def recognize(self):
'''perform the recognition'''
with self.graph.as_default():
#create a hook that will load the model
load_hook = LoadAtBegin(
os.path.join(self.expdir, 'model', 'network.ckpt'),
self.model.variables)
#create a hook for summary writing
summary_hook = SummaryHook(os.path.join(self.expdir, 'logdir'))
directory = os.path.join(self.expdir, 'decoded')
if os.path.isdir(directory):
shutil.rmtree(directory)
os.makedirs(directory)
#start the session
with tf.train.SingularMonitoredSession(
hooks=[load_hook, summary_hook]) as sess:
nameid = 0
for _ in range(self.numbatches):
#decode
outputs = sess.run(self.decoded)
#write to disk
names = self.names[nameid:nameid + self.batch_size]
#cut of the added index to the name
names = ['-'.join(name.split('-')[:-1]) for name in names]
self.decoder.write(outputs, directory, names)
nameid += self.batch_size
def recognize(self):
'''perform the recognition'''
with self.graph.as_default():
#create a hook that will load the model
load_hook = LoadAtBegin(
os.path.join(self.expdir, 'model', 'network.ckpt'),
self.model.variables)
directory = os.path.join(self.expdir, 'decoded')
if os.path.isdir(directory):
shutil.rmtree(directory)
os.makedirs(directory)
#start the session
with tf.train.SingularMonitoredSession(hooks=[load_hook]) as sess:
summary_writer = tf.summary.FileWriter(
os.path.join(self.expdir, 'logdir'))
summary = self.variable_summary.eval(session=sess)
summary_writer.add_summary(summary)
nameid = 0
for i in range(self.numbatches):
if self.eval_summary is not None:
#decode
outputs, summary = sess.run(
[self.decoded, self.eval_summary])
summary_writer.add_summary(summary, i)
else:
outputs = sess.run(self.decoded)
#write to disk
names = self.names[nameid:nameid + self.batch_size]
#cut of the added index to the name
names = ['-'.join(name.split('-')[:-1]) for name in names]
self.decoder.write(outputs, directory, names)
nameid += self.batch_size
def test(expdir, test_model_checkpoint, task):
"""does everything for testing"""
# read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.cfg'))
# read the model config file
model_cfg = configparser.ConfigParser()
model_cfg.read(os.path.join(expdir, 'model.cfg'))
# read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'evaluator.cfg'))
losses_cfg_file = os.path.join(expdir, 'loss.cfg')
if not os.path.isfile(losses_cfg_file):
warnings.warn(
'In following versions it will be required to provide a loss config file',
Warning)
loss_cfg = None
else:
loss_cfg = configparser.ConfigParser()
loss_cfg.read(losses_cfg_file)
if evaluator_cfg.has_option(task, 'output_handling_type'):
output_handling_type = evaluator_cfg.get(task, 'output_handling_type')
else:
output_handling_type = 'reconstructor'
if output_handling_type == 'reconstructor':
# read the reconstructor config file
output_handler_cfg = configparser.ConfigParser()
output_handler_cfg.read(os.path.join(expdir, 'reconstructor.cfg'))
rec_dir = os.path.join(expdir, 'reconstructions', task)
# read the scorer config file
scorer_cfg = configparser.ConfigParser()
scorer_cfg.read(os.path.join(expdir, 'scorer.cfg'))
elif output_handling_type == 'speaker_verification':
# read the speaker verification output handler config file
output_handler_cfg = configparser.ConfigParser()
output_handler_cfg.read(
os.path.join(expdir, 'speaker_verification_handler.cfg'))
store_dir = os.path.join(expdir, 'speaker_verification_data', task)
# read the scorer config file
scorer_cfg = configparser.ConfigParser()
scorer_cfg.read(os.path.join(expdir,
'speaker_verification_scorer.cfg'))
else:
raise BaseException('Unknown output handling type: %s' %
output_handling_type)
# read the postprocessor config file, if it exists
try:
postprocessor_cfg = configparser.ConfigParser()
