculate_receptive_field(
wavenet_params["filter_width"], wavenet_params["dilations"],
wavenet_params["scalar_input"],
wavenet_params["initial_filter_width"]),
sample_size=args.sample_size,
silence_threshold=silence_threshold)
audio_batch = reader.dequeue(args.batch_size)
if gc_enabled:
gc_id_batch = reader.dequeue_gc(args.batch_size)
else:
gc_id_batch = None
if lc_enabled:
lc_batch = reader.dequeue_lc(args.batch_size)
else:
lc_batch = None
# Create network.
net = WaveNetModel(
batch_size=args.batch_size,
dilations=wavenet_params["dilations"],
filter_width=wavenet_params["filter_width"],
residual_channels=wavenet_params["residual_channels"],
dilation_channels=wavenet_params["dilation_channels"],
skip_channels=wavenet_params["skip_channels"],
quantization_channels=wavenet_params["quantization_channels"],
use_biases=wavenet_params["use_biases"],
scalar_input=wavenet_params["scalar_input"],
initial_filter_width=wavenet_params["initial_filter_width"],
histograms=args.histograms,
global_condition_channels=args.gc_channels,
global_condition_cardinality=reader.gc_category_cardinality,
local_condition_channels=args.lc_channels)
if args.l2_regularization_strength == 0:
args.l2_regularization_strength = None
loss = net.loss(input_batch=audio_batch,
global_condition_batch=gc_id_batch,
local_condition_batch=lc_batch,
l2_regularization_strength=args.l2_regularization_strength)
optimizer = optimizer_factory[args.optimizer](
learning_rate=args.learning_rate, momentum=args.momentum)
trainable = tf.trainable_variables()
optim = optimizer.minimize(loss, var_list=trainable)
# Set up logging for TensorBoard.
writer = tf.summary.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
run_metadata = tf.RunMetadata()
summaries = tf.summary.merge_all()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.trainable_variables(),
max_to_keep=args.max_checkpoints)
try:
saved_global_step = load(saver, sess, restore_from)
if is_overwritten_training or saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
except:
print("Something went wrong while restoring checkpoint. "
"We will terminate training to avoid accidentally overwriting "
"the previous model.")
raise
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
step = None
last_saved_step = saved_global_step
try:
for step in range(saved_global_step + 1, args.num_steps):
start_time = time.time()
if args.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
print('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
summary, loss_value, _ = sess.run([summaries, loss, optim],
options=run_options,
run_metadata=run_metadata)
writer.add_summary(summary, step)
writer.add_run_metadata(run_metadata,
'step_{:04d}'.format(step))
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
summary, loss_value, _ = sess.run([summaries, loss, optim])
writer.add_summary(summary, step)
duration = time.time() - start_time
print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % args.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
except KeyboardInterrupt:
# Introduce a line break after ^C is displayed so save message
# is on its own line.
print()
finally:
if step > last_saved_step:
save(saver, sess, logdir, step)
coord.request_stop()
coord.join(threads)
def testing(self, sess, test_writer):
##=======================================
if USE_ROS:
import rospy
from sensor_msgs.msg import PointCloud,Image
from visualization_msgs.msg import MarkerArray, Marker
from tools.data_visualize import Boxes_labels_Gen, Image_Gen,PointCloud_Gen
rospy.init_node('rostensorflow')
pub = rospy.Publisher('prediction', PointCloud, queue_size=1000)
img_pub = rospy.Publisher('images_rgb', Image, queue_size=1000)
box_pub = rospy.Publisher('label_boxes', MarkerArray, queue_size=1000)
rospy.loginfo("ROS begins ...")
#=======================================
with tf.name_scope("Inference"):
RNet_rpn_yaw_pred = self.net.get_output('RNet_theta')[1]
RNet_rpn_yaw_gt_delta = self.net.get_output('cubic_grid')[1]
RNet_rpn_yaw_pred_toshow = RNet_rpn_yaw_pred+RNet_rpn_yaw_gt_delta
rpn_rois_3d = self.net.get_output('rpn_rois')[1]
with tf.name_scope('view_rpn_bv_tb'):
roi_bv = self.net.get_output('rpn_rois')[0]
data_bv = self.net.lidar_bv_data
image_rpn = tf.reshape(test_show_rpn_tf(data_bv,roi_bv), (1, 601, 601, -1))
tf.summary.image('lidar_bv_test', image_rpn)
merged = tf.summary.merge_all()
with tf.name_scope('load_weights'):
weights = self.args.weights
if weights.endswith('.ckpt'):
print 'Loading test model weights from {:s}'.format(self.args.weights)
self.saver.restore(sess, weights)
else:
print "error: Function [combinet_test.testing] can not load weights {:s}!".format(self.args.weights)
return 0
vispy_init() # TODO: Essential step(before sess.run) for using vispy beacuse of the bug of opengl or tensorflow
timer = Timer()
for idx in range(0,self.epoch):
# index_ = input('Type a new index: ')
blobs = self.dataset.get_minibatch(idx)
feed_dict = {
self.net.lidar3d_data: blobs['lidar3d_data'],
self.net.lidar_bv_data: blobs['lidar_bv_data'],
self.net.im_info: blobs['im_info'],
self.net.calib: blobs['calib']}
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
timer.tic()
pred_yaw_toshow_,rpn_rois_3d_,summary = \
sess.run([RNet_rpn_yaw_pred_toshow,rpn_rois_3d,merged],
feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
timer.toc()
if idx % 3 ==0 and cfg.TEST.DEBUG_TIMELINE:
# chrome://tracing
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file = open(cfg.LOG_DIR + '/' +'testing-step-'+ str(idx).zfill(7) + '.ctf.json', 'w')
trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
trace_file.close()
if idx % cfg.TEST.ITER_DISPLAY == 0:
pass
print 'Test: %06d/%06d speed: %.4f s / iter' % (idx+1, self.epoch, timer.average_time)
if VISION_DEBUG:
scan = blobs['lidar3d_data']
img = blobs['image_data']
cubic_cls_value = np.ones([cfg.TRAIN.RPN_POST_NMS_TOP_N],dtype=np.float32)*0
boxes=BoxAry_Theta(pre_box3d=rpn_rois_3d_,pre_theta_value=pred_yaw_toshow_,pre_cube_cls=cubic_cls_value)# RNet_rpn_yaw_pred_toshow_ rpn_rois_3d_[:,-1]
if USE_ROS:
from tools.data_visualize import PointCloud_Gen,Boxes_labels_Gen,Image_Gen
pointcloud = PointCloud_Gen(scan)
label_boxes = Boxes_labels_Gen(boxes, ns='Predict')
img_ros = Image_Gen(img)
pub.publish(pointcloud)
img_pub.publish(img_ros)
box_pub.publish(label_boxes)
else:
pcd_vispy(scan, img, boxes,index=idx,
save_img=True,#cfg.TEST.SAVE_IMAGE,
visible=False,
name='CubicNet testing')
if idx % 1 == 0 and cfg.TEST.TENSORBOARD:
test_writer.add_summary(summary, idx)
pass
print 'Testing process has done, happy every day !'
def train(self, train_data, train_label, valid_data=None, valid_label=None, learning_rate=0.01,
max_epochs=1000, keep_training=False):
""" This function defines the training process of the model
:param train_data: the input training data, must be a shape like [sample_number, data]
:param train_label: the input label of the training data, must be a shape like [sample_number, labels]
:param valid_data: the give validation data, must be a shape like [sample_number, data]
:param valid_label: the give validation labels, must be a shape like [sample_number, labels]
:param learning_rate: the learning rate of the optimizer
:param max_epochs: maximum epoch of the training
:param keep_training: determine to continue train the model
"""
if valid_data or valid_label is None:
valid_data = self.fashion_data.test.images
valid_label = self.fashion_data.test.labels
# Initialize all parameters
# Fashion data images size
pixel_size = train_data.shape[1]
class_number = train_label.shape[1]
graph = tf.Graph()
with graph.as_default(), tf.device('cpu:0'):
global_step = tf.Variable(0, name="global_step", trainable=False)
# Define input sample for each layers
with tf.name_scope('Inputs'):
x_sample, y_sample = self.create_placeholders(pixel_size, class_number)
# Define output layer
hidden_layer1 = self.add_layer(x_sample, pixel_size, 300, 'layer1', activation_function=tf.nn.sigmoid)
if FLAGS.REGULARIZATION == 'drop out':
keep_prob = tf.placeholder(tf.float32)
probability = 0.5
hidden_layer1 = tf.nn.dropout(hidden_layer1, keep_prob)
prediction = self.add_layer(hidden_layer1, 300, class_number, 'layer2', activation_function=tf.nn.softmax)
if FLAGS.REGULARIZATION == 'drop out':
prediction = tf.nn.dropout(prediction, keep_prob)
with tf.name_scope("Training"):
# Define loss function using cross entropy
with tf.name_scope("Loss_Function"):
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_sample *
tf.log(prediction + FLAGS.EPSI), reduction_indices=1))
tf.summary.scalar("Loss/train", cross_entropy)
if FLAGS.OPTIMIZER == 'Adam':
training = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy, global_step=global_step)
elif FLAGS.OPTIMIZER == 'Momentum':
training = tf.train.MomentumOptimizer(learning_rate, momentum=0.7).minimize(cross_entropy,
global_step=global_step)
else:
training = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy,
global_step=global_step)
# else:
# print('Please select a correct optimizer.')
with tf.name_scope("Accuracy"):
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_sample, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
tf.summary.scalar('accuracy', accuracy)
# Create Data Pipeline
batch_size = 512
minibatch = InputPipeline(batch_size, train_data, train_label)
minibatch.schedule(buffer_size=10000)
x_batch, y_batch = minibatch.next()
iterator_initializer, mini_dict = minibatch.initializer()
num_minibatch = int(train_data.shape[0] / batch_size)
folder_name = (FLAGS.SUMMARY_FOLDER, FLAGS.TIMELINE_FOLDER, FLAGS.LOG_FOLDER, FLAGS.CHECKINGPOINT_FOLDER)
for folder in folder_name:
if not os.path.exists(folder):
os.makedirs(folder)
f_result = open(FLAGS.LOG_FOLDER + '/training result_{:.0f}.txt'.format(time.time()), 'a')
# Initialize a session and saver
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver()
# Initialize Tensorboard Summary
merged_train = tf.summary.merge_all()
merged_test = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.SUMMARY_FOLDER + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.SUMMARY_FOLDER + '/test', sess.graph)
# Set runtime statistics option
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
start_time = time.time()
if FLAGS.VERBOSE:
print('Training Start!')
print('Optimizer:', FLAGS.OPTIMIZER)
print('Regularization method:', FLAGS.REGULARIZATION)
# Start training process and iteration is determined by max_epochs
if keep_training is True:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.CHECKINGPOINT_FOLDER))
if FLAGS.VERBOSE:
print("Restore model from ", FLAGS.CHECKINGPOINT_FOLDER)
for epoch in range(max_epochs):
sess.run(iterator_initializer, mini_dict)
# Start an epoch and training through all mini batches
for step in range(num_minibatch):
global_step_value = sess.run(global_step)
batch_x_sample, batch_y_sample = sess.run([x_batch, y_batch])
if FLAGS.REGULARIZATION == 'drop out':
sess.run(training, feed_dict={x_sample: batch_x_sample,
y_sample: batch_y_sample, keep_prob: probability})
else:
sess.run(training, feed_dict={x_sample: batch_x_sample, y_sample: batch_y_sample})
epoch = int(global_step_value / num_minibatch) + 1
# Start learning process and writer summary every 30 epochs
if epoch % 30 == 0 or epoch == max_epochs:
if FLAGS.REGULARIZATION == 'drop out':
loss_train = sess.run(cross_entropy, feed_dict={x_sample: train_data,
y_sample: train_label, keep_prob: 1})
loss_test = sess.run(cross_entropy, feed_dict={x_sample: valid_data,
y_sample: valid_label, keep_prob: 1})
acc_test = sess.run(accuracy, feed_dict={x_sample: valid_data,
y_sample: valid_label, keep_prob: 1})
train_summary = sess.run(merged_train,
feed_dict={x_sample: train_data,
y_sample: train_label, keep_prob: 1},
options=run_options, run_metadata=run_metadata)
test_summary = sess.run(merged_test, feed_dict={x_sample: valid_data,
y_sample: valid_label, keep_prob: 1},
options=run_options, run_metadata=run_metadata)
else:
loss_train = sess.run(cross_entropy, feed_dict={x_sample: train_data, y_sample: train_label})
loss_test = sess.run(cross_entropy, feed_dict={x_sample: valid_data, y_sample: valid_label})
acc_test = sess.run(accuracy, feed_dict={x_sample: valid_data, y_sample: valid_label})
train_summary = sess.run(merged_train,
feed_dict={x_sample: train_data, y_sample: train_label},
options=run_options, run_metadata=run_metadata)
test_summary = sess.run(merged_test, feed_dict={x_sample: valid_data, y_sample: valid_label},
options=run_options, run_metadata=run_metadata)
# Add tensorboard summary
train_writer.add_run_metadata(run_metadata, 'epoch%d' % epoch)
train_writer.add_summary(train_summary, global_step=epoch)
test_writer.add_summary(test_summary, global_step=epoch)
result_log = "Epoch: {}, Accuracy: {:.3f}, Loss train: {:.3f}," \
" Loss test: {:.3f}\n".format(epoch, acc_test, loss_train, loss_test)
if FLAGS.VERBOSE:
print('Adding run metadata for epoch:', epoch)
print(result_log)
# Save training logs to txt file
f_result.write('Adding run metadata for epoch:{}\n'.format(epoch))
f_result.write(result_log)
# Save runtime statistic results
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(FLAGS.TIMELINE_FOLDER + '/timeline_epoch_{}.json'.format(epoch), 'w') as f_tracing:
f_tracing.write(chrome_trace)
duration = time.time() - start_time
if FLAGS.VERBOSE:
print('Training for {} epochs took {:.3f} sec.\n'.format(epoch, duration))
print('Training process finished')
f_result.write('Training for {} epochs took {:.3f} sec.'.format(epoch, duration))
f_result.close()
# Save trained model to .ckpt files
saver.save(sess, FLAGS.CHECKINGPOINT_FOLDER + '/project1_trained_model')
sess.close()
def profile(run_metadata, epoch=0):
with open('profs/timeline_step' + str(epoch) + '.json', 'w') as f:
# Create the Timeline object, and write it to a json file
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
f.write(chrome_trace)
result = tf.matmul(matrix1, matrix2)
return result
if __name__ == "__main__":
batch_run = 4
image_size = 32
count = 0
results = []
##define the graph
while count < batch_run:
results.append(matrix_mul(image_size))
count = count + 1
# build option for perf
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
# build tensorboard
tf_writer = tf.summary.FileWriter("./tensorboard_multi_ops_group", graph=tf.get_default_graph())
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
init = tf.global_variables_initializer()
sess.run(init)
start = time.time()
final_result = sess.run(tf.group(*results), options = options, run_metadata = run_metadata)
end = time.time()
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open('timeline_02_step_%d.json', 'w') as f:
f.write(chrome_trace)
print("benchmark time: {}".format(end - start))
def run_op_benchmark(self,
sess,
op_or_tensor,
feed_dict=None,
burn_iters=2,
min_iters=10,
store_trace=False,
store_memory_usage=True,
name=None,
extras=None,
mbs=0):
"""Run an op or tensor in the given session. Report the results.
