def task_new_tfrecord_file(setting="training"):
"""Create a new tfrecord file."""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.tftools.tfrecord.write_tfrecord(
dl_multi.config.settings.get_data(setting),
dl_multi.config.settings._SETTINGS["param_specs"],
dl_multi.config.settings._SETTINGS["param_tfrecord"],
param_label=glu.get_value(dl_multi.config.settings._SETTINGS,
"param_label", dict()))
def task_train_multi_task():
"""Call the main routine for training of a multi task model"""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.models.train_multi_task.train(
dl_multi.config.settings._SETTINGS["param_info"],
dl_multi.config.settings._SETTINGS["param_log"],
dl_multi.config.settings._SETTINGS["param_batch"],
dl_multi.config.settings._SETTINGS["param_save"],
dl_multi.config.settings._SETTINGS["param_train"])
def task_train_single_task_classification():
"""Call the main routine for training of a single task classification model"""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.models.train_single_task_classification.train(
dl_multi.config.settings._SETTINGS["param_info"],
dl_multi.config.settings._SETTINGS["param_log"],
dl_multi.config.settings._SETTINGS["param_batch"],
dl_multi.config.settings._SETTINGS["param_save"],
dl_multi.config.settings._SETTINGS["param_train"])
def task_test_tfrecord_file(setting="training"):
"""Write out items that are created by reading tfrecord file."""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.tftools.tfrecord.test_tfrecord(
dl_multi.config.settings.get_data(setting),
dl_multi.config.settings._SETTINGS["param_specs"],
dl_multi.config.settings._SETTINGS["param_info"],
dl_multi.config.settings._SETTINGS["param_io"],
dl_multi.config.settings._SETTINGS["param_tfrecord"])
def task_eval_single_task_regression(setting="training"):
"""Call the main routine for evaluation of a single task regression model with training data"""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.eval.eval_single_task_regression.eval(
dl_multi.config.settings.get_data(setting),
dl_multi.config.settings._SETTINGS["param_specs"],
dl_multi.config.settings._SETTINGS["param_io"],
dl_multi.config.settings._SETTINGS["param_log"],
dl_multi.config.settings._SETTINGS["param_eval"],
dl_multi.config.settings._SETTINGS["param_label"],
dl_multi.config.settings._SETTINGS["param_class"])
def task_eval_tasks(setting="training"):
"""Call the main routine for the generalized evaluation of a single or multi task model with training data"""
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
dl_multi.eval.eval_tasks.eval(
dl_multi.config.settings.get_data(setting),
dl_multi.config.settings._SETTINGS["param_specs"],
dl_multi.config.settings._SETTINGS["param_io"],
dl_multi.config.settings._SETTINGS["param_log"],
dl_multi.config.settings._SETTINGS["param_eval"],
dl_multi.config.settings._SETTINGS["param_label"],
dl_multi.config.settings._SETTINGS["param_class"])
def task_test_tfrecords_utils():
"""Test the shortcut functions to convert a standard TensorFlow type to a tf.Example-compatible tf.train.Feature
"""
import numpy as np
dl_multi.config.dl_multi.set_cuda_properties(
glu.get_value(dl_multi.config.settings._SETTINGS, "param_cuda",
dict()))
_logger.info(
"Test the shortcut functions to convert a standard TensorFlow type to a tf.Example-compatible tf.train.Feature"
)
# print the results of testing the shortcut functions
print(dl_multi.tfrecords_utils._bytes_feature(b'test_string'))
print(
dl_multi.tfrecords_utils._bytes_feature(u'test_bytes'.encode('utf-8')))
print(dl_multi.tftools.tfutils._float_feature(np.exp(1)))
print(dl_multi.tftools.tfutils._int64_feature(True))
print(dl_multi.tftools.tfutils._int64_feature(1))
print(dl_multi.tftools.tfutils._int64_feature(1, True))
def get_data(
files,
param_specs, # list()
param_io=dict(),
param_label=dict(),
param_show=dict(), # scale=100, show=False, live=True,
param_log=dict() # log_dir, ext=".log"
# default_spec="image",
):
load = lambda path, spec: get_image(
path,
spec,
param_label=param_label,
**param_show # scale=100, show=False, live=True
)
img_in = list()
for f_set in files:
img = dl_multi.utils.imgcontainer.ImgListContainer(
load=load, log_dir=glu.get_value(param_log, "path_dir"))
for f, s in zip(f_set, param_specs):
img.append(path=f, spec=s,
**param_show) # scale=100, show=False, live=True
img_in.append(img)
if not param_io:
return img_in
get_path = glu.PathCreator(**param_io)
img_out = lambda path, img, **kwargs: save_image(get_path(path, **kwargs),
img)
log_out = lambda path, **kwargs: get_path(path, **param_log, **kwargs)
return img_in, img_out, log_out, get_path
def train(param_info, param_log, param_batch, param_save, param_train):
_logger.info(
"Start training multi task classification and regression model with settings:\nparam_info:\t{}\nparam_log:\t{}\nparam_batch:\t{},\nparam_save:\t{},\nparam_train:\t{}"
.format(param_info, param_log, param_batch, param_save, param_train))
# settings ------------------------------------------------------------
# -----------------------------------------------------------------------
# Create the log and checkpoint folders if they do not exist
checkpoint = dl_multi.utils.general.Folder().set_folder(
**param_train["checkpoint"])
log_dir = dl_multi.utils.general.Folder().set_folder(**param_log)
tasks = len(param_train["objective"]) if isinstance(
param_train["objective"], list) else 1
data_io = dl_multi.tftools.tfrecord.tfrecord(param_train["tfrecord"],
param_info,
param_train["input"],
param_train["output"])
data = data_io.get_data()