postprocessor_cfg.read(os.path.join(expdir, 'postprocessor.cfg'))
if not postprocessor_cfg.sections():
postprocessor_cfg = None
except:
postprocessor_cfg = None
# load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
models = pickle.load(fid)
if \
'/esat/spchtemp/scratch/jzegers/Nabu-SS2.0/Default17_MERL_DANet_Drude2018_sum_task_losses_sweep' in expdir or \
'/esat/spchtemp/scratch/jzegers/Nabu-SS2.0/Default17_MERL_DANet_Drude2018_acc_step_norm_weights_sweep' in expdir:
models['speaker_embeddings_model'].conf['no_bias'] = 'True'
models['outlayer'].conf['no_bias'] = 'True'
models['id_outlayer'].conf['no_bias'] = 'True'
with open(os.path.join(expdir, 'model', 'model.pkl'), 'wb') as fid2:
pickle.dump(models, fid2)
elif \
'/esat/spchtemp/scratch/jzegers/Nabu-SS2.0/Default17_SREMix_101trspks_DANet_hamming_scipy_Drude2018' in expdir:
models['speaker_embeddings_model'].conf['no_bias'] = 'True'
models['outlayer'].conf['no_bias'] = 'True'
models['id_outlayer'].conf['no_bias'] = 'False'
with open(os.path.join(expdir, 'model', 'model.pkl'), 'wb') as fid2:
pickle.dump(models, fid2)
if os.path.isfile(os.path.join(expdir, 'loss_%s' % task)):
print 'Already reconstructed all signals for task %s, going straight to scoring' % task
if evaluator_cfg.has_option(task, 'requested_utts'):
requested_utts = int(evaluator_cfg.get(task, 'requested_utts'))
else:
requested_utts = int(
evaluator_cfg.get('evaluator', 'requested_utts'))
if evaluator_cfg.has_option(task, 'batch_size'):
batch_size = int(evaluator_cfg.get(task, 'batch_size'))
else:
batch_size = int(evaluator_cfg.get('evaluator', 'batch_size'))
numbatches = int(float(requested_utts) / float(batch_size))
else:
print 'Evaluating task %s' % task
# create the evaluator
if loss_cfg:
loss_cfg = dict(
loss_cfg.items(evaluator_cfg.get(task, 'loss_type')))
evaltype = evaluator_cfg.get(task, 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(conf=evaluator_cfg,
lossconf=loss_cfg,
dataconf=database_cfg,
models=models,
task=task)
checkpoint_dir = os.path.join(expdir, 'logdir_%s' % task)
# create the output handler
if output_handling_type == 'reconstructor':
# create the reconstructor
task_output_handler_cfg = dict(output_handler_cfg.items(task))
reconstruct_type = task_output_handler_cfg['reconstruct_type']
# whether the targets should be used to determine the optimal speaker permutation on frame level. Should
# only be used for analysis and not for reporting results.
if 'optimal_frame_permutation' in task_output_handler_cfg and \
task_output_handler_cfg['optimal_frame_permutation'] == 'True':
optimal_frame_permutation = True
else:
optimal_frame_permutation = False
output_handler = reconstructor_factory.factory(reconstruct_type)(
conf=task_output_handler_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
rec_dir=rec_dir,
task=task,
optimal_frame_permutation=optimal_frame_permutation)
if optimal_frame_permutation:
opt_frame_perm_op = getattr(
output_handler, "reconstruct_signals_opt_frame_perm", None)
if not callable(opt_frame_perm_op):
raise NotImplementedError(
'The "optimal_frame_permutation" flag was set while the function '
'"reconstruct_signals_opt_frame_perm" is not implemented in the reconstructor'
)
elif output_handling_type == 'speaker_verification':
task_output_handler_cfg = dict(output_handler_cfg.items(task))
speaker_verification_handler_type = task_output_handler_cfg[
'speaker_verification_handler_type']
output_handler = speaker_verification_handler_factory.factory(
speaker_verification_handler_type)(
conf=task_output_handler_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
store_dir=store_dir,
exp_dir=expdir,
task=task)
else:
raise BaseException('Unknown output handling type: %s' %
output_handling_type)