Args:
sess: `Session` object to use for timing.
op_or_tensor: `Operation` or `Tensor` to benchmark.
feed_dict: A `dict` of values to feed for each op iteration (see the
`feed_dict` parameter of `Session.run`).
burn_iters: Number of burn-in iterations to run.
min_iters: Minimum number of iterations to use for timing.
store_trace: Boolean, whether to run an extra untimed iteration and
store the trace of iteration in returned extras.
The trace will be stored as a string in Google Chrome trace format
in the extras field "full_trace_chrome_format". Note that trace
will not be stored in test_log_pb2.TestResults proto.
store_memory_usage: Boolean, whether to run an extra untimed iteration,
calculate memory usage, and store that in extras fields.
name: (optional) Override the BenchmarkEntry name with `name`.
Otherwise it is inferred from the top-level method name.
extras: (optional) Dict mapping string keys to additional benchmark info.
Values may be either floats or values that are convertible to strings.
mbs: (optional) The number of megabytes moved by this op, used to
calculate the ops throughput.
Returns:
A `dict` containing the key-value pairs that were passed to
`report_benchmark`. If `store_trace` option is used, then
`full_chrome_trace_format` will be included in return dictionary even
though it is not passed to `report_benchmark` with `extras`.
"""
for _ in range(burn_iters):
sess.run(op_or_tensor, feed_dict=feed_dict)
deltas = [None] * min_iters
for i in range(min_iters):
start_time = time.time()
sess.run(op_or_tensor, feed_dict=feed_dict)
end_time = time.time()
delta = end_time - start_time
deltas[i] = delta
extras = extras if extras is not None else {}
unreported_extras = {}
if store_trace or store_memory_usage:
run_options = config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE)
run_metadata = config_pb2.RunMetadata()
sess.run(op_or_tensor, feed_dict=feed_dict,
options=run_options, run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
if store_trace:
unreported_extras["full_trace_chrome_format"] = (
tl.generate_chrome_trace_format())
if store_memory_usage:
step_stats_analysis = tl.analyze_step_stats(show_memory=True)
allocator_maximums = step_stats_analysis.allocator_maximums
for k, v in allocator_maximums.items():
extras["allocator_maximum_num_bytes_%s" % k] = v.num_bytes
def _median(x):
if not x:
return -1
s = sorted(x)
l = len(x)
lm1 = l - 1
return (s[l//2] + s[lm1//2]) / 2.0
def _mean_and_stdev(x):
if not x:
return -1, -1
l = len(x)
mean = sum(x) / l
if l == 1:
return mean, -1
variance = sum([(e - mean) * (e - mean) for e in x]) / (l - 1)
return mean, math.sqrt(variance)
median_delta = _median(deltas)
benchmark_values = {
"iters": min_iters,
"wall_time": median_delta,
"extras": extras,
"name": name,
"throughput": mbs / median_delta
}
self.report_benchmark(**benchmark_values)
mean_delta, stdev_delta = _mean_and_stdev(deltas)
unreported_extras["wall_time_mean"] = mean_delta
unreported_extras["wall_time_stdev"] = stdev_delta
benchmark_values["extras"].update(unreported_extras)
return benchmark_values
def prediction_callback(self, input_msg):
tic = timeit.default_timer()
print("subscribed to prediction input")
tic0 = timeit.default_timer()
feed_dict = {
self.car1: multiarray_to_numpy(input_msg.car1),
self.car2: multiarray_to_numpy(input_msg.car2),
self.extras: multiarray_to_numpy(input_msg.extras),
self.traj_lengths: multiarray_to_numpy(input_msg.traj_lengths),
self.sample_ct: [input_msg.sample_ct]
}
if input_msg.car1_future.data:
feed_dict[self.car1_future] = multiarray_to_numpy(
input_msg.car1_future)
else:
feed_dict[self.car1_future_x] = multiarray_to_numpy(
input_msg.car1_future_x)
feed_dict[self.car1_future_y] = multiarray_to_numpy(
input_msg.car1_future_y)
toc0 = timeit.default_timer()
print("constructing feed_dict took: ", toc0 - tic0,
" (s), running tf!")
tic0 = timeit.default_timer()
if profile:
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
c1top32 = self.sess.run(self.c1top32,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(
'/home/schmrlng/Dropbox/timeline' +
os.environ["CUDA_VISIBLE_DEVICES"] + '_0.json', 'w') as f:
f.write(chrome_trace)
feed_dict[self.car1_future] = c1top32
feed_dict[self.sample_ct] = [input_msg.sample_ct * 64]
feed_dict.pop(self.car1_future_x) # should be unnecessary
feed_dict.pop(self.car1_future_y) # should be unnecessary
run_metadata = tf.RunMetadata()
c1best, r = self.sess.run([self.c1best, self.r],
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(
'/home/schmrlng/Dropbox/timeline' +
os.environ["CUDA_VISIBLE_DEVICES"] + '_1.json', 'w') as f:
f.write(chrome_trace)
else:
c1top32 = self.sess.run(self.c1top32, feed_dict=feed_dict)
feed_dict[self.car1_future] = c1top32
feed_dict[self.sample_ct] = [input_msg.sample_ct * 64]
feed_dict.pop(self.car1_future_x) # should be unnecessary
feed_dict.pop(self.car1_future_y) # should be unnecessary
c1best, r = self.sess.run([self.c1best, self.r],
feed_dict=feed_dict)
toc0 = timeit.default_timer()
print("done running tf!, took (s): ", toc0 - tic0)
tic0 = timeit.default_timer()
output_msg = prediction_output()
output_msg.y = numpy_to_multiarray(c1best)
output_msg.r = numpy_to_multiarray(r)
self.pub.publish(output_msg)
toc0 = timeit.default_timer()
toc = timeit.default_timer()
print("output_msg constructed and published, took (s): ", toc0 - tic0)
print("total time taken (s): ", toc - tic)
def main_LSTM(args):
'''
implementation of the original LSTM approach (https://github.com/KhaledSaleh/driving_behaviour_classification)
'''
# set config params specific for the original code
config = Config()
config.training_volume = args.training_volume
config.input_dim = args.input_dim
config.encoding_dim = args.encoding_dim
config.scale = args.scale
if args.runtime_measurement:
config.n_time_measures = 10
else:
config.n_time_measures = 1
# load preprocessed data
data = load_dataset(args.dataset,config)
X_train = data[0]
X_test = data[1]
y_train = data[2]
y_test = data[3]
config = data[4]
logs = []
# if train test data not a list, create one
if type(X_train)==list:
print("given data is not a list")
X_train_list = X_train
X_test_list = X_test
y_train_list = y_train
y_test_list = y_test
else:
X_train_list =[X_train]
X_test_list = [X_test]
y_train_list = [y_train]
y_test_list = [y_test]
#######################################################################################
# statistical iteration
#######################################################################################
acc_mean = []
f1_mean = []
for stat_it in range(args.stat_iterations):
logger.info('Statistial iteration: ' + str(stat_it))
# train for each element in list (that is why we need list form, even if it contains only one element)
logger.info('Training data contains ' + str(len(X_train_list)) + ' training instances...')
scores = []
accs = []
for it in range(len(X_train_list)):
logger.info(('.......'))
logger.info('instance ' + str(it) + ':')
X_train = X_train_list[it]
X_test = X_test_list[it]
y_train = y_train_list[it]
y_test = y_test_list[it]
# use only fraction of training samples (if given)
X_train = X_train[1:int(X_train.shape[0] * config.training_volume), :]
y_train = y_train[1:int(y_train.shape[0] * config.training_volume), :]
config.n_inputs = X_train.shape[2]
config.train_count = len(X_train)
config.test_data_count = len(X_test)
config.n_steps = len(X_train[0])
config.n_classes = len(np.unique(y_train))
logger.info('Training dataset shape: ' + str(X_train.shape) + str(y_train.shape))
logger.info('Test dataset shape: ' + str(X_test.shape) + str(y_test.shape))
graph = tf.Graph()
with graph.as_default():
X = tf.compat.v1.placeholder(tf.float32, [None, config.n_steps, config.n_inputs], name="X")
Y = tf.compat.v1.placeholder(tf.float32, [None, config.n_classes], name="Y")
pred_Y = LSTM_Network(X, config)
# Loss,optimizer,evaluation
l2 = config.lambda_loss_amount * \
sum(tf.nn.l2_loss(tf_var) for tf_var in tf.compat.v1.trainable_variables())
# Softmax loss and L2
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=pred_Y, labels=Y), name="cost") + l2
optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=config.learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred_Y, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, dtype=tf.float32))
saver = tf.compat.v1.train.Saver()
with tf.compat.v1.Session(graph=graph, config=tf.compat.v1.ConfigProto(log_device_placement=False)) as sess:
if not args.test:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
best_accuracy = 0.0
# Start training for each batch and loop epochs
for i in range(config.training_epochs):
starttime = time()
for start, end in zip(range(0, config.train_count, config.batch_size),
range(config.batch_size, config.train_count + 1, config.batch_size)):
sess.run(optimizer, feed_dict={X: X_train[start:end],
Y: one_hot(y_train[start:end],config.n_classes)})
saver.save(sess, os.path.join("./weights", 'LSTM_model'))
# Test completely at every epoch: calculate accuracy
pred_out, accuracy_out, loss_out = sess.run([pred_Y, accuracy, cost], feed_dict={
X: X_test, Y: one_hot(y_test, config.n_classes)})
logs.append(time() - starttime)
print("Training iter: {},".format(i) + \
" Test accuracy : {},".format(accuracy_out) + \
" Loss : {}".format(loss_out))
best_accuracy = max(best_accuracy, accuracy_out)
print("")
mean_epoch_time = np.mean(logs)
overall_time = np.sum(logs)
logger.info("Mean Epoch time: " + str(mean_epoch_time))
logger.info("overall training time: " + str(overall_time))
logger.info("Final test accuracy: {}".format(accuracy_out))
logger.info("Best epoch's test accuracy: {}".format(best_accuracy))
print("")
# start testing the trained model
else:
saver.restore(sess, os.path.join("./weights", 'LSTM_model'))
t1 = time()
pred_out, accuracy_out, loss_out = sess.run([pred_Y, accuracy, cost], feed_dict={
X: X_test, Y: one_hot(y_test,config.n_classes)})
inference_time = time() - t1
print(" Test accuracy : {},".format(accuracy_out) + \
" Loss : {}".format(loss_out))
#############################################################################################
# evaluation of results
#############################################################################################
pred_test_bool = pred_out.argmax(1)
# runtime measurement
t=[]
traces = []
options = tf.compat.v1.RunOptions(trace_level=tf.compat.v1.RunOptions.FULL_TRACE)
run_metadata = tf.compat.v1.RunMetadata()
for i in range(config.n_time_measures):
with tf.compat.v1.Session(graph=graph, config=tf.compat.v1.ConfigProto(log_device_placement=False)) as Sess:
init_op = tf.compat.v1.global_variables_initializer()
Sess.run(init_op)
t1 = time()
Sess.run([pred_Y, accuracy, cost], feed_dict={
X: X_test, Y: one_hot(y_test, config.n_classes)}, options=options, run_metadata=run_metadata)
inference_time = time() - t1
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
traces.append(chrome_trace)
t.append(inference_time)
with open('./logs/LSTM_ts_preproc_timeline_test.json', 'w') as f:
f.write(traces[-1])
inference_time = np.median(inference_time)
logger.info("Inference time: " + str(inference_time))
logger.info("Inference time of one sequence [ms]: " + str(inference_time*1000/X_test.shape[0]))
logger.info('Accuracy on training data: ')
report = classification_report(y_test.astype(int), pred_test_bool, output_dict=True)
logger.info(classification_report(y_test.astype(int), pred_test_bool))
accs.append((report['accuracy']))
logger.info("Confusion matrix:")
confusion_matrix = metrics.confusion_matrix(y_test.astype(int), pred_test_bool)
logger.info(confusion_matrix)
# f1 score
f1 = f1_score(y_test.astype(int), pred_test_bool, average='weighted')
scores.append(f1)
logger.info("F1 Score: " + str(f1))
# add results to statistical result array
acc_mean.append(np.mean(accs))
f1_mean.append(np.mean(scores))
# save as mat files
save_dic = {"report": report, "confusion_matrix": confusion_matrix, "config": config, "pred": pred_out,
"label": y_test, "f1": np.mean(f1_mean), "acc_mean": np.mean(acc_mean)}
savemat("results/" + args.dataset + "/results_origNet_" + str(config.training_volume) + ".mat", save_dic)
logger.info('Accuracy results of statistical repetitions: ' + str(acc_mean))
logger.info('F1 scores of statistical repetitions: ' + str(f1_mean))
# write all scores to extra file
logger.info('Mean Score: ' + str(np.mean(f1_mean)))
logger.info('Mean Accuracy: ' + str(np.mean(acc_mean)))
with open("results/results_" + args.dataset + "_LSTM.txt", 'a') as file:
file.write(str(args.stat_iterations) + '\t'
+ str(round(np.mean(f1_mean), 3)) + '\t'
+ str(round(np.mean(acc_mean), 3)) + '\t'
+ str(round(np.std(f1_mean), 3)) + '\t'
+ str(round(np.std(acc_mean), 3)) + '\t'
+ str(args.training_volume) + '\n'
)
threads = [
threading.Thread(group=None, target=run_op, args=(op, ))
for op in (enqueue_zeros, enqueue_ones)
]
if reverse:
threads.reverse()
for t in threads:
t.start()
# wait for threads to finish
for t in threads:
t.join()
# generate merged timeline
merged_metadata = tf.RunMetadata()
for run_metadata in run_metadatas:
merged_metadata.MergeFrom(run_metadata)
tl = timeline.Timeline(merged_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
with open(sys.argv[0] + '_%s_timeline.json' % (reverse), 'w') as f:
f.write(ctf)
assert sess.run(queue.size()) == 2 * n
result = sess.run(queue.dequeue_many(2 * n))
padding = np.array([0])
diffs = np.concatenate([padding, result]) - np.concatenate([result, padding])
print("Interleaving detected: %s" % (abs(diffs).sum() > 2))
def train_model(self, sess, max_iters):
"""Network training loop."""