data = dl_multi.tftools.tfutils.preprocessing(data, param_train["input"],
param_train["output"])
data = dl_multi.tftools.tfaugmentation.rnd_crop(
data, param_train["image-size"],
data_io.get_spec_item_list("channels"),
data_io.get_spec_item_list("scale"), **param_train["augmentation"])
objectives = dl_multi.tftools.tflosses.Losses(param_train["objective"],
logger=_logger,
**glu.get_value(
param_train,
"multi-task", dict()))
# execution -----------------------------------------------------------
# -----------------------------------------------------------------------
# Create batches by randomly shuffling tensors. The capacity specifies the maximum of elements in the queue
data_batch = tf.train.shuffle_batch(data, **param_batch)
input_batch = data_batch[0]
output_batch = data_batch[1:] if isinstance(data_batch[1:],
list) else [data_batch[1:]]
with tf.variable_scope("net"):
pred = dl_multi.plugin.get_module_task(
"models", *param_train["model"])(input_batch)
pred = list(pred) if isinstance(pred, tuple) else [pred]
objectives.update(output_batch, pred)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.contrib.opt.AdamWOptimizer(0).minimize(
objectives.get_loss())
# tfsession -----------------------------------------------------------
# -----------------------------------------------------------------------
# Operation for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = dl_multi.tftools.tfsaver.Saver(tf.train.Saver(),
**param_save,
logger=_logger)
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# Iteration over epochs
for epoch in saver:
stats_epoch, _ = sess.run([objectives.get_stats(), train_step])
print(objectives.get_stats_str(epoch._index, stats_epoch))
saver.save(sess, checkpoint, step=True)
coord.request_stop()
coord.join(threads)
saver.save(sess, checkpoint)
def eval(files, param_specs, param_io, param_log, param_eval, param_label,
param_class):
_logger.info(
"Start training multi task classification and regression model with settings:\nparam_io:\t{}\nparam_log:\t{}\nparam_eval:\t{}\nparam_label:\t{}\nparam_class:\t{}"
.format(param_io, param_log, param_eval, param_label, param_class))
# settings ------------------------------------------------------------
# -----------------------------------------------------------------------
img_in, img_out, log_out, _ = dl_multi.utils.imgio.get_data(
files,
param_specs,
param_io,
param_log=param_log,
param_label=param_label)
# Create the log and checkpoint folders if they do not exist
checkpoint = glu.Folder().set_folder(**param_eval["checkpoint"])
log_file = glu.Folder().set_folder(**param_log)
tasks = len(param_eval["objective"]) if isinstance(param_eval["objective"],
list) else 1
eval_obj = dl_multi.metrics.metrics.Metrics(
param_eval["objective"],
len(img_in),
categories=len(param_label),
labels=list(param_label.values()),
label_spec=param_class,
sklearn=glu.get_value(param_eval, "sklearn", True),
logger=_logger)
time_obj_img = dl_multi.utils.time.MTime(number=len(img_in), label="IMAGE")
# execution -------------------------------------------------------
# -------------------------------------------------------------------
for item, time_img, eval_img in zip(img_in, time_obj_img, eval_obj):
img = dl_multi.plugin.get_module_task(
"tftools", param_eval["input"]["method"],
"normalization")(item.spec("image").data,
**param_eval["input"]["param"])
truth = [
dl_multi.plugin.get_module_task(
"tftools", param_eval["output"][task]["method"],
"normalization")(imgtools.expand_image_dim(
item.spec(param_eval["truth"][task]).data,
**param_eval["output"][task]["param"]))
for task in range(tasks)
]
patches = dl_multi.utils.patches.Patches(img,
obj=param_eval["objective"],
categories=len(param_label),
limit=param_eval["limit"],
margin=param_eval["margin"],
pad=param_eval["pad"],
stitch=param_eval["stitch"],
logger=_logger)
for patch in patches:
patch.status()
tf.reset_default_graph()
tf.Graph().as_default()
data = tf.expand_dims(patch.get_image_patch(), 0)
with tf.variable_scope("net", reuse=tf.AUTO_REUSE):
pred = dl_multi.plugin.get_module_task(
"models", *param_eval["model"])(data)
# tfsession ---------------------------------------------------
# ---------------------------------------------------------------
# Operation for initializing the variables.
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, checkpoint)
sess.graph.finalize()
model_out = sess.run([pred])
patch.set_patch([model_out[0]])
patch.time()
# tfsession ---------------------------------------------------
# ---------------------------------------------------------------
# output --------------------------------------------------------------
# -----------------------------------------------------------------------
label = item.spec(glu.get_value(
param_eval, "truth_label", None)).data if glu.get_value(
param_eval, "truth_label", None) else None
for task in range(tasks):
img_out(item.spec(param_eval["truth"][task]).path,
patches.get_img(task=task),
prefix=param_eval["truth"][task])
eval_img.update(truth,
[patches.get_img(task=task) for task in range(tasks)],
label=label)
eval_obj.write_log([
log_out(item.spec(param_eval["truth"][task]).log,
prefix=param_eval["truth"][task]) for task in range(tasks)
],
write="w+",
current=True,
verbose=True)
print(eval_img.print_current_stats())
time_img.stop()
_logger.info(time_img.overall())
_logger.info(time_img.stats())
eval_obj.write_log(log_file, verbose=True)
print(eval_obj)
def set_cuda_properties(param):
if not param:
return
set_cuda_visible_devices(glu.get_value(param, "visible_devices", None))