# create the graph
with tf.Graph().as_default():
# create a hook that will load the model
load_hook = LoadAtBegin(test_model_checkpoint, models)
# create a hook for summary writing
# summary_hook = SummaryHook(os.path.join(expdir, 'logdir'))
#
saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir=checkpoint_dir,
save_steps=np.ceil(1000.0 / float(evaluator.batch_size)))
config = tf.ConfigProto(intra_op_parallelism_threads=6,
inter_op_parallelism_threads=2,
device_count={
'CPU': 8,
'GPU': 0
})
options = tf.RunOptions()
options.report_tensor_allocations_upon_oom = True
#
current_batch_ind_tf = tf.get_variable(
name='global_step',
shape=[],
dtype=tf.int32,
initializer=tf.constant_initializer(0),
trainable=False)
current_batch_ind_inc_op = current_batch_ind_tf.assign_add(1)
reset_current_batch_ind_op = current_batch_ind_tf.assign(0)
# get the current batch_ind
with tf.train.SingularMonitoredSession(
config=config, checkpoint_dir=checkpoint_dir) as sess:
start_batch_ind = sess.run(current_batch_ind_tf)
start_utt_ind = start_batch_ind * evaluator.batch_size
output_handler.pos = start_utt_ind
output_handler.open_scp_files(from_start=start_utt_ind == 0)
# compute the loss
batch_loss, batch_norm, numbatches, batch_outputs, batch_targets, batch_seq_length = evaluator.evaluate(
start_utt_ind=start_utt_ind)
# only keep the outputs requested by the reconstructor (usually the output of the output layer)
batch_outputs = {
out_name: out
for out_name, out in batch_outputs.iteritems()
if out_name in output_handler.requested_output_names
}
batch_seq_length = {
seq_name: seq
for seq_name, seq in batch_seq_length.iteritems()
if seq_name in output_handler.requested_output_names
}
hooks = [load_hook]
# hooks = [load_hook, summary_hook]
if numbatches > 100:
hooks.append(saver_hook)
# start the session
with tf.train.SingularMonitoredSession(
hooks=hooks, config=config,
checkpoint_dir=checkpoint_dir) as sess:
loss = 0.0
loss_norm = 0.0
for batch_ind in range(start_batch_ind, numbatches):
print('evaluating batch number %d' % batch_ind)
last_time = time.time()
[
batch_loss_eval, batch_norm_eval, batch_outputs_eval,
batch_targets_eval, batch_seq_length_eval
] = sess.run(fetches=[
batch_loss, batch_norm, batch_outputs, batch_targets,
batch_seq_length
],
options=options)
loss += batch_loss_eval
loss_norm += batch_norm_eval
print('%f' % (time.time() - last_time))
last_time = time.time()
if output_handling_type != 'reconstructor' or not optimal_frame_permutation:
output_handler(batch_outputs_eval,
batch_seq_length_eval)
else:
output_handler.opt_frame_perm(batch_outputs_eval,
batch_targets_eval,
batch_seq_length_eval)
sess.run(current_batch_ind_inc_op)
print('%f' % (time.time() - last_time))
loss = loss / loss_norm
print('task %s: loss = %0.6g' % (task, loss))
# write the loss to disk
with open(os.path.join(expdir, 'loss_%s' % task), 'w') as fid:
fid.write(str(loss))
if hasattr(output_handler, 'scp_file'):
output_handler.scp_fid.close()
if hasattr(output_handler, 'masks_pointer_file'):
output_handler.masks_pointer_fid.close()
if os.path.isdir(checkpoint_dir):
try:
os.rmdir(checkpoint_dir)
except:
pass
# from here on there is no need for a GPU anymore ==> score script to be run separately on
# different machine?
if evaluator_cfg.has_option(task, 'scorers_names'):
scorers_names = evaluator_cfg.get(task, 'scorers_names').split(' ')
else:
scorers_names = [task]
for scorer_name in scorers_names:
task_scorer_cfg = dict(scorer_cfg.items(scorer_name))
score_types = task_scorer_cfg['score_type'].split(' ')
for score_type in score_types:
if os.path.isfile(
os.path.join(
expdir, 'results_%s_%s_complete.json' %
(scorer_name, score_type))):
print(
'Already found a score for score task %s for score type %s, skipping it.'