# 返回一个RoIDataLayer类对象，内容self._roidb ,self._num_classes ,self._perm,self._cur
data_layer = get_data_layer(self.roidb, self.imdb.num_classes)
# RPN
# classification loss
# 将'rpn_cls_score_reshape'层的输出（1,n，n，18）reshape为（-1,2）,其中2为前景与背景的多分类得分（）
rpn_cls_score = tf.reshape(
self.net.get_output('rpn_cls_score_reshape'), [-1, 2])
# 'rpn-data'层输出的[0]为rpn_label,shape为(1, 1, A * height, width)，中存的是所有anchor的label（-1,0,1）
# 问题1：目前感觉有异议，数据读取方向labels有问题################################
rpn_label = tf.reshape(self.net.get_output('rpn-data')[0], [-1])
# 把rpn_label不等于-1对应引索的rpn_cls_score取出，重新组合成rpn_cls_score
rpn_cls_score = tf.reshape(
tf.gather(rpn_cls_score, tf.where(tf.not_equal(rpn_label, -1))),
[-1, 2])
# 把rpn_label不等于-1对应引索的rpn_label取出，重新组合成rpn_label
rpn_label = tf.reshape(
tf.gather(rpn_label, tf.where(tf.not_equal(rpn_label, -1))), [-1])
# score损失：tf.nn.sparse_softmax_cross_entropy_with_logits函数的两个参数logits，labels数目相同（shape[0]相同），分别为最后一层的输出与标签
# NOTE：这个函数返回的是一个向量，要求交叉熵就tf.reduce_sum，要求损失就tf.reduce_mean
# 问题2：logits，labels应该shape相同的，但这里不同，有异议
rpn_cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_label))
# bounding box regression L1 loss
# 'rpn_bbox_pred'层为了回归bbox,存的是（dx,dy,dw,dh）
rpn_bbox_pred = self.net.get_output('rpn_bbox_pred')
# 'rpn-data'[1]返回一个用于anchor回归成target的包含每个anchor回归值(dx、dy、dw、dh)的array,形状（(len(inds_inside), 4），即（anchors.shape[0],4）
# 重新reshape成(1, height, width, A * 4)
rpn_bbox_targets = tf.transpose(
self.net.get_output('rpn-data')[1], [0, 2, 3, 1])
# rpn_bbox_inside_weights：标签为1的anchor,对应(1.0, 1.0, 1.0, 1.0)
# 重新reshape成(1, height, width, A * 4)
rpn_bbox_inside_weights = tf.transpose(
self.net.get_output('rpn-data')[2], [0, 2, 3, 1])
# rpn_bbox_outside_weights:标签为0或者1的，权重初始化都为（1/num_examples，1/num_examples，1/num_examples，1/num_examples），num_examples为标签为0或者1的anchor总数
# 重新reshape成(1, height, width, A * 4)
rpn_bbox_outside_weights = tf.transpose(
self.net.get_output('rpn-data')[3], [0, 2, 3, 1])
# 计算smooth_l1损失
rpn_smooth_l1 = self._modified_smooth_l1(3.0, rpn_bbox_pred,
rpn_bbox_targets,
rpn_bbox_inside_weights,
rpn_bbox_outside_weights)
# rpn_smooth_l1计算出的为一个向量，现在要合成loss形式
rpn_loss_box = tf.reduce_mean(
tf.reduce_sum(rpn_smooth_l1, reduction_indices=[1, 2, 3]))
# R-CNN
# classification loss
# 得到最后一个score分支fc层的输出
cls_score = self.net.get_output('cls_score')
# label：筛选出的proposal与GT结合形成all_roi,从all_roi中筛选出符合的roi，得到这些roi的label
label = tf.reshape(self.net.get_output('roi-data')[1], [-1])
# 用这些roi的label与最后一个score分支fc层的输出相比较，得到loss
cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score,
labels=label))
# bounding box regression L1 loss
# 得到最后一个bbox分支fc层的输出
bbox_pred = self.net.get_output('bbox_pred')
bbox_targets = self.net.get_output('roi-data')[2]
bbox_inside_weights = self.net.get_output('roi-data')[3]
bbox_outside_weights = self.net.get_output('roi-data')[4]
# 计算smooth_l1损失
smooth_l1 = self._modified_smooth_l1(1.0, bbox_pred, bbox_targets,
bbox_inside_weights,
bbox_outside_weights)
# smooth_l1计算出的为一个向量，现在要合成loss形式
loss_box = tf.reduce_mean(
tf.reduce_sum(smooth_l1, reduction_indices=[1]))
# final loss 计算总损失
loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box
# optimizer and learning rate
global_step = tf.Variable(0, trainable=False)
# cfg.TRAIN.LEARNING_RATE为0.001, cfg.TRAIN.STEPSIZE为50000
# tf.train.exponential_decay（初始lr，初始步数，多少步进入下一平台值，总步数，下一次平台值是多少（基于上次的比率），staircase）
# staircase为True则遵循刚才规则，如为False则每一次迭代更新一次
lr = tf.train.exponential_decay(cfg.TRAIN.LEARNING_RATE,
global_step,
cfg.TRAIN.STEPSIZE,
0.1,
staircase=True)
# cfg.TRAIN.MOMENTUM 为 0.9
momentum = cfg.TRAIN.MOMENTUM
# 动态系数为0.9的梯度下降法
train_op = tf.train.MomentumOptimizer(lr, momentum).minimize(
loss, global_step=global_step)
# iintialize variables
sess.run(tf.global_variables_initializer())
if self.pretrained_model is not None: #如果有预训练模型，则加载
print('Loading pretrained model weights from {:s}'.format(
self.pretrained_model))
self.net.load(self.pretrained_model, sess, self.saver, True)
last_snapshot_iter = -1
timer = Timer() #记录当前时间
for iter in range(max_iters):
# get one batch
blobs = data_layer.forward() #得到一个batch信息
# Make one SGD update
feed_dict={self.net.data: blobs['data'], self.net.im_info: blobs['im_info'], self.net.keep_prob: 0.5, \
self.net.gt_boxes: blobs['gt_boxes']} #给定placehold信息
run_options = None
run_metadata = None
# False
if cfg.TRAIN.DEBUG_TIMELINE:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
timer.tic()
rpn_loss_cls_value, rpn_loss_box_value, loss_cls_value, loss_box_value, _ = sess.run(
[
rpn_cross_entropy, rpn_loss_box, cross_entropy, loss_box,
train_op
],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
timer.toc()
if cfg.TRAIN.DEBUG_TIMELINE:
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file = open(
str(long(time.time() * 1000)) + '-train-timeline.ctf.json',
'w')
trace_file.write(
trace.generate_chrome_trace_format(show_memory=False))
trace_file.close()
if (iter + 1) % (cfg.TRAIN.DISPLAY) == 0:
print('iter: %d / %d, total loss: %.4f, rpn_loss_cls: %.4f, rpn_loss_box: %.4f, loss_cls: %.4f, loss_box: %.4f, lr: %f'%\
(iter+1, max_iters, rpn_loss_cls_value + rpn_loss_box_value + loss_cls_value + loss_box_value ,rpn_loss_cls_value, rpn_loss_box_value,loss_cls_value, loss_box_value, lr.eval())) # clw modify: for py3
print('speed: {:.3f}s / iter'.format(
timer.average_time)) # clw modify: for py3
if (iter + 1) % cfg.TRAIN.SNAPSHOT_ITERS == 0:
last_snapshot_iter = iter
self.snapshot(sess, iter)
if last_snapshot_iter != iter:
self.snapshot(sess, iter)
def train_loop(sess, train_step, global_step, optlist, args, trainset,
validationset, disable_training, enable_tf_timeline):
train_loop_logger = logger(int(args["task_index"]), "Train Loop")
train_loop_logger.start_timer()
options = None
run_metadata = None
many_runs_timeline = None
if enable_tf_timeline:
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
many_runs_timeline = timeliner()
#counter stuff
trainset.reset()
validationset.reset()
#restore weights belonging to graph
epochs_completed = 0
if not args['restart']:
last_model = tf.train.latest_checkpoint(args['modelpath'])
print("Restoring model %s.", last_model)
model_saver.restore(sess, last_model)
#losses
train_loss = 0.
train_batches = 0
total_batches = 0
train_time = 0
#do training
while not sess.should_stop():
train_iteration_logger = logger(int(args['task_index']),
"Training Iteration", epochs_completed)
train_iteration_logger.start_timer()
#increment total batch counter
total_batches += 1
#get next batch
images, labels, normweights, _, _ = trainset.next_batch(
args['train_batch_size_per_node'])
#set weights to zero
normweights[:] = 1.
#set up feed dict:
feed_dict = {
variables['images_']: images,
variables['labels_']: labels,
variables['weights_']: normweights,
variables['keep_prob_']: args['dropout_p']
}
if not disable_training:
#update weights
start_time = time.time()
if args['create_summary']:
_, gstep, summary, tmp_loss = sess.run(
[train_step, global_step, train_summary, loss_fn],
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
if enable_tf_timeline:
fetched_timeline = timeline.Timeline(
run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
)
many_runs_timeline.update_timeline(chrome_trace)
else:
_, gstep, tmp_loss = sess.run(
[train_step, global_step, loss_fn],
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
if enable_tf_timeline:
fetched_timeline = timeline.Timeline(
run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
)
many_runs_timeline.update_timeline(chrome_trace)
#update kfac parameters
if optlist:
sess.run(optlist[0],
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
if enable_tf_timeline:
fetched_timeline = timeline.Timeline(
run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
)
many_runs_timeline.update_timeline(chrome_trace)
if gstep % args["kfac_inv_update_frequency"] == 0:
sess.run(optlist[1],
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
if enable_tf_timeline:
fetched_timeline = timeline.Timeline(
run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
)
many_runs_timeline.update_timeline(chrome_trace)
end_time = time.time()
train_time += end_time - start_time
#increment train loss and batch number
train_loss += tmp_loss
train_batches += 1
#determine if we give a short update:
if gstep % args['display_interval'] == 0:
print(
time.time(), "REPORT rank", args["task_index"],
"global step %d., average training loss %g (%.3f sec/batch)" %
(gstep, train_loss / float(train_batches),
train_time / float(train_batches)))
#check if epoch is done
if trainset._epochs_completed > epochs_completed:
epochs_completed = trainset._epochs_completed
print(
time.time(), "COMPLETED rank", args["task_index"],
"epoch %d, average training loss %g (%.3f sec/batch)" %
(epochs_completed, train_loss / float(train_batches),
train_time / float(train_batches)))
#reset counters
train_loss = 0.
train_batches = 0
train_time = 0
#compute validation loss:
#reset variables
validation_loss = 0.
validation_batches = 0
#iterate over batches
while True:
#get next batch
images, labels, normweights, weights, _ = validationset.next_batch(
args['validation_batch_size_per_node'])
#set weights to 1:
normweights[:] = 1.
weights[:] = 1.
if not disable_training:
#compute loss
if args['create_summary']:
summary, tmp_loss = sess.run(
[validation_summary, loss_fn],
feed_dict={
variables['images_']: images,
variables['labels_']: labels,
variables['weights_']: normweights,
variables['keep_prob_']: 1.0
})
else:
tmp_loss = sess.run(
[loss_fn],
feed_dict={
variables['images_']: images,
variables['labels_']: labels,
variables['weights_']: normweights,
variables['keep_prob_']: 1.0
})
#add loss
validation_loss += tmp_loss[0]
validation_batches += 1
#update accuracy
sess.run(accuracy_fn[1],
feed_dict={
variables['images_']: images,
variables['labels_']: labels,
variables['weights_']: normweights,
variables['keep_prob_']: 1.0
})
#update auc
sess.run(auc_fn[1],
feed_dict={
variables['images_']: images,
variables['labels_']: labels,
variables['weights_']: normweights,
variables['keep_prob_']: 1.0
})
#check if full pass done
if validationset._epochs_completed > 0:
validationset.reset()
break
print(
time.time(), "COMPLETED epoch %d, average validation loss %g" %
(epochs_completed,
validation_loss / float(validation_batches)))
validation_accuracy = sess.run(accuracy_fn[0])
print(
time.time(), "COMPLETED epoch %d, average validation accu %g" %
(epochs_completed, validation_accuracy))
validation_auc = sess.run(auc_fn[0])
print(
time.time(), "COMPLETED epoch %d, average validation auc %g" %
(epochs_completed, validation_auc))
if enable_tf_timeline:
many_runs_timeline.save('Timeliner_output.json')
train_iteration_logger.end_timer()
if enable_tf_timeline:
many_runs_timeline.save('Timeliner_output.json')
train_loop_logger.end_timer()
def benchmark_model(self, warmup_runs, bm_runs, num_threads,
trace_filename=None):
"""Benchmark model."""
