def write_to_file(self, f):
"""Write a BatchFile to a file object.
f -- the file to write to (needs to support write)
"""
write_record(f, RecordTypes.BESTANDSVOORLOOP, **self.recordargs)
for i, batch in enumerate(self.batches):
batch.write_to_file(f, i+1) # i+1 -- people start counting at 1
write_record(f, RecordTypes.BESTANDSSLUIT)
def vis_one(vis_graph, filepath, samples, predictions, sv):
movefile(filepath)
record.write_record(None, reader)
with sv.managed_session('', start_standard_services=False) as sess:
sv.saver.restore(sess, tf.train.latest_checkpoint(A_checkpoint_dir))
a = sv.start_queue_runners(sess)
save_dir = os.path.join(work_dir, _SEMANTIC_PREDICTION_SAVE_FOLDER)
my_process_batch(sess, samples[common.ORIGINAL_IMAGE], predictions,
samples[common.IMAGE_NAME], samples[common.HEIGHT],
samples[common.WIDTH], save_dir)
def serve(filepath):
global reader_graph
movefile(filepath)
# tf.reset_default_graph()
get_reader()
with reader_graph.as_default():
record.write_record(None, get_reader())
# input('record finish')
vis_graph = tf.Graph()
vis_graph.as_default()
# with vis_graph.as_default():
# -------------------------------------------
samples, predictions = do_prepare()
tf.train.get_or_create_global_step()
saver = tf.train.Saver(slim.get_variables_to_restore())
# supervisor = tf.train.Supervisor(graph=vis_graph,
supervisor = tf.train.Supervisor(init_op=tf.global_variables_initializer(),
summary_op=None,
summary_writer=None,
global_step=None,
saver=saver)
with supervisor.managed_session(A_master,
start_standard_services=False) as sess:
supervisor.start_queue_runners(sess)
my_checkpoint = tf.train.latest_checkpoint(A_checkpoint_dir)
supervisor.saver.restore(sess, my_checkpoint)
print(my_checkpoint)
do_process_batch(sess, samples, predictions)
skel_extract.extract()
skel_extract.load()
# graph.finalize()
vis_graph.finalize()
def write_to_file(self, f, index):
"""Write a Batch to a file object.
f -- the file to write to (needs to support write)
index -- index number of this batch
"""
recordargs = self.recordargs
recordargs.update({
'batchvolgnummer': index,
'totaalbedrag': 0,
'totaalrekeningen': 0,
'aantalposten': 0,
})
write_record(f, RecordTypes.BATCHVOORLOOP, **recordargs)
try:
self.description.write_to_file(f)
except AttributeError:
pass
write_record(f, RecordTypes.OPDRACHTGEVER, **recordargs)
# TODO: Loop over transactions
write_record(f, RecordTypes.BATCHSLUIT, **recordargs)
def process_one(filepath):
global reader_graph
global vis_graph
movefile(filepath)
with reader_graph.as_default():
record.write_record(None, get_reader())
# with get_prepare_graph().as_default():
# samples, predictions = do_prepare()
#
# with vis_graph.as_default():
# tf.train.get_or_create_global_step()
# sv = get_supervisor()
# with sv.managed_session(A_master, start_standard_services=False) as sess:
# do_process_batch(sess, samples, predictions)
# skel_extract.extract()
# skel_extract.load()
with vis_graph.as_default():
with get_session().as_default():
samples, predictions = do_prepare()
tf.train.get_or_create_global_step()
do_process_batch(get_session(), samples, predictions)
def process_one(filepath):
movefile(filepath)
record.write_record(None, reader)
# with get_prepare_graph().as_default():
# samples, predictions = do_prepare()
#
# with vis_graph.as_default():
# tf.train.get_or_create_global_step()
# sv = get_supervisor()
# with sv.managed_session(A_master, start_standard_services=False) as sess:
# do_process_batch(sess, samples, predictions)
# skel_extract.extract()
# skel_extract.load()
vis_graph = tf.Graph()
with vis_graph.as_default():
dataset = segmentation_dataset.get_dataset(A_dataset,
A_vis_split,
dataset_dir=A_dataset_dir)
samples = input_generator.get(dataset,
A_vis_crop_size,
A_vis_batch_size,
min_resize_value=A_min_resize_value,
max_resize_value=A_max_resize_value,
resize_factor=A_resize_factor,
dataset_split=A_vis_split,
is_training=False,
model_variant=A_model_variant)
model_options = mycommon.ModelOptions(
outputs_to_num_classes={common.OUTPUT_TYPE: dataset.num_classes},
crop_size=A_vis_crop_size,
atrous_rates=A_atrous_rates,
output_stride=A_output_stride)
print(samples[common.IMAGE])
predictions = model.predict_labels(samples[common.IMAGE],
model_options=model_options,
image_pyramid=A_image_pyramid)
predictions = predictions[common.OUTPUT_TYPE]
tf.train.get_or_create_global_step()
vis_session = tf.Session(graph=vis_graph)
saver = tf.train.Saver(slim.get_variables_to_restore())
sv = tf.train.Supervisor(graph=vis_graph,
logdir=A_vis_logdir,
init_op=tf.global_variables_initializer(),
summary_op=None,
summary_writer=None,
global_step=None,
saver=saver)
with sv.managed_session('', start_standard_services=False) as sess:
sv.start_queue_runners(sess)
sv.saver.restore(sess,
tf.train.latest_checkpoint(A_checkpoint_dir))
#samples, predictions = do_prepare()
#tf.train.get_or_create_global_step()
#do_process_batch(get_session(), samples, predictions)
save_dir = os.path.join(work_dir, _SEMANTIC_PREDICTION_SAVE_FOLDER)
my_process_batch(sess, samples[common.ORIGINAL_IMAGE], predictions,
samples[common.IMAGE_NAME],
samples[common.HEIGHT], samples[common.WIDTH],
save_dir)