% (scorer_name, score_type))
else:
print('Scoring task %s for score type %s' %
(scorer_name, score_type))
checkpoint_file = os.path.join(
expdir,
'checkpoint_results_%s_%s' % (scorer_name, score_type))
if output_handling_type == 'reconstructor':
# create the scorer
scorer = scorer_factory.factory(score_type)(
conf=task_scorer_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
rec_dir=rec_dir,
numbatches=numbatches,
task=task,
scorer_name=scorer_name,
checkpoint_file=checkpoint_file)
elif output_handling_type == 'speaker_verification':
# create the scorer
scorer = speaker_verification_scorer_factory.factory(
score_type)(conf=task_scorer_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
store_dir=store_dir,
numbatches=numbatches,
task=task,
scorer_name=scorer_name,
checkpoint_file=checkpoint_file)
# run the scorer
scorer()
result_summary = scorer.summarize()
with open(
os.path.join(
expdir, 'results_%s_%s_summary.json' %
(scorer_name, score_type)), 'w') as fid:
json.dump(result_summary, fid)
with open(
os.path.join(
expdir, 'results_%s_%s_complete.json' %
(scorer_name, score_type)), 'w') as fid:
json.dump(scorer.storable_result(), fid)
if os.path.isfile(checkpoint_file):
try:
os.remove(checkpoint_file)
except:
pass
# legacy code to be removed
if postprocessor_cfg != None: # && postprocessing is not done yet for this task
from nabu.postprocessing.postprocessors import postprocessor_factory
if evaluator_cfg.has_option(task, 'postprocessors_names'):
postprocessors_names = evaluator_cfg.get(
task, 'postprocessors_names').split(' ')
else:
postprocessors_names = [task]
for postprocessors_name in postprocessors_names:
task_postprocessor_cfg = dict(
postprocessor_cfg.items(postprocessors_name))
postprocess_types = task_postprocessor_cfg[
'postprocess_type'].split(' ')
for postprocess_type in postprocess_types:
print('Postprocessing task %s for postprocessor type %s' %
(postprocessors_name, postprocess_type))
# create the postprocessor
postprocessor = postprocessor_factory.factory(
postprocess_type)(conf=task_postprocessor_cfg,
evalconf=evaluator_cfg,
expdir=expdir,
rec_dir=rec_dir,
postprocessors_name=postprocessors_name)
# run the postprocessor
postprocessor()
postprocessor.matlab_eng.quit()
def test(expdir):
'''does everything for testing'''
#read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.cfg'))
#read the model config file
model_cfg = configparser.ConfigParser()
model_cfg.read(os.path.join(expdir, 'model.cfg'))
#read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'evaluator.cfg'))
#quick fix
#evaluator_cfg.set('evaluator','batch_size','5')
#read the reconstructor config file
reconstructor_cfg = configparser.ConfigParser()
reconstructor_cfg.read(os.path.join(expdir, 'reconstructor.cfg'))
#read the scorer config file
scorer_cfg = configparser.ConfigParser()
scorer_cfg.read(os.path.join(expdir, 'scorer.cfg'))
#read the postprocessor config file, if it exists
try:
postprocessor_cfg = configparser.ConfigParser()
postprocessor_cfg.read(os.path.join(expdir, 'postprocessor.cfg'))
if not postprocessor_cfg.sections():
postprocessor_cfg = None
except:
postprocessor_cfg = None
postprocessor_cfg = None
if evaluator_cfg.get('evaluator', 'evaluator') == 'multi_task':
tasks = evaluator_cfg.get('evaluator', 'tasks').split(' ')
else:
raise 'unkown type of evaluation %s' % evaluator_cfg.get(
'evaluator', 'evaluator')
#evaluate each task separately
for task in tasks:
rec_dir = os.path.join(expdir, 'reconstructions', task)
#load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
models = pickle.load(fid)
if os.path.isfile(os.path.join(expdir, 'loss_%s' % task)):
print 'already reconstructed all signals for task %s, going straight to scoring' % task
if evaluator_cfg.has_option(task, 'requested_utts'):
requested_utts = int(evaluator_cfg.get(task, 'requested_utts'))
else:
requested_utts = int(
evaluator_cfg.get('evaluator', 'requested_utts'))
if evaluator_cfg.has_option(task, 'batch_size'):
batch_size = int(evaluator_cfg.get(task, 'batch_size'))
else:
batch_size = int(evaluator_cfg.get('evaluator', 'batch_size'))
numbatches = int(float(requested_utts) / float(batch_size))
else:
print 'Evaluating task %s' % task
#create the evaluator
evaltype = evaluator_cfg.get(task, 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(