if self.tensorrt:
print('Using tensorrt ', self.tensorrt)
self.build_and_save_model()
graphdef = self.freeze_model()
if num_threads > 0:
print('num_threads for benchmarking: {}'.format(num_threads))
sess_config = tf.ConfigProto(
intra_op_parallelism_threads=num_threads,
inter_op_parallelism_threads=1)
else:
sess_config = tf.ConfigProto()
# rewriter_config_pb2.RewriterConfig.OFF
sess_config.graph_options.rewrite_options.dependency_optimization = 2
if self.use_xla:
sess_config.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_2)
with tf.Graph().as_default(), tf.Session(config=sess_config) as sess:
inputs = tf.placeholder(tf.float32, name='input', shape=self.inputs_shape)
output = self.build_model(inputs, is_training=False)
img = np.random.uniform(size=self.inputs_shape)
sess.run(tf.global_variables_initializer())
if self.tensorrt:
fetches = [inputs.name] + [i.name for i in output]
goutput = self.convert_tr(graphdef, fetches)
inputs, output = goutput[0], goutput[1:]
if not self.use_xla:
# Don't use tf.group because XLA removes the whole graph for tf.group.
output = tf.group(*output)
for i in range(warmup_runs):
start_time = time.time()
sess.run(output, feed_dict={inputs: img})
print('Warm up: {} {:.4f}s'.format(i, time.time() - start_time))
print('Start benchmark runs total={}'.format(bm_runs))
timev = []
for i in range(bm_runs):
if trace_filename and i == (bm_runs // 2):
run_options = tf.RunOptions()
run_options.trace_level = tf.RunOptions.FULL_TRACE
run_metadata = tf.RunMetadata()
sess.run(output, feed_dict={inputs: img},
options=run_options, run_metadata=run_metadata)
tf.logging.info('Dumping trace to %s' % trace_filename)
trace_dir = os.path.dirname(trace_filename)
if not tf.io.gfile.exists(trace_dir):
tf.io.gfile.makedirs(trace_dir)
with tf.io.gfile.GFile(trace_filename, 'w') as trace_file:
from tensorflow.python.client import timeline # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file.write(
trace.generate_chrome_trace_format(show_memory=True))
start_time = time.time()
sess.run(output, feed_dict={inputs: img})
timev.append(time.time() - start_time)
timev.sort()
timev = timev[2:bm_runs-2]
print('{} {}runs {}threads: mean {:.4f} std {:.4f} min {:.4f} max {:.4f}'
.format(self.model_name, len(timev), num_threads, np.mean(timev),
np.std(timev), np.min(timev), np.max(timev)))
def run(self, *args_, **kwargs_):
if self._end_trace:
ret = self.sess.run(*args_, **kwargs_)
elif not self._end_trace and self.step_cnt < self.start_step:
ret = self.sess.run(*args_, **kwargs_)
self.step_cnt += 1
elif not self._end_trace and self.step_cnt < self.end_step:
ret = self.sess.run(*args_,
options=self.run_options,
run_metadata=self.run_metadata,
**kwargs_)
# Create the Timeline object, and write it to a json
tl = timeline.Timeline(self.run_metadata.step_stats)
ctf = json.loads(tl.generate_chrome_trace_format())
self.traces["traceEvents"] += ctf["traceEvents"]
print("Add the {}th step of traces".format(self.step_cnt))
self.step_cnt += 1
### Create the DAG
if self.dag is None:
self.dag = nx.DiGraph()
for trace in ctf["traceEvents"]:
if trace["ph"] == "M" or "args" not in trace:
continue
op = trace["args"]["op"]
name = trace["args"]["name"]
### Add nodes to the DAG
if name not in self.dag.nodes:
self.dag.add_node(name)
### Add dependency info
for k, v in trace["args"].items():
if "input" in k:
self.dag.add_edge(v, name)
try:
not_found = False
nx.find_cycle(self.dag.cycle)
except:
not_found = True
assert not_found
def flatten_fetch_list(fetch_list):
if not isinstance(fetch_list, (list, tuple)):
return [fetch_list]
else:
result_list = []
for op in fetch_list:
result_list += flatten_fetch_list(op)
return result_list
### Output traces
if self.step_cnt == self.end_step:
fd = kwargs_.get("feed_dict")
tensor_names, tensor_shape_ops = self.tensor_shape_ops
out_shapes = self.sess.run(tensor_shape_ops, feed_dict=fd)
self.tensor_shapes = {}
for name, shape in zip(tensor_names, out_shapes):
self.tensor_shapes[name] = [int(s) for s in list(shape)]
# collect feed dict meta
self.fetches = [
tensor.name for tensor in flatten_fetch_list(args_[0])
]
for key, tensor in fd.items():
shape_as_list = [int(dim) for dim in tensor.shape]
dtype_as_str = (str(tensor.dtype).split("\'")[1]
if "\'" in str(tensor.dtype) else str(
tensor.dtype)).split("_ref")[0]
self.feed_dict_meta[key.op.name] = {
"shape": shape_as_list,
"dtype": dtype_as_str
}
self._end_trace = True
self.output_traces()
### Return all fetches
return ret
def main():
args = get_arguments()
data_dir = 'midi-Corpus/' + args.data_set + '/'
logdir = data_dir + 'max_dilation=%d_reps=%d/' % (args.max_dilation_pow,
args.expansion_reps)
print('*************************************************')
print(logdir)
print('*************************************************')
sys.stdout.flush()
restore_from = logdir
if not os.path.exists(logdir):
os.makedirs(logdir)
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
wavenet_params = loadParams(args.max_dilation_pow, args.expansion_reps,
args.dil_chan, args.res_chan, args.skip_chan)
with open(logdir + 'wavenet_params.json', 'w') as outfile:
json.dump(wavenet_params, outfile)
# Create coordinator.
coord = tf.train.Coordinator()
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
gc_enabled = False
# data queue for the training set
train_dir = data_dir + 'train/'
train_reader = MidiReader(
train_dir,
coord,
sample_rate=wavenet_params['sample_rate'],
gc_enabled=gc_enabled,
receptive_field=WaveNetModel.calculate_receptive_field(
wavenet_params["filter_width"], wavenet_params["dilations"],
wavenet_params["scalar_input"],
wavenet_params["initial_filter_width"]),
sample_size=args.sample_size)
train_batch = train_reader.dequeue(args.batch_size)
if gc_enabled:
gc_id_batch = reader.dequeue_gc(args.batch_size)
else:
gc_id_batch = None
# Create network.
net = WaveNetModel(
batch_size=BATCH_SIZE,
dilations=wavenet_params["dilations"],
filter_width=wavenet_params["filter_width"],
residual_channels=wavenet_params["residual_channels"],
dilation_channels=wavenet_params["dilation_channels"],
skip_channels=wavenet_params["skip_channels"],
use_biases=wavenet_params["use_biases"],
scalar_input=wavenet_params["scalar_input"],
initial_filter_width=wavenet_params["initial_filter_width"],
histograms=False,
global_condition_channels=None,
global_condition_cardinality=train_reader.gc_category_cardinality)
if args.l2_regularization_strength == 0:
args.l2_regularization_strength = None
print('constructing training loss')
sys.stdout.flush()
train_loss, target_output, prediction = net.loss(
input_batch=train_batch,
global_condition_batch=gc_id_batch,
l2_regularization_strength=args.l2_regularization_strength)
print('constructing validation loss')
sys.stdout.flush()
print('making optimizer')
sys.stdout.flush()
optimizer = optimizer_factory['adam'](learning_rate=args.learning_rate,
momentum=args.momentum)
trainable = tf.trainable_variables()
optim = optimizer.minimize(train_loss, var_list=trainable)
print('setting up tensorboard')
sys.stdout.flush()
# Set up logging for TensorBoard.
writer = tf.summary.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
run_metadata = tf.RunMetadata()
summaries = tf.summary.merge_all()
valid_input = tf.placeholder(dtype=tf.float32, shape=(1, None, 88))
valid_loss, valid_target_output, valid_prediction = net.loss(
input_batch=valid_input,
global_condition_batch=gc_id_batch,
l2_regularization_strength=args.l2_regularization_strength)
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
init = tf.global_variables_initializer()
sess.run(init)
print('saver')
sys.stdout.flush()
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=5)
try:
saved_global_step = load(saver, sess, restore_from)
if is_overwritten_training or saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
except:
print("Something went wrong while restoring checkpoint. "
"We will terminate training to avoid accidentally overwriting "
"the previous model.")
raise
print('thread stuff')
sys.stdout.flush()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
train_reader.start_threads(sess)
step = None
last_saved_step = saved_global_step
# load validation data
validation_audio = load_all_audio(data_dir + 'valid/')
num_valid_files = len(validation_audio)
valid_loss_values = np.zeros((int(np.ceil(args.num_steps / 50)), ))
vl_ind = 0
print('optimization time')
sys.stdout.flush()
min_valid_loss = 1e10
try:
for step in range(saved_global_step + 1, args.num_steps):
start_time = time.time()
if args.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
summary, loss_value, _ = sess.run(
[summaries, train_loss, optim],
options=run_options,
run_metadata=run_metadata)
writer.add_summary(summary, step)
writer.add_run_metadata(run_metadata,
'step_{:04d}'.format(step))
valid_losses_step = np.zeros((num_valid_files, ))
for i in range(num_valid_files):
audio_i = np.expand_dims(validation_audio[i], 0)
valid_losses_step[i] = sess.run(valid_loss,
{valid_input: audio_i})
valid_loss_value_step = np.mean(valid_losses_step)
valid_loss_values[vl_ind] = valid_loss_value_step
np.savez(logdir + 'validation.npz',
validation_loss=valid_loss_values)
vl_ind += 1
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
if (valid_loss_value_step < min_valid_loss):
min_valid_loss = valid_loss_value_step
save(saver, sess, logdir, step)
last_saved_step = step
else:
summary, loss_value, _ = sess.run(
[summaries, train_loss, optim])
writer.add_summary(summary, step)
duration = time.time() - start_time
print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
sys.stdout.flush()
except KeyboardInterrupt:
# Introduce a line break after ^C is displayed so save message
# is on its own line.
print()
finally:
if step > last_saved_step:
save(saver, sess, logdir, step)
coord.request_stop()
coord.join(threads)
def train(self, data, valid_data):
stop_batch = self.stop_batch
if self.run_opt.is_distrib:
self._init_sess_distrib()
else:
self._init_sess_serial()
self.print_head()
fp = None
if self.run_opt.is_chief:
fp = open(self.disp_file, "a")
cur_batch = self.sess.run(self.global_step)
is_first_step = True
self.cur_batch = cur_batch
self.run_opt.message(
"start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e"
% (self.sess.run(self.learning_rate), self.lr.value(cur_batch),
self.lr.decay_steps_, self.lr.decay_rate_,
self.lr.value(stop_batch)))
prf_options = None
prf_run_metadata = None
if self.profiling:
prf_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
prf_run_metadata = tf.RunMetadata()
train_time = 0
while cur_batch < stop_batch:
batch_data = data.get_batch(sys_probs=self.sys_probs,
auto_prob_style=self.auto_prob_style)
feed_dict_batch = {}
for kk in batch_data.keys():
if kk == 'find_type' or kk == 'type':
continue
if 'find_' in kk:
feed_dict_batch[self.place_holders[kk]] = batch_data[kk]
else:
feed_dict_batch[self.place_holders[kk]] = np.reshape(
batch_data[kk], [-1])
for ii in ['type']:
feed_dict_batch[self.place_holders[ii]] = np.reshape(
batch_data[ii], [-1])
for ii in ['natoms_vec', 'default_mesh']:
feed_dict_batch[self.place_holders[ii]] = batch_data[ii]
feed_dict_batch[self.place_holders['is_training']] = True
if self.display_in_training and is_first_step:
self.test_on_the_fly(fp, valid_data, feed_dict_batch)
is_first_step = False
if self.timing_in_training: tic = time.time()
self.sess.run([self.train_op],
feed_dict=feed_dict_batch,
options=prf_options,
run_metadata=prf_run_metadata)
if self.timing_in_training: toc = time.time()
if self.timing_in_training: train_time += toc - tic
cur_batch = self.sess.run(self.global_step)
self.cur_batch = cur_batch
if self.display_in_training and (cur_batch % self.disp_freq == 0):
tic = time.time()
self.test_on_the_fly(fp, valid_data, feed_dict_batch)
toc = time.time()
test_time = toc - tic
if self.timing_in_training:
self._message(
"batch %7d training time %.2f s, testing time %.2f s" %
(cur_batch, train_time, test_time))
train_time = 0
if self.save_freq > 0 and cur_batch % self.save_freq == 0 and self.run_opt.is_chief:
if self.saver is not None:
self.saver.save(self.sess,
os.getcwd() + "/" + self.save_ckpt)
self._message("saved checkpoint %s" % self.save_ckpt)
if self.run_opt.is_chief:
fp.close()
if self.profiling and self.run_opt.is_chief:
fetched_timeline = timeline.Timeline(prf_run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(self.profiling_file, 'w') as f:
f.write(chrome_trace)
def run_model(model,
horovod=False,
gpu_num=1,
output=None,
steptime=False,
profile=False,
timeline=False,
loss=False,
session=1,
step=1,
batchsize=None,
graph=False):
# TODO: description
# cannot dump graph if timeline or profile is On
if graph and (timeline or profile):
raise ValueError("cannot dump graph togother with timeline or tfprof")
with tf.Graph().as_default():
times_list = []
losses_list = []
op, _loss = tf_model.get_model(model, batchsize, horovod=horovod)
# set gpus available
config = tf.ConfigProto()
if horovod is True:
config.gpu_options.allow_growth = False
config.gpu_options.visible_device_list = str(hvd.local_rank())
# print('DEBUG: ', str(hvd.local_rank()))
else:
# buildup gpus='0,1,2...'
config.gpu_options.allow_growth = False
gpus = ','.join(map(str, range(gpu_num)))
print('DEBUG: gpus=%s' % gpus)
config.gpu_options.visible_device_list = gpus
for i in range(session):
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
times = []
losses = []
opts = None
run_metadata = None
# the dump graph mode on
if graph:
opts = tf.RunOptions(output_partition_graphs=True)
run_metadata = tf.RunMetadata()
# the profile mode on
elif profile or timeline:
# create runOptions and run_metadata object
opts = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
if profile:
# Create a profiler.
profiler = model_analyzer.Profiler(sess.graph)
for n in range(step):
start_time = time.time()
# run model
if loss is True:
res = sess.run([op, _loss],
options=opts,
run_metadata=run_metadata)
losses.append(res[1])
else:
res = sess.run(op, options=opts, run_metadata=run_metadata)
train_time = time.time() - start_time
times.append(train_time)
# print steptime and loss at realtime
if loss is True:
print('Sess%d/%d Step%d/%d: time=%.2fms loss=%.2f' %
(i + 1, session, n + 1, step, train_time * 1000,
res[1]))
else:
print('Sess%d/%d Step%d/%d: time=%.2fms' %
(i + 1, session, n + 1, step, train_time * 1000))
if (not graph) and profile:
profiler.add_step(step=step, run_meta=run_metadata)
times_list.append(times)
losses_list.append(losses)
if output is not None:
# make folder if it not exist
try:
if not os.path.exists(output):
os.makedirs(output)
except (FileExistsError):
print("")
file_loss = '_lossOn' if loss else ''
file_trace = '_traceOn' if profile or timeline else ''
file_horovod = '_hvdRank%d' % hvd.rank() if horovod else ''
file_batchsize = '_bs%d' % batchsize if batchsize is not None\
else '_bsDefault'
file_gpunum = '_gpunum%d' % gpu_num
if steptime is True:
filename = '%s%s%s%s%s%s_steptime.csv' %\
(model, file_batchsize, file_loss, file_trace,
file_horovod, file_gpunum)
output_csv(filename, times_list, path=output, scale=1000)
if loss is True:
filename = '%s%s%s%s%s%s_loss.csv' % \
(model, file_batchsize, file_loss, file_trace,
file_horovod, file_gpunum)
output_csv(filename, losses_list, path=output)
if graph:
# save each partition of graph with _output_shapes attr
if horovod:
graph_dir = os.path.join(
output, '%s%s%s%s_partitionGraph' %
(model, file_batchsize, file_loss, file_gpunum),
str(hvd.rank()))
if not os.path.exists(graph_dir):
os.makedirs(graph_dir)
save_partition_graph_shapes(run_metadata, graph_dir,
'graph')
else:
save_partition_graph_shapes(
run_metadata, output, '%s%s%s%s%s_partitionGraph' %
(model, file_batchsize, file_loss, file_horovod,
file_gpunum))
if profile is True:
filename = '%s%s%s%s%s_gpunum%d.profile' % \
(model, file_batchsize, file_loss, file_trace,
file_horovod, gpu_num)
filepath = output + '/' + filename
generate_tfprof_profile(profiler, filepath)
if timeline is True:
filename = '%s%s%s%s%s_gpunum%d.timeline' % \
(model, file_batchsize, file_loss, file_trace,
file_horovod, gpu_num)
filepath = output + '/' + filename
tl = _timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
with open(filepath, 'w') as f:
f.write(ctf)
def train(train_data, test_data=None, sampler_name='Uniform'):
G = train_data[0]
features = train_data[1]
id_map = train_data[2]
class_map = train_data[4]
if isinstance(list(class_map.values())[0], list):
num_classes = len(list(class_map.values())[0])
else:
num_classes = len(set(class_map.values()))
if not features is None:
# pad with dummy zero vector
features = np.vstack([features, np.zeros((features.shape[1], ))])
context_pairs = train_data[3] if FLAGS.random_context else None
placeholders = construct_placeholders(num_classes)
minibatch = NodeMinibatchIterator(G,
id_map,
placeholders,
class_map,
num_classes,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree,
context_pairs=context_pairs)
adj_info_ph = tf.placeholder(tf.int32, shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
adj_shape = adj_info.get_shape().as_list()
if FLAGS.model == 'mean_concat':
# Create model
if sampler_name == 'Uniform':
sampler = UniformNeighborSampler(adj_info)
elif sampler_name == 'ML':
sampler = MLNeighborSampler(adj_info, features)
elif sampler_name == 'FastML':
sampler = FastMLNeighborSampler(adj_info, features)
if FLAGS.samples_3 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2),
SAGEInfo("node", sampler, FLAGS.samples_3, FLAGS.dim_3)
]
elif FLAGS.samples_2 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
else:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1)
]
# modified
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos,
concat=True,
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'mean_add':
# Create model
if sampler_name == 'Uniform':
sampler = UniformNeighborSampler(adj_info)
elif sampler_name == 'ML':
sampler = MLNeighborSampler(adj_info, features)
elif sampler_name == 'FastML':
sampler = FastMLNeighborSampler(adj_info, features)
if FLAGS.samples_3 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2),
SAGEInfo("node", sampler, FLAGS.samples_3, FLAGS.dim_3)
]
elif FLAGS.samples_2 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
else:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1)
]
# modified
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos,
concat=False,
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'gcn':
if sampler_name == 'Uniform':
sampler = UniformNeighborSampler(adj_info)
elif sampler_name == 'ML':
sampler = MLNeighborSampler(adj_info, features)
elif sampler_name == 'FastML':
sampler = FastMLNeighborSampler(adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, 2 * FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, 2 * FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="gcn",
model_size=FLAGS.model_size,
concat=False,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_seq':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="seq",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_maxpool':
if sampler_name == 'Uniform':
sampler = UniformNeighborSampler(adj_info)
elif sampler_name == 'ML':
sampler = MLNeighborSampler(adj_info, features)
elif sampler_name == 'FastML':
sampler = FastMLNeighborSampler(adj_info, features)
#sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="maxpool",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_meanpool':
if sampler_name == 'Uniform':
sampler = UniformNeighborSampler(adj_info)
elif sampler_name == 'ML':
sampler = MLNeighborSampler(adj_info, features)
elif sampler_name == 'FastML':
sampler = FastMLNeighborSampler(adj_info, features)
#sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="meanpool",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
else:
raise Exception('Error: model name unrecognized.')
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
#config.gpu_options.per_process_gpu_memory_fraction = GPU_MEM_FRACTION
config.allow_soft_placement = True
# Initialize session
sess = tf.Session(config=config)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(log_dir(sampler_name), sess.graph)
# Save model
model_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
saver = tf.train.Saver(var_list=model_vars)
model_path = './model/' + FLAGS.train_prefix.split(
"/")[-1] + '-' + model_prefix() + '-' + sampler_name
model_path += hyper_prefix()
if not os.path.exists(model_path):
os.makedirs(model_path)
# Init variables
sess.run(tf.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Restore params of ML sampler model
if sampler_name == 'ML' or sampler_name == 'FastML':
sampler_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="MLsampler")
#pdb.set_trace()
saver_sampler = tf.train.Saver(var_list=sampler_vars)
if FLAGS.allhop_rewards:
sampler_model_path = './model/MLsampler-' + FLAGS.train_prefix.split(
'/')[-1] + '-' + model_prefix() + '-allhops'
else:
sampler_model_path = './model/MLsampler-' + FLAGS.train_prefix.split(
'/')[-1] + '-' + model_prefix() + '-lasthop'
sampler_model_path += hyper_prefix()
saver_sampler.restore(sess, sampler_model_path + 'model.ckpt')
# Train model
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_adj_info = tf.assign(adj_info, minibatch.adj)
val_adj_info = tf.assign(adj_info, minibatch.test_adj)
val_cost_ = []
val_f1_mic_ = []
val_f1_mac_ = []
duration_ = []
epoch_laps_ = []
ln_acc = sparse.csr_matrix((adj_shape[0], adj_shape[0]), dtype=np.float32)
lnc_acc = sparse.csr_matrix((adj_shape[0], adj_shape[0]), dtype=np.int32)
ln_acc = ln_acc.tolil()
lnc_acc = lnc_acc.tolil()
#learning_rate = [0.01, 0.001, 0.0001]
learning_rate = [FLAGS.learning_rate]
for lr_iter in range(len(learning_rate)):
for epoch in range(FLAGS.epochs):
epoch_time = time.time()
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch + 1))
epoch_val_costs.append(0)
while not minibatch.end():
# Construct feed dictionary
feed_dict, labels = minibatch.next_minibatch_feed_dict()
if feed_dict.values()[0] != FLAGS.batch_size:
break
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
feed_dict.update(
{placeholders['learning_rate']: learning_rate[lr_iter]})
t = time.time()
# Training step
outs = sess.run([
merged, model.opt_op, model.loss, model.preds,
model.loss_node, model.loss_node_count
],
feed_dict=feed_dict)
train_cost = outs[2]
if iter % FLAGS.validate_iter == 0:
# Validation
sess.run(val_adj_info.op)
if FLAGS.validate_batch_size == -1:
val_cost, val_f1_mic, val_f1_mac, duration = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size)
else:
val_cost, val_f1_mic, val_f1_mac, duration = evaluate(
sess, model, minibatch, FLAGS.validate_batch_size)
# accumulate val results
val_cost_.append(val_cost)
val_f1_mic_.append(val_f1_mic)
val_f1_mac_.append(val_f1_mac)
duration_.append(duration)
#
sess.run(train_adj_info.op)
epoch_val_costs[-1] += val_cost
if total_steps % FLAGS.print_every == 0:
summary_writer.add_summary(outs[0], total_steps)
# Print results
avg_time = (avg_time * total_steps + time.time() -
t) / (total_steps + 1)
ln = outs[4].values
ln_idx = outs[4].indices
ln_acc[ln_idx[:, 0], ln_idx[:, 1]] += ln
lnc = outs[5].values
lnc_idx = outs[5].indices
lnc_acc[lnc_idx[:, 0], lnc_idx[:, 1]] += lnc
if total_steps % FLAGS.print_every == 0:
train_f1_mic, train_f1_mac = calc_f1(labels, outs[3])
print("Iter:", '%04d' % iter, "train_loss=",
"{:.5f}".format(train_cost), "train_f1_mic=",
"{:.5f}".format(train_f1_mic), "val_loss=",
"{:.5f}".format(val_cost), "val_f1_mic=",
"{:.5f}".format(val_f1_mic), "time per iter=",
"{:.5f}".format(avg_time))
iter += 1
total_steps += 1
if total_steps > FLAGS.max_total_steps:
break
epoch_laps = time.time() - epoch_time
epoch_laps_.append(epoch_laps)
print("Epoch time=", "{:.5f}".format(epoch_laps))
if total_steps > FLAGS.max_total_steps:
break
print("avg time per epoch=", "{:.5f}".format(np.mean(epoch_laps_)))
# Save model
save_path = saver.save(sess, model_path + 'model.ckpt')
print('model is saved at %s' % save_path)
# Save loss node and count
loss_node_path = './loss_node/' + FLAGS.train_prefix.split(
'/')[-1] + '-' + model_prefix() + '-' + sampler_name
loss_node_path += hyper_prefix()
if not os.path.exists(loss_node_path):
os.makedirs(loss_node_path)
loss_node = sparse.save_npz(loss_node_path + 'loss_node.npz',
sparse.csr_matrix(ln_acc))
loss_node_count = sparse.save_npz(loss_node_path + 'loss_node_count.npz',
sparse.csr_matrix(lnc_acc))
print('loss and count per node is saved at %s' % loss_node_path)
print("Optimization Finished!")
sess.run(val_adj_info.op)
# test
val_cost_ = []
val_f1_mic_ = []
val_f1_mac_ = []
duration_ = []
print("Writing test set stats to file (don't peak!)")
# timeline
if FLAGS.timeline == True:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
else:
run_options = None
run_metadata = None
for iter in range(10):
val_cost, val_f1_mic, val_f1_mac, duration = incremental_evaluate(
sess,
model,
minibatch,
FLAGS.batch_size,
run_options,
run_metadata,
test=True)
print("Full validation stats:", "loss=", "{:.5f}".format(val_cost),
"f1_micro=", "{:.5f}".format(val_f1_mic), "time=",
"{:.5f}".format(duration))
val_cost_.append(val_cost)
val_f1_mic_.append(val_f1_mic)
duration_.append(duration)
print("mean: loss={:.5f} f1_micro={:.5f} time={:.5f}\n".format(
np.mean(val_cost_), np.mean(val_f1_mic_), np.mean(duration_)))
# write test results
with open(log_dir(sampler_name) + "test_stats.txt", "w") as fp:
for iter in range(10):
fp.write("loss={:.5f} f1_micro={:.5f} time={:.5f}\n".format(
val_cost_[iter], val_f1_mic_[iter], duration_[iter]))
fp.write("mean: loss={:.5f} f1_micro={:.5f} time={:.5f}\n".format(
np.mean(val_cost_), np.mean(val_f1_mic_), np.mean(duration_)))
fp.write("variance: loss={:.5f} f1_micro={:.5f} time={:.5f}\n".format(
np.var(val_cost_), np.var(val_f1_mic_), np.var(duration_)))
# create timeline object, and write it to a json
if FLAGS.timeline == True:
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format(show_memory=True)
with open(log_dir(sampler_name) + 'timeline.json', 'w') as f:
print('timeline written at %s' %
(log_dir(sampler_name) + 'timelnie.json'))
f.write(ctf)
sess.close()
tf.reset_default_graph()
def im_detect(sess, net, im, boxes=None):
"""Detect object classes in an image given object proposals.
