def evaluation():
pbars = multi_pbars(
["Logits(batches)", "ctc(batches)", "logits(files)", "ctc(files)"])
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
predict = tf.nn.ctc_beam_search_decoder(tf.transpose(logits,
perm=[1, 0, 2]),
seq_length,
merge_repeated=False,
beam_width=FLAGS.beam)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
sess = tf.train.MonitoredSession(
session_creator=tf.train.ChiefSessionCreator(config=config))
if os.path.isdir(FLAGS.input):
file_list = os.listdir(FLAGS.input)
file_dir = FLAGS.input
else:
file_list = [os.path.basename(FLAGS.input)]
file_dir = os.path.abspath(os.path.join(FLAGS.input, os.path.pardir))
for file in file_list:
file_path = os.path.join(file_dir, file)
eval_data = read_data_for_eval(file_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
for i in range(0, reads_n, FLAGS.batch_size):
batch_x, seq_len, _ = eval_data.next_batch(FLAGS.batch_size,
shuffle=False,
sig_norm=False)
batch_x = np.pad(batch_x,
((0, FLAGS.batch_size - len(batch_x)), (0, 0)),
mode='constant')
seq_len = np.pad(seq_len, ((0, FLAGS.batch_size - len(seq_len))),
mode='constant')
feed_dict = {
x: batch_x,
seq_length: np.round(seq_len / ratio).astype(np.int32),
training: False,
}
logits_val, predict_val = sess.run([logits, predict],
feed_dict=feed_dict)
def output_list(x, seq_length):
training = tf.constant(False, dtype=tf.bool, name='Training')
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = read_config(config_path)
logits, ratio = inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
ratio = tf.constant(ratio, dtype=tf.float32, shape=[])
seq_length_r = tf.cast(
tf.round(tf.cast(seq_length, dtype=tf.float32) / ratio), tf.int32)
prob_logits = path_prob(logits)
predict, log_prob = tf.nn.ctc_beam_search_decoder(
tf.transpose(logits, perm=[1, 0, 2]),
seq_length_r,
merge_repeated=True,
beam_width=FLAGS.beam_width)
return predict[0], logits, prob_logits, log_prob
def input_output_list():
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
model_configure = chiron_model.read_config(FLAGS.config_path)
logits, _ = inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
predict = tf.nn.ctc_greedy_decoder(tf.transpose(logits, perm=[1, 0, 2]),
seq_length,
merge_repeated=True)
input_dict = {'x': x, 'seq_length': seq_length, 'training': training}
output_dict = {
'decoded_indices': predict[0][0].indices,
'decoded_values': predict[0][0].values,
'neg_sum_logits': predict[1]
}
return input_dict, output_dict
def evaluation():
pbars = multi_pbars(
["Logits(batches)", "ctc(batches)", "logits(files)", "ctc(files)"])
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
logits_index = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
logits_fname = tf.placeholder(tf.string, shape=[FLAGS.batch_size])
logits_queue = tf.FIFOQueue(
capacity=1000,
dtypes=[tf.float32, tf.string, tf.int32, tf.int32],
shapes=[
logits.shape, logits_fname.shape, logits_index.shape,
seq_length.shape
])
logits_queue_size = logits_queue.size()
logits_enqueue = logits_queue.enqueue(
(logits, logits_fname, logits_index, seq_length))
logits_queue_close = logits_queue.close()
### Decoding logits into bases
decode_predict_op, decode_prob_op, decoded_fname_op, decode_idx_op, decode_queue_size = decoding_queue(
logits_queue)
saver = tf.train.Saver(var_list=tf.trainable_variables() +
tf.moving_average_variables())
with tf.train.MonitoredSession(
session_creator=tf.train.ChiefSessionCreator(
config=config)) as sess:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model))
if os.path.isdir(FLAGS.input):
file_list = os.listdir(FLAGS.input)
file_dir = FLAGS.input
else:
file_list = [os.path.basename(FLAGS.input)]
file_dir = os.path.abspath(
os.path.join(FLAGS.input, os.path.pardir))
file_n = len(file_list)
pbars.update(2, total=file_n)
pbars.update(3, total=file_n)
if not os.path.exists(FLAGS.output):
os.makedirs(FLAGS.output)
if not os.path.exists(os.path.join(FLAGS.output, 'segments')):
os.makedirs(os.path.join(FLAGS.output, 'segments'))
if not os.path.exists(os.path.join(FLAGS.output, 'result')):
os.makedirs(os.path.join(FLAGS.output, 'result'))
if not os.path.exists(os.path.join(FLAGS.output, 'meta')):
os.makedirs(os.path.join(FLAGS.output, 'meta'))
def worker_fn():
batch_x = np.asarray([[]]).reshape(0, FLAGS.segment_len)
seq_len = np.asarray([])
logits_idx = np.asarray([])
logits_fn = np.asarray([])
for f_i, name in enumerate(file_list):
if not name.endswith('.signal'):
continue
input_path = os.path.join(file_dir, name)
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
pbars.update(0, total=reads_n, progress=0)
pbars.update_bar()
i = 0
while (eval_data.epochs_completed == 0):
current_batch, current_seq_len, _ = eval_data.next_batch(
FLAGS.batch_size - len(batch_x), shuffle=False)
current_n = len(current_batch)
batch_x = np.concatenate((batch_x, current_batch), axis=0)
seq_len = np.concatenate((seq_len, current_seq_len),
axis=0)
logits_idx = np.concatenate((logits_idx, [i] * current_n),
axis=0)
logits_fn = np.concatenate((logits_fn, [name] * current_n),
axis=0)
i += current_n
if len(batch_x) < FLAGS.batch_size:
pbars.update(0, progress=i)
pbars.update_bar()
continue
feed_dict = {
x: batch_x,
seq_length: np.round(seq_len / ratio).astype(np.int32),
training: False,
logits_index: logits_idx,
logits_fname: logits_fn,
}
#Training: Set it to True for a temporary fix of the batch normalization problem: https://github.com/haotianteng/Chiron/commit/8fce3a3b4dac8e9027396bb8c9152b7b5af953ce
#TODO: change the training FLAG back to False after the new model has been trained.