conf=evaluator_cfg,
dataconf=database_cfg,
models=models,
task=task)
#create the reconstructor
task_reconstructor_cfg = dict(reconstructor_cfg.items(task))
reconstruct_type = task_reconstructor_cfg['reconstruct_type']
reconstructor = reconstructor_factory.factory(reconstruct_type)(
conf=task_reconstructor_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
rec_dir=rec_dir,
task=task)
#create the graph
graph = tf.Graph()
with graph.as_default():
#compute the loss
batch_loss, batch_norm, numbatches, batch_outputs, batch_seq_length = evaluator.evaluate(
)
#create a hook that will load the model
load_hook = LoadAtBegin(
os.path.join(expdir, 'model', 'network.ckpt'), models)
#create a hook for summary writing
summary_hook = SummaryHook(os.path.join(expdir, 'logdir'))
config = tf.ConfigProto(device_count={'CPU': 1, 'GPU': 0})
options = tf.RunOptions()
options.report_tensor_allocations_upon_oom = True
#start the session
with tf.train.SingularMonitoredSession(
hooks=[load_hook,
summary_hook], config=config) as sess:
loss = 0.0
loss_norm = 0.0
for batch_ind in range(0, numbatches):
print 'evaluating batch number %d' % batch_ind
last_time = time.time()
[
batch_loss_eval, batch_norm_eval,
batch_outputs_eval, batch_seq_length_eval
] = sess.run(fetches=[
batch_loss, batch_norm, batch_outputs,
batch_seq_length
],
options=options)
loss += batch_loss_eval
loss_norm += batch_norm_eval
print '%f' % (time.time() - last_time)
last_time = time.time()
#chosing the first seq_length
reconstructor(batch_outputs_eval,
batch_seq_length_eval)
print '%f' % (time.time() - last_time)
loss = loss / loss_norm
print 'task %s: loss = %0.6g' % (task, loss)
#write the loss to disk
with open(os.path.join(expdir, 'loss_%s' % task), 'w') as fid:
fid.write(str(loss))
#from here on there is no need for a GPU anymore ==> score script to be run separately on
#different machine?
task_scorer_cfg = dict(scorer_cfg.items(task))
score_types = task_scorer_cfg['score_type'].split(' ')
for score_type in score_types:
if os.path.isfile(
os.path.join(
expdir,
'results_%s_%s_complete.json' % (task, score_type))):
print 'Already found a score for task %s for score type %s, skipping it.' % (
task, score_type)
else:
print 'Scoring task %s for score type %s' % (task, score_type)
#create the scorer
scorer = scorer_factory.factory(score_type)(
conf=task_scorer_cfg,
evalconf=evaluator_cfg,
dataconf=database_cfg,
rec_dir=rec_dir,
numbatches=numbatches,
task=task)
#run the scorer
scorer()
with open(
os.path.join(
expdir, 'results_%s_%s_complete.json' %
(task, score_type)), 'w') as fid:
json.dump(scorer.results, fid)
result_summary = scorer.summarize()
with open(
os.path.join(
expdir,
'results_%s_%s_summary.json' % (task, score_type)),
'w') as fid:
json.dump(result_summary, fid)
if postprocessor_cfg != None: # && postprocessing is not done yet for this task
task_postprocessor_cfg = dict(postprocessor_cfg.items(task))
task_processor_cfg = dict(
postprocessor_cfg.items('processor_' + task))
postprocess_types = task_postprocessor_cfg[
'postprocess_type'].split(' ')
for postprocess_type in postprocess_types:
#create the postprocessor
postprocessor = postprocessor_factory.factory(
postprocess_type)(conf=task_postprocessor_cfg,
proc_conf=task_processor_cfg,
evalconf=evaluator_cfg,
expdir=expdir,
rec_dir=rec_dir,
task=task)
#run the postprocessor
postprocessor()
postprocessor.matlab_eng.quit()
def test(expdir, testing=False):
'''does everything for testing
args:
expdir: the experiments directory
testing: if true only the graph will be created for debugging purposes
'''
#read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.cfg'))
if testing:
model_cfg = configparser.ConfigParser()
model_cfg.read(os.path.join(expdir, 'model.cfg'))
trainer_cfg = configparser.ConfigParser()
trainer_cfg.read(os.path.join(expdir, 'trainer.cfg'))
model = Model(conf=model_cfg,
trainlabels=int(trainer_cfg.get('trainer',
'trainlabels')),
constraint=None)
else:
#load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
model = pickle.load(fid)
#read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'test_evaluator.cfg'))
#create the evaluator
evaltype = evaluator_cfg.get('evaluator', 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(conf=evaluator_cfg,