Arguments:
net (caffe.Net): Fast R-CNN network to use
im (ndarray): color image to test (in BGR order)
boxes (ndarray): R x 4 array of object proposals
Returns:
scores (ndarray): R x K array of object class scores (K includes
background as object category 0)
boxes (ndarray): R x (4*K) array of predicted bounding boxes
"""
blobs, im_scales = _get_blobs(im, boxes)
# When mapping from image ROIs to feature map ROIs, there's some aliasing
# (some distinct image ROIs get mapped to the same feature ROI).
# Here, we identify duplicate feature ROIs, so we only compute features
# on the unique subset.
if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes,
return_index=True,
return_inverse=True)
blobs['rois'] = blobs['rois'][index, :]
boxes = boxes[index, :]
if cfg.TEST.HAS_RPN:
im_blob = blobs['data']
blobs['im_info'] = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]],
dtype=np.float32)
# forward pass
if cfg.TEST.HAS_RPN:
feed_dict = {
net.data: blobs['data'],
net.im_info: blobs['im_info'],
net.keep_prob: 1.0
}
else:
feed_dict = {
net.data: blobs['data'],
net.rois: blobs['rois'],
net.keep_prob: 1.0
}
run_options = None
run_metadata = None
if cfg.TEST.DEBUG_TIMELINE:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
cls_score, cls_prob, bbox_pred, rois = sess.run([
net.get_output('cls_score'),
net.get_output('cls_prob'),
net.get_output('bbox_pred'),
net.get_output('rois')
],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
if cfg.TEST.HAS_RPN:
assert len(im_scales) == 1, "Only single-image batch implemented"
boxes = rois[:, 1:5] / im_scales[0]
if cfg.TEST.SVM:
# use the raw scores before softmax under the assumption they
# were trained as linear SVMs
scores = cls_score
else:
# use softmax estimated probabilities
scores = cls_prob
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = _clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
# Map scores and predictions back to the original set of boxes
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
if cfg.TEST.DEBUG_TIMELINE:
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file = open(
str(long(time.time() * 1000)) + '-test-timeline.ctf.json', 'w')
trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
trace_file.close()
return scores, pred_boxes
def train(self,
names_train,
y_train,
names_valid,
y_valid,
model_path,
batch_size=128,
patience=1024000,
stat_interval=1000,
valid_interval=1000,
summary_interval=1000,
valid_batch_size=2048,
profile=False):
"""Train a gender classifier on the name/gender pairs."""
start_time = time()
def add_metric_summaries(mode, iteration, name2metric):
"""Add summary for metric."""
metric_summary = tf.Summary()
for name, metric in name2metric.items():
metric_summary.value.add(tag='{}_{}'.format(mode, name),
simple_value=metric)
summary_writer.add_summary(metric_summary, global_step=iteration)
def show_train_stats(epoch, iteration, losses, y_cat, y_cat_pred):
# compute mean statistics
loss = np.mean(losses)
accuracy = accuracy_score(y_cat, y_cat_pred)
score = accuracy - loss
_LOGGER.info(
'Epoch={}, Iter={:,}, Mean Training Loss={:.4f}, Accuracy={:.4f}, '
'Accuracy - Loss={:.4f}'.format(epoch, iteration, loss,
accuracy, score))
add_metric_summaries(
'train', iteration, {
'cross_entropy': loss,
'accuracy': accuracy,
'accuracy - loss': score
})
_LOGGER.info('\n{}'.format(
classification_report(y_cat, y_cat_pred, digits=3)))
return list(), list(), list()
def validate(epoch, iteration, X, y, best_score, patience):
"""Validate the model on validation set."""
batch_generator = BatchGenerator(X,
y,
batch_size=valid_batch_size,
valid=True)
losses, y_cat, y_cat_pred = list(), list(), list()
for X_batch, y_batch in batch_generator:
X_batch, word_lens, char_lens = self._add_padding(X_batch)
loss, y_pred = session.run(
[nodes['loss'], nodes['y_pred']],
feed_dict={
nodes['X']: X_batch,
nodes['y']: y_batch,
nodes['word_lens']: word_lens,
nodes['char_lens']: char_lens,
nodes['is_train']: False
},
options=run_options,
run_metadata=run_metadata)
losses.append(loss)
y_cat.extend(self._categorize_y(y_batch))
y_cat_pred.extend(self._categorize_y(y_pred))
# compute mean statistics
loss = np.mean(losses)
accuracy = accuracy_score(y_cat, y_cat_pred)
score = accuracy - loss
_LOGGER.info(
'Epoch={}, Iter={:,}, Validation Loss={:.4f}, Accuracy={:.4f}, '
'Accuracy - Loss={:.4f}'.format(epoch, iteration, loss,
accuracy, score))
add_metric_summaries(
'valid', iteration, {
'cross_entropy': loss,
'accuracy': accuracy,
'accuracy - loss': score
})
_LOGGER.info('\n{}'.format(
classification_report(y_cat, y_cat_pred, digits=3)))
if score > best_score:
_LOGGER.info(
'Best score (Accuracy - Loss) so far, save the model.')
self._save(model_path, session)
best_score = score
if iteration * 2 > patience:
patience = iteration * 2
_LOGGER.info('Increased patience to {:,}'.format(patience))
if run_metadata:
with open(_VALID_PROFILE_FILE, 'w') as file_:
file_.write(
timeline.Timeline(run_metadata.step_stats).
generate_chrome_trace_format())
return best_score, patience
_LOGGER.info('Prepare inputs and other variables for the model...')
self._fit_encoder(names_train + names_valid)
X_train = self._encode_chars(names_train)
X_valid = self._encode_chars(names_valid)
train_size = len(X_train)
train_batch_generator = BatchGenerator(X_train, y_train, batch_size)
best_valid_score = np.float64('-inf')
losses = list()
y_cat = list()
y_cat_pred = list()
iteration = 0
# profiler
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE) if profile else None
run_metadata = tf.RunMetadata() if profile else None
_LOGGER.info('Building the tensorflow graph...')
self._build_graph()
nodes = self._nodes
session = tf.Session(graph=self._graph)
summary_writer = tf.summary.FileWriter(
os.path.join(model_path, self._tensorboard_dir), session.graph)
self._visualize_embedding(model_path, summary_writer)
session.run(nodes['init'])
_LOGGER.info('Start fitting a model...')
# iterate over batches
for batch_id, (X_batch, y_batch) in enumerate(train_batch_generator):
epoch = 1 + iteration // train_size
if batch_id % summary_interval == 0:
summaries = session.run(nodes['summaries'])
summary_writer.add_summary(summaries, global_step=iteration)
X_batch, word_lens, char_lens = self._add_padding(X_batch)
# Predict labels and update the parameters
_, loss, y_pred = session.run(
[nodes['optimizer'], nodes['loss'], nodes['y_pred']],
feed_dict={
nodes['X']: X_batch,
nodes['y']: y_batch,
nodes['word_lens']: word_lens,
nodes['char_lens']: char_lens,
nodes['is_train']: True
},
options=run_options,
run_metadata=run_metadata)
losses.append(loss)
y_cat.extend(self._categorize_y(y_batch))
y_cat_pred.extend(self._categorize_y(y_pred))
iteration += batch_size
if run_metadata:
with open(_TRAIN_PROFILE_FILE, 'w') as file_:
file_.write(
timeline.Timeline(run_metadata.step_stats).
generate_chrome_trace_format())
if batch_id % stat_interval == 0:
losses, y_cat, y_cat_pred = show_train_stats(
epoch, iteration, losses, y_cat, y_cat_pred)
if batch_id % valid_interval == 0:
best_valid_score, patience = validate(epoch, iteration,
X_valid, y_valid,
best_valid_score,
patience)
if iteration > patience:
_LOGGER.info(
'Iteration is more than patience, finish training.')
break
_LOGGER.info('Finished fitting the model.')
_LOGGER.info(
'Best Validation Score (Accuracy - Cross-entropy Loss): {:.4f}'.
format(best_valid_score))
# close the session
session.close()
end_time = time()
_LOGGER.info('Took {:,} seconds to train the model.'.format(
int(end_time - start_time)))
return best_valid_score
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError(
'Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = 'D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\moon,D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\son'
#DATA_DIRECTORY = 'D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\moon'
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
#LOGDIR = None
LOGDIR = './/logdir-wavenet//train//2019-03-27T20-27-18'
parser.add_argument(
'--logdir',
type=str,
default=LOGDIR,
help=
'Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.'
)
parser.add_argument(
'--logdir_root',
type=str,
default=None,
help=
'Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.'
)
parser.add_argument(
'--restore_from',
type=str,
default=None,
help=
'Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.'
)
CHECKPOINT_EVERY = 1000 # checkpoint 저장 주기
parser.add_argument(
'--checkpoint_every',
type=int,
default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after. Default: ' +
str(CHECKPOINT_EVERY) + '.')
parser.add_argument('--eval_every',
type=int,
default=2,
help='Steps between eval on test data')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
config.data_dir = config.data_dir.split(",")
try:
directories = validate_directories(config, default_hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
log_path = os.path.join(logdir, 'train.log')
infolog.init(log_path, logdir)
global_step = tf.Variable(0, name='global_step', trainable=False)
if default_hparams.l2_regularization_strength == 0:
default_hparams.l2_regularization_strength = None
# Create coordinator.
coord = tf.train.Coordinator()
num_speakers = len(config.data_dir)
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = default_hparams.silence_threshold if default_hparams.silence_threshold > EPSILON else None
gc_enable = True # Before: num_speakers > 1 After: 항상 True
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = DataFeederWavenet(
coord,
config.data_dir,
batch_size=default_hparams.wavenet_batch_size,
gc_enable=gc_enable,
test_mode=False)
# test를 위한 DataFeederWavenet를 하나 만들자. 여기서는 딱 1개의 파일만 가져온다.
reader_test = DataFeederWavenet(coord,
config.data_dir,
batch_size=1,
gc_enable=gc_enable,
test_mode=True,
queue_size=1)
audio_batch, lc_batch, gc_id_batch = reader.inputs_wav, reader.local_condition, reader.speaker_id
# Create train network.
net = create_network(default_hparams,
default_hparams.wavenet_batch_size,
num_speakers,
is_training=True)
net.add_loss(
input_batch=audio_batch,
local_condition=lc_batch,
global_condition_batch=gc_id_batch,
l2_regularization_strength=default_hparams.l2_regularization_strength,
upsample_type=default_hparams.upsample_type)
net.add_optimizer(default_hparams, global_step)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)
) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=default_hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
start_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or start_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
start_step = 0
except:
print(
"Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model."
)
raise
###########
reader.start_in_session(sess, start_step)
reader_test.start_in_session(sess, start_step)
################### Create test network. <---- Queue 생성 때문에, sess restore후 test network 생성
net_test = create_network(default_hparams,
1,
num_speakers,
is_training=False)
if default_hparams.scalar_input:
samples = tf.placeholder(tf.float32, shape=[net_test.batch_size, None])
waveform = 2 * np.random.rand(net_test.batch_size).reshape(
net_test.batch_size, -1) - 1
else:
samples = tf.placeholder(tf.int32, shape=[
net_test.batch_size, None
]) # samples: mu_law_encode로 변환된 것. one-hot으로 변환되기 전. (batch_size, 길이)
waveform = np.random.randint(default_hparams.quantization_channels,
size=net_test.batch_size).reshape(
net_test.batch_size, -1)
upsampled_local_condition = tf.placeholder(
tf.float32, shape=[net_test.batch_size, default_hparams.num_mels])
speaker_id = tf.placeholder(tf.int32, shape=[net_test.batch_size])
next_sample = net_test.predict_proba_incremental(
samples, upsampled_local_condition, speaker_id
) # Fast Wavenet Generation Algorithm-1611.09482 algorithm 적용
sess.run(net_test.queue_initializer)
# test를 위한 placeholder는 모두 3개: samples,speaker_id,upsampled_local_condition
# test용 mel-spectrogram을 하나 뽑자. 그것을 고정하지 않으면, thread가 계속 돌아가면서 data를 읽어온다. reader_test의 역할은 여기서 끝난다.
mel_input_test, speaker_id_test = sess.run(
[reader_test.local_condition, reader_test.speaker_id])
with tf.variable_scope('wavenet', reuse=tf.AUTO_REUSE):
upsampled_local_condition_data = net_test.create_upsample(
mel_input_test, upsample_type=default_hparams.upsample_type)
upsampled_local_condition_data_ = sess.run(
upsampled_local_condition_data
) # upsampled_local_condition_data_ 을 feed_dict로 placehoder인 upsampled_local_condition에 넣어준다.