dataconf=database_cfg,
model=model)
#create the graph
graph = tf.Graph()
with graph.as_default():
#compute the loss
loss, update_loss, numbatches = evaluator.evaluate()
if testing:
return
#create a histogram for all trainable parameters
for param in tf.trainable_variables():
tf.summary.histogram(param.name,
param,
collections=['variable_summaries'])
eval_summary = tf.summary.merge_all('eval_summaries')
variable_summary = tf.summary.merge_all('variable_summaries')
#create a hook that will load the model
load_hook = LoadAtBegin(os.path.join(expdir, 'model', 'network.ckpt'),
model.variables)
#start the session
with tf.train.SingularMonitoredSession(hooks=[load_hook]) as sess:
summary_writer = tf.summary.FileWriter(
os.path.join(expdir, 'logdir'))
summary = variable_summary.eval(session=sess)
summary_writer.add_summary(summary)
print 'TENSORFLOW ITEMS'
print '---Errors----'
op = sess.graph.get_operations()
print 'errors:'
test = tf.get_default_graph().get_tensor_by_name(
"evaluate/evaluate_decoder/Sum_1:0")
print test.eval(session=sess)
print 'new_num_targets'
test = tf.get_default_graph().get_tensor_by_name(
"evaluate/evaluate_decoder/add:0")
print test.eval(session=sess)
print 'batch_targets'
test = tf.get_default_graph().get_tensor_by_name(
"evaluate/evaluate_decoder/Sum_3:0")
print test.eval(session=sess)
print '--CTC DECODER ---'
print 'loss:'
test = tf.get_default_graph().get_tensor_by_name(
"validation_loss:0")
print test.eval(session=sess)
print 'outputs:'
print 'references:'
test = tf.get_default_graph().get_tensor_by_name(
"evaluate/input_pipeline/batch:2")
print test.eval(session=sess)
print 'references_seq_length'
test = tf.get_default_graph().get_tensor_by_name(
"evaluate/input_pipeline/batch:3")
print test.eval(session=sess)
for i in range(numbatches):
if eval_summary is not None:
_, summary = sess.run([update_loss, eval_summary])
summary_writer.add_summary(summary, i)
else:
update_loss.run(session=sess)
print 'loss: '
temploss = loss.eval(session=sess)
print temploss
loss = loss.eval(session=sess)
print 'loss = %f' % loss
#write the result to disk
with open(os.path.join(expdir, 'result'), 'w') as fid:
fid.write(str(loss))
def test(expdir):
'''does everything for testing'''
#read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.cfg'))
#load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
model = pickle.load(fid)
#read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'evaluator.cfg'))
#create the evaluator
evaltype = evaluator_cfg.get('evaluator', 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(
conf=evaluator_cfg,
dataconf=database_cfg,
model=model)
#create the reconstructor
reconstruct_type = evaluator_cfg.get('reconstructor', 'reconstruct_type')
reconstructor = reconstructor_factory.factory(reconstruct_type)(
conf=evaluator_cfg,
dataconf=database_cfg,
expdir=expdir)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#create the graph
graph = tf.Graph()
with graph.as_default():
#compute the loss
batch_loss, numbatches, batch_outputs, batch_seq_length = evaluator.evaluate()
#create a hook that will load the model
load_hook = LoadAtBegin(
os.path.join(expdir, 'model', 'network.ckpt'),
model)
#create a hook for summary writing
summary_hook = SummaryHook(os.path.join(expdir, 'logdir'))
#start the session
with tf.train.SingularMonitoredSession(
hooks=[load_hook, summary_hook]) as sess:
loss = 0.0
for batch_ind in range(0,numbatches):
print 'evaluating batch number %d' %batch_ind
batch_loss_eval, batch_outputs_eval, batch_seq_length_eval = sess.run(
fetches=[batch_loss, batch_outputs, batch_seq_length])
loss += batch_loss_eval
reconstructor(batch_outputs_eval['outputs'],
batch_seq_length_eval['features'])
loss = loss#/numbatches
print 'loss = %0.6g' % loss
#write the loss to disk
with open(os.path.join(expdir, 'loss'), 'w') as fid:
fid.write(str(loss))
#from here on there is no need for a GPU anymore ==> score script to be run separately on
#different machine? reconstructor.rec_dir has to be known though. can be put in evaluator_cfg
score_type = evaluator_cfg.get('scorer', 'score_type')
for i in range(10):
# Sometime it fails and not sure why. Just retry then. max 10 times
try:
#create the scorer
scorer = scorer_factory.factory(score_type)(
conf=evaluator_cfg,
dataconf=database_cfg,