######################################################
start_step = sess.run(global_step)
step = last_saved_step = start_step
try:
while not coord.should_stop():
start_time = time.time()
if default_hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
log('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize],
options=run_options,
run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize])
duration = time.time() - start_time
log('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
if step % config.eval_every == 0: # config.eval_every
eval_step(sess, logdir, step, waveform,
upsampled_local_condition_data_, speaker_id_test,
mel_input_test, samples, speaker_id,
upsampled_local_condition, next_sample)
if step >= default_hparams.num_steps:
# error message가 나오지만, 여기서 멈춘 것은 맞다.
raise Exception('End xxx~~~yyy')
except Exception as e:
print('finally')
log('Exiting due to exception: %s' % e, slack=True)
#if step > last_saved_step:
# save(saver, sess, logdir, step)
traceback.print_exc()
coord.request_stop(e)
def train_mnist_cnn(FLAGS):
# Config
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
inter_op_parallelism_threads=1)
# Enable the custom optimizer using the rewriter config options
# CRL-ORIG: config = ngraph_bridge.update_config(config)
# Note: Additional configuration option to boost performance is to set the
# following environment for the run:
# OMP_NUM_THREADS=44 KMP_AFFINITY=granularity=fine,scatter
# The OMP_NUM_THREADS number should correspond to the number of
# cores in the system
# Set Seed
shuffle_batch = True
if FLAGS.make_deterministic:
seed = 1
tf.random.set_random_seed(seed)
shuffle_batch = False
supported_optimizers = ["adam", "sgd"]
assert (FLAGS.optimizer in supported_optimizers), "Optimizer not supported"
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
optimizer_scope = FLAGS.optimizer + "_optimizer"
with tf.name_scope(optimizer_scope):
if FLAGS.optimizer == "adam":
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
elif FLAGS.optimizer == "sgd":
train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
tf.summary.scalar('Training accuracy', accuracy)
tf.summary.scalar('Loss function', cross_entropy)
graph_location = "./tf-profile-train"
print('Saving graph to: %s' % graph_location)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
train_loops = FLAGS.train_loop_count
loss_values = []
for i in range(train_loops):
batch = mnist.train.next_batch(FLAGS.batch_size,
shuffle=shuffle_batch)
if i % 10 == 0:
t = time.time()
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
})
#tf.summary.scalar('Training accuracy', train_accuracy)
print('step %d, training accuracy %g, %g sec to evaluate' %
(i, train_accuracy, time.time() - t))
t = time.time()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
_, summary, loss = sess.run([train_step, merged, cross_entropy],
feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
},
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata,
'cnn_' + "step_{}".format(i), i)
if (i >= 100) and (
i < 105): # Only write timelines for steps 100 through 104
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(graph_location + '/timeline_{}.json'.format(i),
'w') as f:
f.write(chrome_trace)
loss_values.append(loss)
print('step %d, loss %g, %g sec for training step' %
(i, loss, time.time() - t))
train_writer.add_summary(summary, i)
print("Training finished. Running test")
num_test_images = FLAGS.test_image_count
x_test = mnist.test.images[:num_test_images]
y_test = mnist.test.labels[:num_test_images]
test_accuracy = accuracy.eval(feed_dict={
x: x_test,
y_: y_test,
keep_prob: 1.0
})
print('test accuracy %g' % test_accuracy)
saver.save(sess, FLAGS.model_dir)
return loss_values, test_accuracy
def train_model(job_id):
model_type_list = cfg_para.multi_model_type
num_layer_list = cfg_para.multi_num_layer
activation_list = cfg_para.multi_activation
batch_size_list = cfg_para.multi_batch_size
learning_rate_list = cfg_para.multi_learning_rate
optimizer_list = cfg_para.multi_opt
model_type = model_type_list[job_id]
num_layer = num_layer_list[job_id]
activation = activation_list[job_id]
batch_size = batch_size_list[job_id]
learning_rate = learning_rate_list[job_id]
optimizer = optimizer_list[job_id]
num_epoch = cfg_para.multi_num_epoch
train_dataset = cfg_para.multi_train_dataset
use_tf_timeline = cfg_para.multi_use_tb_timeline
use_cpu = cfg_para.multi_use_cpu
if use_cpu:
train_device = '/cpu:0'
else:
train_device = '/gpu:0'
model_name = '{0}-{1}-{2}-{3}-{4}-{5}-{6}-{7}'.format(
job_id, model_type, num_layer, batch_size, learning_rate, optimizer,
num_epoch, train_dataset)
##########################################
# load dataset
##########################################
img_width, img_height, num_channel, num_class = load_dataset_para(
train_dataset)
train_feature_input, train_label_input = load_train_dataset(train_dataset)
##########################################
# build model
##########################################
features = tf.placeholder(tf.float32,
[None, img_width, img_height, num_channel])
labels = tf.placeholder(tf.int64, [None, num_class])
dm = ModelImporter(model_type,
str(job_id),
num_layer,
img_height,
img_width,
num_channel,
num_class,
batch_size,
optimizer,
learning_rate,
activation,
batch_padding=False)
model_entity = dm.get_model_entity()
model_logit = model_entity.build(features, is_training=True)
train_op = model_entity.train(model_logit, labels)
##########################################
# train model
##########################################
step_time = 0
step_count = 0
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
if train_dataset == 'imagenet':
image_list = sorted(os.listdir(train_feature_input))
with tf.device(train_device):
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
num_batch = train_label_input.shape[0] // batch_size
for e in range(num_epoch):
for i in range(num_batch):
print('epoch %d / %d, step %d / %d' %
(e + 1, num_epoch, i + 1, num_batch))
if i != 0:
start_time = timer()
batch_offset = i * batch_size
batch_end = (i + 1) * batch_size
if train_dataset == 'imagenet':
batch_list = image_list[batch_offset:batch_end]
train_feature_batch = load_imagenet_raw(
train_feature_input, batch_list, img_height,
img_width)
else:
train_feature_batch = train_feature_input[
batch_offset:batch_end]
train_label_batch = train_label_input[
batch_offset:batch_end]
if use_tf_timeline:
profile_path = cfg_path.profile_path
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(train_op,
feed_dict={
features: train_feature_batch,
labels: train_label_batch
},
options=run_options,
run_metadata=run_metadata)
trace = timeline.Timeline(
step_stats=run_metadata.step_stats)
trace_file = open(
profile_path + '/' + str(model_type) + '-' +
str(batch_size) + '-' + str(i) + '.json', 'w')
trace_file.write(
trace.generate_chrome_trace_format(
show_dataflow=True, show_memory=True))
else:
sess.run(train_op,
feed_dict={
features: train_feature_batch,
labels: train_label_batch
})
if i != 0:
end_time = timer()
dur_time = end_time - start_time
print("step time:", dur_time)
step_time += dur_time
step_count += 1
step_time_result = f'average step time (ms) of {model_name}: {step_time / step_count * 1000}'
return step_time_result
def main():
configproto = tf.ConfigProto()
configproto.gpu_options.allow_growth = True
configproto.log_device_placement = args.log_device
configproto.allow_soft_placement = args.soft_placement
configproto.inter_op_parallelism_threads = args.num_cores
configproto.intra_op_parallelism_threads = args.num_cores
with tf.Graph().as_default(), tf.Session(config=configproto) as sess:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE) if show_run_meta else None
run_metadata = tf.RunMetadata() if show_run_meta else None
model = graph_moudle.Model()
model.init_global_step()
vt, vs, vo = model.model_setup()
tf.initialize_all_variables().run()
cnt = 0
for var in vs:
cnt += 1
str_line = str(cnt) + '. ' + str(var.name) + ': ' + str(
var.get_shape())
print(str_line)
ssll = input('aaaaa')
np.random.seed(1234567890)
qs = np.random.randint(0, args.vocab_size,
[10, args.batchsize, args.max_sent_length])
qsm = np.ones_like(qs, dtype=np.float32)
qsm[:, :, -1:] = 0
ts = np.random.randint(0, args.vocab_size,
[10, args.batchsize, 3, args.max_sent_length])
tsm = np.ones_like(ts, dtype=np.float32)
g = np.random.randint(1, 3, (10, args.batchsize))
for i in range(400):
bs = i % 10
if bs == 0:
idx = np.random.shuffle(np.arange(10 * args.batchsize))
qs = np.reshape(qs, [10 * args.batchsize, -1])[idx]
ts = np.reshape(ts, [10 * args.batchsize, -1])[idx]
g = np.reshape(g, [10 * args.batchsize, -1])[idx]
qs = np.reshape(qs, qsm.shape)
ts = np.reshape(ts, tsm.shape)
g = np.reshape(g, (10, args.batchsize))
stime = time.time()
loss, regu_loss = 0, 0
step, loss, pair_loss, regu_loss, acc, acc01, score, _ = \
model.run_epoch(sess, [qs[bs], qsm[bs], ts[bs], tsm[bs], g[bs], True],
run_options=run_options, run_metadata=run_metadata)
'''
pair_loss, acc, acc01, score = \
model.run_epoch(sess, [qs[bs], qsm[bs], ts[bs], tsm[bs], g[bs], False],
run_options=run_options, run_metadata=run_metadata)
'''
print(loss, pair_loss, regu_loss, acc, acc01, time.time() - stime)
print(score[0, :], g[bs][0])
if show_run_meta:
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format(show_memory=True)
with open(args.log_dir_path + '/timeline.json', 'w') as f:
f.write(ctf)
def train(hparams):
"""Build and train the model as specified in hparams"""
ckptsdir = str(Path(hparams.modeldir, "ckpts"))
# build training and eval graphs
train_tuple = create_model(hparams, tf.contrib.learn.ModeKeys.TRAIN)
eval_tuple = create_model(hparams, tf.contrib.learn.ModeKeys.EVAL)
with train_tuple.graph.as_default():
initializer = tf.global_variables_initializer()
train_tables_initializer = tf.tables_initializer()
with eval_tuple.graph.as_default():
local_initializer = tf.local_variables_initializer()
eval_tables_initializer = tf.tables_initializer()
# Summary writers
summary_writer = tf.summary.FileWriter(hparams.modeldir,
train_tuple.graph,
max_queue=25,
flush_secs=30)
if hparams.saved is not None:
# load checkpoint
train_tuple.model.saver.restore(train_tuple.session, hparams.saved)
else:
train_tuple.session.run([initializer])
start_time = process_time()
# initialize the training dataset
train_tuple.session.run([train_tables_initializer])
train_tuple.session.run([train_tuple.iterator.initializer])
# initialize the eval table only once
eval_tuple.session.run([eval_tables_initializer])
# finalize the graph
train_tuple.graph.finalize()
profile_next_step = False
profiled = False
# Train until the dataset throws an error (at the end of num_epochs)
while True:
step_time = []
try:
curr_time = process_time()
if False:
#if not profiled and profile_next_step:
print("Running training step with profiling")
# run profiling
_, train_loss, global_step, _, summary, metadata = train_tuple.model.\
train_with_profile(train_tuple.session, summary_writer)
# write the metadata out to a chrome trace file
trace = timeline.Timeline(step_stats=metadata.step_stats)
with open(hparams.modeldir + "/timeline.ctf.json",
"w") as tracefile:
tracefile.write(trace.generate_chrome_trace_format())
profile_next_step = False
profiled = True
else:
_, train_loss, global_step, _, summary = train_tuple.model.train(
train_tuple.session)
step_time.append(process_time() - curr_time)
# write train summaries
if global_step == 1:
summary_writer.add_summary(summary, global_step)
if global_step % 15 == 0:
summary_writer.add_summary(summary, global_step)
print("Step: %d, Training Loss: %f, Avg Sec/Step: %2.2f" %
(global_step, train_loss, np.mean(step_time)))
if global_step % 100 == 0:
step_time = []
profile_next_step = True
# Do one evaluation
checkpoint_path = train_tuple.model.saver.save(
train_tuple.session,
ckptsdir + "/ckpt",
global_step=global_step)
print(checkpoint_path)
eval_tuple.model.saver.restore(eval_tuple.session,
checkpoint_path)
eval_tuple.session.run(
[eval_tuple.iterator.initializer, local_initializer])
while True:
try:
eval_loss, eval_acc, eval_summary, _ = eval_tuple.model.eval(
eval_tuple.session)
# summary_writer.add_summary(summary, global_step)
except tf.errors.OutOfRangeError:
print("Step: %d, Eval Loss: %f, Eval Accuracy: %f" %
(global_step, eval_loss, eval_acc))
summary_writer.add_summary(eval_summary, global_step)
break
except tf.errors.OutOfRangeError:
print("- End of Trainig -")
break
# End of training
summary_writer.close()
print("Total Training Time: %4.2f" % (process_time() - start_time))
def train_step(self, sess, train_op, global_step, train_step_kwargs):
"""Function that takes a gradient step and specifies whether to stop.
Args:
sess: The current session.
train_op: An `Operation` that evaluates the gradients and returns the
total loss.
global_step: A `Tensor` representing the global training step.
train_step_kwargs: A dictionary of keyword arguments.
Returns:
The total loss and a boolean indicating whether or not to stop training.
Raises:
ValueError: if 'should_trace' is in `train_step_kwargs` but `logdir` is not.
"""
start_time = time.time()
trace_run_options = None
run_metadata = None
if 'should_trace' in train_step_kwargs:
if 'logdir' not in train_step_kwargs:
raise ValueError(
'logdir must be present in train_step_kwargs when '
'should_trace is present')
if sess.run(train_step_kwargs['should_trace']):
trace_run_options = config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE)
run_metadata = config_pb2.RunMetadata()
total_loss, np_global_step = sess.run([train_op, global_step],
options=trace_run_options,
run_metadata=run_metadata)
time_elapsed = time.time() - start_time
# self.debug_training(sess,global_step)
if run_metadata is not None:
tl = timeline.Timeline(run_metadata.step_stats)
trace = tl.generate_chrome_trace_format()
trace_filename = os.path.join(train_step_kwargs['logdir'],
'tf_trace-%d.json' % np_global_step)
logging.info('Writing trace to %s', trace_filename)
file_io.write_string_to_file(trace_filename, trace)
if 'summary_writer' in train_step_kwargs:
train_step_kwargs['summary_writer'].add_run_metadata(
run_metadata, 'run_metadata-%d' % np_global_step)
if 'should_log' in train_step_kwargs:
if sess.run(train_step_kwargs['should_log']):
logging.info('global step %d: loss = %.4f (%.2f sec/step)',
np_global_step, total_loss, time_elapsed)
# TODO(nsilberman): figure out why we can't put this into sess.run. The
# issue right now is that the stop check depends on the global step. The
# increment of global step often happens via the train op, which used
# created using optimizer.apply_gradients.
#
# Since running `train_op` causes the global step to be incremented, one
# would expected that using a control dependency would allow the
# should_stop check to be run in the same session.run call:
#
# with ops.control_dependencies([train_op]):
# should_stop_op = ...
#
# However, this actually seems not to work on certain platforms.
if 'should_stop' in train_step_kwargs:
should_stop = sess.run(train_step_kwargs['should_stop'])
else:
should_stop = False
return total_loss, should_stop
feed_dict={
x: batch_xs,
y_true: batch_ys,
keep_prob: 0.5
},
options=run_options,
run_metadata=run_metadata)
writer.add_summary(summary_str, i)
if (i % 10) == 0:
test_xs, test_ys = mnist.test.next_batch(100)
#test_xs, test_ys = [mnist.test.images, mnist.test.labels]
train_acc = sess.run(accuracy,
feed_dict={
x: batch_xs,
y_true: batch_ys,
keep_prob: 1
})
test_acc = sess.run(accuracy,
feed_dict={
x: test_xs,
y_true: test_ys,
keep_prob: 1
})
print('Step %.4d : train_err = %.2f%% ; test_err = %.2f%%' %
(i, (1 - train_acc) * 100, (1 - test_acc) * 100))
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('/tmp/layers/timeline.ctf.json', 'w') as outfile:
outfile.write(trace.generate_chrome_trace_format())
step = None
last_saved_step = saved_global_step
minvalloss = 10000
try:
for step in range(saved_global_step + 1, args.num_steps):
start_time = time.time()
if args.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
print('Storing metadata')
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
summary, trloss_value, _ = sess.run([summaries, trloss, optim],
options=run_options,
run_metadata=run_metadata)
writer.add_summary(summary, step)
writer.add_run_metadata(run_metadata, 'step_{:04d}'.format(step))
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
summary, trloss_value, _ = sess.run([summaries, trloss, optim])
writer.add_summary(summary, step)
duration = time.time() - start_time
print('step {:d} - trloss = {:.3f}, ({:.3f} sec/step)'.format(
step, trloss_value, duration))
if step % args.checkpoint_every == 0:
valloss_value = sess.run(valloss)
print('validateLoss = {:.3f}, ({:.3f} sec/step)'.format(
valloss_value, duration))
if (valloss_value < minvalloss):
def training(self, sess, train_writer):
with tf.name_scope('loss_function'):
RNet_rpn_yaw_pred = self.net.get_output('RNet_theta')[1]
RNet_rpn_yaw_gt_delta = self.net.get_output('cubic_grid')[1]
RNet_rpn_yaw_gt = self.net.get_output(
'rpn_rois'
)[1][:,
-1] #rpn_3d_boxes:(x1,y1,z1),(x2,y2,z2),score,rpn_cls_label,yaw
RNet_rpn_yaw_gt_new = RNet_rpn_yaw_gt - RNet_rpn_yaw_gt_delta
RNet_rpn_yaw_pred_toshow = RNet_rpn_yaw_pred + RNet_rpn_yaw_gt_delta
rpn_cls_labels = self.net.get_output(
'rpn_rois'
)[1][:,
-2] #rpn_3d_boxes:(x1,y1,z1),(x2,y2,z2),score,rpn_cls_label,yaw
RNet_rpn_yaw_pred = self.angle_trans(RNet_rpn_yaw_pred)
RNet_rpn_yaw_gt_new = self.angle_trans(RNet_rpn_yaw_gt_new)
debug_pred = tf.multiply(rpn_cls_labels,
self.angle_trans(RNet_rpn_yaw_pred))
debug_gt = tf.multiply(rpn_cls_labels,
self.angle_trans(RNet_rpn_yaw_gt_new))
tower_l1_loss = self.Rnet_modified_smooth_l1(
sigma=3,
bbox_pred=RNet_rpn_yaw_pred,
bbox_targets=RNet_rpn_yaw_gt_new)
tower_l1_loss_keep_positive = tf.multiply(rpn_cls_labels,
tower_l1_loss)
loss = tf.reduce_sum(tower_l1_loss_keep_positive) / (
1e-5 + tf.reduce_sum(
tf.cast(tf.not_equal(tower_l1_loss_keep_positive, 0.0),
dtype=tf.float32)))
with tf.name_scope('train_op'):
global_step = tf.Variable(1, trainable=False, name='Global_Step')
lr = tf.train.exponential_decay(cfg.TRAIN.LEARNING_RATE,
global_step,
10000,
0.90,
name='decay-Lr')
Optimizer = tf.train.AdamOptimizer(lr)
var_and_grad = Optimizer.compute_gradients(
loss, var_list=tf.trainable_variables())
train_op = Optimizer.minimize(loss, global_step=global_step)
with tf.name_scope('debug_board'):
tf.summary.scalar('total_loss', loss)
glb_var = tf.trainable_variables()
for i in range(len(glb_var)):
tf.summary.histogram(glb_var[i].name, glb_var[i])
tf.summary.image('theta',
self.net.get_output('RNet_theta')[0],
max_outputs=50)
merged = tf.summary.merge_all() #hxd: before the next summary ops
with tf.name_scope('epoch_valid'):
epoch_cube_theta = tf.placeholder(dtype=tf.float32)
epoch_cube_theta_sum_op = tf.summary.scalar(
'valid_los', epoch_cube_theta)
sess.run(tf.global_variables_initializer())
if self.args.fine_tune:
if True:
# #full graph restore
print 'Loading pre-trained model weights from {:s}'.format(
self.args.weights)
self.net.load(self.args.weights, sess, self.saver, True)
else: # #part graph restore
# # METHOD one
# ref_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope=['vgg_feat_fc'])
# saver1 = tf.train.Saver(ref_vars)
# saver1.restore(sess, self.args.weights)
# # METHOD two
reader = pywrap_tensorflow.NewCheckpointReader(
self.args.weights)
var_to_shape_map = reader.get_variable_to_shape_map()
with tf.variable_scope('', reuse=tf.AUTO_REUSE) as scope:
for key in var_to_shape_map:
try:
var = tf.get_variable(key, trainable=False)
sess.run(var.assign(reader.get_tensor(key)))
print " Assign pretrain model: " + key
except ValueError:
print " Ignore variable:" + key
trainable_var_for_chk = tf.trainable_variables(
) #tf.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
print 'Variables to train: ', trainable_var_for_chk
timer = Timer()
rpn_rois_3d = self.net.get_output('rpn_rois')[1]
if DEBUG:
pass # TODO: Essential step(before sess.run) for using vispy beacuse of the bug of opengl or tensorflow
vispy_init()
i = 0
training_series = range(self.epoch) #self.epoch
for epo_cnt in range(self.args.epoch_iters):
for data_idx in training_series: # DO NOT EDIT the "training_series",for the latter shuffle
iter = global_step.eval(
) # function "minimize()"will increase global_step
blobs = self.dataset.get_minibatch(data_idx,
'train') # get one batch
feed_dict = {
self.net.lidar3d_data: blobs['lidar3d_data'],
self.net.lidar_bv_data: blobs['lidar_bv_data'],
self.net.im_info: blobs['im_info'],
self.net.keep_prob: 0.5,
self.net.gt_boxes_bv: blobs['gt_boxes_bv'],
self.net.gt_boxes_3d: blobs['gt_boxes_3d'],
self.net.gt_boxes_corners: blobs['gt_boxes_corners'],
self.net.calib: blobs['calib'],
}
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
timer.tic()
debug_pred_,delta_,RNet_rpn_yaw_gt_delta_,rpn_rois_3d_,loss_,RNet_rpn_yaw_pred_toshow_,debug_gt_,merged_,_ = \
sess.run([debug_pred, tower_l1_loss_keep_positive, RNet_rpn_yaw_gt_delta, rpn_rois_3d, loss, RNet_rpn_yaw_pred_toshow, debug_gt, merged, train_op, ]
, feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
# debug_pred_,delta_,RNet_rpn_yaw_gt_delta_,rpn_rois_3d_,RNet_rpn_yaw_pred_toshow_,debug_gt_,merged_, = \
# sess.run([debug_pred,tower_l1_loss_keep_positive,RNet_rpn_yaw_gt_delta,rpn_rois_3d,RNet_rpn_yaw_pred_toshow,debug_gt,merged,]
# ,feed_dict=feed_dict,options=run_options, run_metadata=run_metadata)
timer.toc()
if iter % cfg.TRAIN.ITER_DISPLAY == 0:
print 'Iter: %d/%d, Serial_num: %s, Speed: %.3fs/iter, Loss: %.3f ' % (
iter, self.args.epoch_iters * self.epoch,
blobs['serial_num'], timer.average_time, loss_)
print 'theta_delta: ',
for i in range(50):
if delta_[i] != 0.0:
print '%6.3f' % (delta_[i]),
print '\nPredicted angle: ',
for j in range(50):
if debug_pred_[j] != 0.0:
print '%6.3f' % (debug_pred_[j]),
print '\nGt yaw angle: ',
for j in range(50):
if debug_gt_[j] != 0.0:
print '%6.3f' % (debug_gt_[j]),
print '\n'
if iter % 20 == 0 and cfg.TRAIN.TENSORBOARD:
train_writer.add_summary(merged_, iter)
pass
if (iter % 4000 == 0 and cfg.TRAIN.DEBUG_TIMELINE) or (iter
== 100):
#chrome://tracing
trace = timeline.Timeline(
step_stats=run_metadata.step_stats)
trace_file = open(
cfg.LOG_DIR + '/' + 'training-step-' +
str(iter).zfill(7) + '.ctf.json', 'w')
trace_file.write(
trace.generate_chrome_trace_format(show_memory=False))
trace_file.close()
if DEBUG:
scan = blobs['lidar3d_data']
cubic_cls_value = np.ones([cfg.TRAIN.RPN_POST_NMS_TOP_N],
dtype=np.float32) * 0
boxes = BoxAry_Theta(
gt_box3d=blobs['gt_boxes_3d'],
pre_box3d=rpn_rois_3d_,
pre_theta_value=RNet_rpn_yaw_pred_toshow_,
pre_cube_cls=cubic_cls_value
) # RNet_rpn_yaw_pred_toshow_ rpn_rois_3d_[:,-1]
pcd_vispy(scan,
boxes=boxes,
name='CubicNet training',
index=i,
vis_size=(800, 600),
save_img=False,
visible=False)
i += 1
if cfg.TRAIN.EPOCH_MODEL_SAVE: #iter % 2000==0 and :
self.snapshot(sess, iter)
pass
if cfg.TRAIN.USE_VALID and True: #TODO: to complete the valid process
with tf.name_scope('valid_cubic_' + str(epo_cnt + 1)):
print 'Valid the net at the end of epoch_{} ...'.format(
epo_cnt + 1)
valid_loss_total = 0.0
for data_idx in range(self.val_epoch): # self.val_epoch
blobs = self.dataset.get_minibatch(data_idx, 'valid')
feed_dict_ = {
self.net.lidar3d_data: blobs['lidar3d_data'],
self.net.lidar_bv_data: blobs['lidar_bv_data'],
self.net.im_info: blobs['im_info'],
self.net.keep_prob: 0.5,
self.net.gt_boxes_bv: blobs['gt_boxes_bv'],
self.net.gt_boxes_3d: blobs['gt_boxes_3d'],
self.net.gt_boxes_corners:
blobs['gt_boxes_corners'],
self.net.calib: blobs['calib'],
}
loss_valid = sess.run(loss, feed_dict=feed_dict_)
# train_writer.add_summary(valid, data_idx)
valid_loss_total += loss_valid
if cfg.TRAIN.VISUAL_VALID and data_idx % 20 == 0:
print 'Valid step: {:d}/{:d} , theta_loss = {:.3f}'\
.format(data_idx + 1,self.val_epoch,float(loss_valid))
if data_idx % 20 == 0 and cfg.TRAIN.TENSORBOARD:
pass
# train_writer.add_summary(valid_result_, data_idx/20+epo_cnt*1000)
valid_summary = tf.summary.merge([epoch_cube_theta_sum_op])
valid_res = sess.run(valid_summary,
feed_dict={
epoch_cube_theta:
float(valid_loss_total) /
self.val_epoch
})
train_writer.add_summary(valid_res, epo_cnt + 1)
print 'Validation of epoch_{}:theta_loss_total = {:.3f}\n'\
.format(epo_cnt + 1,float(valid_loss_total)/self.val_epoch)
random.shuffle(training_series) # shuffle the training series
print 'Training process has done, enjoy every day !'
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError(
'Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = '.\\data\\moon,.\\data\\son'
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
LOGDIR = None
#LOGDIR = './/logdir-wavenet//train//2018-12-21T22-58-10'
parser.add_argument(
'--logdir',
type=str,
default=LOGDIR,
help=
'Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.'
)
parser.add_argument(
'--logdir_root',
type=str,
default=None,
help=
'Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.'
)
parser.add_argument(
'--restore_from',
type=str,
default=None,
help=
'Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.'
)
CHECKPOINT_EVERY = 1000 # checkpoint 저장 주기
parser.add_argument(
'--checkpoint_every',
type=int,
default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after. Default: ' +
str(CHECKPOINT_EVERY) + '.')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
config.data_dir = config.data_dir.split(",")
try:
directories = validate_directories(config, hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
log_path = os.path.join(logdir, 'train.log')
infolog.init(log_path, logdir)
global_step = tf.Variable(0, name='global_step', trainable=False)
# Create coordinator.
coord = tf.train.Coordinator()
num_speakers = len(config.data_dir)
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = hparams.silence_threshold if hparams.silence_threshold > EPSILON else None
gc_enable = num_speakers > 1
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = DataFeederWavenet(
coord,
config.data_dir,
batch_size=hparams.wavenet_batch_size,
receptive_field=WaveNetModel.calculate_receptive_field(
hparams.filter_width, hparams.dilations, hparams.scalar_input,
hparams.initial_filter_width),
gc_enable=gc_enable)
if gc_enable:
audio_batch, lc_batch, gc_id_batch = reader.inputs_wav, reader.local_condition, reader.speaker_id
else:
audio_batch, lc_batch = reader.inputs_wav, self.local_condition
# Create network.
net = WaveNetModel(
batch_size=hparams.wavenet_batch_size,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels=hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases, # True
scalar_input=hparams.scalar_input,
initial_filter_width=hparams.initial_filter_width,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=num_speakers,
local_condition_channels=hparams.num_mels,
upsample_factor=hparams.upsample_factor,
train_mode=True)
if hparams.l2_regularization_strength == 0:
hparams.l2_regularization_strength = None
net.add_loss(input_batch=audio_batch,
local_condition=lc_batch,
global_condition_batch=gc_id_batch,
l2_regularization_strength=hparams.l2_regularization_strength)
net.add_optimizer(hparams, global_step)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)
) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
start_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or start_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
except:
print(
"Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model."
)
raise
###########
start_step = sess.run(global_step)
last_saved_step = start_step
try:
reader.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
if hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
log('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize],
options=run_options,
run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize])
duration = time.time() - start_time
log('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
if step >= hparams.num_steps:
# error message가 나오지만, 여기서 멈춘 것은 맞다.
raise Exception('End xxx~~~yyy')
except Exception as e:
print('finally')
#if step > last_saved_step:
# save(saver, sess, logdir, step)
coord.request_stop(e)