rec_dir=reconstructor.rec_dir,
numbatches=numbatches)
#run the scorer
scorer()
except Exception:
if i==9:
raise Exception
else:
continue
break
with open(os.path.join(expdir, 'results_complete.json'), 'w') as fid:
json.dump(scorer.results,fid)
result_summary = scorer.summarize()
with open(os.path.join(expdir, 'results_summary.json'), 'w') as fid:
json.dump(result_summary,fid)
def test(expdir, testing=False):
'''does everything for testing
args:
expdir: the experiments directory
testing: if true only the graph will be created for debugging purposes
'''
#read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.conf'))
if testing:
model_cfg = configparser.ConfigParser()
model_cfg.read(os.path.join(expdir, 'model.cfg'))
trainer_cfg = configparser.ConfigParser()
trainer_cfg.read(os.path.join(expdir, 'trainer.cfg'))
model = Model(conf=model_cfg,
trainlabels=int(trainer_cfg.get('trainer',
'trainlabels')),
constraint=None)
else:
#load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
model = pickle.load(fid)
#read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'test_evaluator.cfg'))
#create the evaluator
evaltype = evaluator_cfg.get('evaluator', 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(conf=evaluator_cfg,
dataconf=database_cfg,
model=model)
#create the graph
graph = tf.Graph()
with graph.as_default():
#compute the loss
loss, update_loss, numbatches = evaluator.evaluate()
if testing:
return
#create a histogram for all trainable parameters
for param in tf.trainable_variables():
tf.summary.histogram(param.name,
param,
collections=['variable_summaries'])
eval_summary = tf.summary.merge_all('eval_summaries')
variable_summary = tf.summary.merge_all('variable_summaries')
#create a hook that will load the model
load_hook = LoadAtBegin(os.path.join(expdir, 'model', 'network.ckpt'),
model.variables)
#start the session
with tf.train.SingularMonitoredSession(hooks=[load_hook]) as sess:
summary_writer = tf.summary.FileWriter(
os.path.join(expdir, 'logdir'))
summary = variable_summary.eval(session=sess)
summary_writer.add_summary(summary)
for i in range(numbatches):
if eval_summary is not None:
_, summary = sess.run([update_loss, eval_summary])
summary_writer.add_summary(summary, i)
else:
update_loss.run(session=sess)
loss = loss.eval(session=sess)
print 'loss = %f' % loss
#write the result to disk
with open(os.path.join(expdir, 'result'), 'w') as fid:
fid.write(str(loss))
def test(expdir, testing=False):
'''does everything for testing
args:
expdir: the experiments directory
testing: if true only the graph will be created for debugging purposes
'''
#read the database config file
database_cfg = configparser.ConfigParser()
database_cfg.read(os.path.join(expdir, 'database.conf'))
if testing:
model_cfg = configparser.ConfigParser()
model_cfg.read(os.path.join(expdir, 'model.cfg'))
trainer_cfg = configparser.ConfigParser()
trainer_cfg.read(os.path.join(expdir, 'trainer.cfg'))
model = Model(conf=model_cfg,
trainlabels=int(trainer_cfg.get('trainer',
'trainlabels')))
else:
#load the model
with open(os.path.join(expdir, 'model', 'model.pkl'), 'rb') as fid:
model = pickle.load(fid)
#read the evaluator config file
evaluator_cfg = configparser.ConfigParser()
evaluator_cfg.read(os.path.join(expdir, 'test_evaluator.cfg'))
#create the evaluator
evaltype = evaluator_cfg.get('evaluator', 'evaluator')
evaluator = evaluator_factory.factory(evaltype)(conf=evaluator_cfg,
dataconf=database_cfg,
model=model)
#create the graph
graph = tf.Graph()
with graph.as_default():
#compute the loss
batch_loss, numbatches = evaluator.evaluate()
if testing:
return
#create a histogram for all trainable parameters
for param in model.variables:
tf.summary.histogram(param.name, param)
#create a hook that will load the model
load_hook = LoadAtBegin(os.path.join(expdir, 'model', 'network.ckpt'),
model.variables)
#create a hook for summary writing
summary_hook = SummaryHook(os.path.join(expdir, 'logdir'))
#start the session
with tf.train.SingularMonitoredSession(
hooks=[load_hook, summary_hook]) as sess:
loss = 0.0
for _ in range(numbatches):
loss += batch_loss.eval(session=sess)
loss = loss / numbatches
print 'loss = %f' % loss
#write the result to disk
with open(os.path.join(expdir, 'result'), 'w') as fid:
fid.write(str(loss))