sess.run(logits_enqueue, feed_dict=feed_dict)
batch_x = np.asarray([[]]).reshape(0, FLAGS.segment_len)
seq_len = np.asarray([])
logits_idx = np.asarray([])
logits_fn = np.asarray([])
pbars.update(0, progress=i)
pbars.update_bar()
pbars.update(2, progress=f_i + 1)
pbars.update_bar()
### All files has been processed.
batch_n = len(batch_x)
if batch_n > 0:
pad_width = FLAGS.batch_size - batch_n
batch_x = np.pad(batch_x, ((0, pad_width), (0, 0)),
mode='wrap')
seq_len = np.pad(seq_len, ((0, pad_width)), mode='wrap')
logits_idx = np.pad(logits_idx, (0, pad_width),
mode='constant',
constant_values=-1)
logits_fn = np.pad(logits_fn, (0, pad_width),
mode='constant',
constant_values='')
sess.run(logits_enqueue,
feed_dict={
x: batch_x,
seq_length:
np.round(seq_len / ratio).astype(np.int32),
training: False,
logits_index: logits_idx,
logits_fname: logits_fn,
})
sess.run(logits_queue_close)
#
worker = threading.Thread(target=worker_fn, args=())
worker.setDaemon(True)
worker.start()
val = defaultdict(
dict) # We could read vals out of order, that's why it's a dict
for f_i, name in enumerate(file_list):
start_time = time.time()
if not name.endswith('.signal'):
continue
file_pre = os.path.splitext(name)[0]
input_path = os.path.join(file_dir, name)
if FLAGS.mode == 'rna':
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
else:
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
pbars.update(1, total=reads_n, progress=0)
pbars.update_bar()
reading_time = time.time() - start_time
reads = list()
if 'total_count' not in val[name].keys():
val[name]['total_count'] = 0
if 'index_list' not in val[name].keys():
val[name]['index_list'] = []
while True:
l_sz, d_sz = sess.run([logits_queue_size, decode_queue_size])
if val[name]['total_count'] == reads_n:
pbars.update(1, progress=val[name]['total_count'])
break
decode_ops = [
decoded_fname_op, decode_idx_op, decode_predict_op,
decode_prob_op
]
decoded_fname, i, predict_val, logits_prob = sess.run(
decode_ops, feed_dict={training: False})
decoded_fname = np.asarray(
[x.decode("UTF-8") for x in decoded_fname])
##Have difficulties integrate it into the tensorflow graph, as the number of file names in a batch is variable.
##And for loop can't be implemented as the eager execution is disabled due to the use of queue.
uniq_fname, uniq_fn_idx = np.unique(decoded_fname,
return_index=True)
for fn_idx, fn in enumerate(uniq_fname):
i = uniq_fn_idx[fn_idx]
if fn != '':
occurance = np.where(decoded_fname == fn)[0]
start = occurance[0]
end = occurance[-1] + 1
assert (len(occurance) == end - start)
if 'total_count' not in val[fn].keys():
val[fn]['total_count'] = 0
if 'index_list' not in val[fn].keys():
val[fn]['index_list'] = []
val[fn]['total_count'] += (end - start)
val[fn]['index_list'].append(i)
sliced_sparse = slice_ctc_decoding_result(
predict_val, start, end)
val[fn][i] = (sliced_sparse,
logits_prob[decoded_fname == fn])
pbars.update(1, progress=val[name]['total_count'])
pbars.update_bar()
pbars.update(3, progress=f_i + 1)
pbars.update_bar()
qs_list = np.empty((0, 1), dtype=np.float)
qs_string = None
for i in np.sort(val[name]['index_list']):
predict_val, logits_prob = val[name][i]
predict_read, unique = sparse2dense(predict_val)
predict_read = predict_read[0]
unique = unique[0]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[unique]
if FLAGS.extension == 'fastq':
qs_list = np.concatenate((qs_list, logits_prob))
reads += predict_read
val.pop(name) # Release the memory
basecall_time = time.time() - start_time
bpreads = [index2base(read) for read in reads]
js_ratio = FLAGS.jump / FLAGS.segment_len
kernal = get_assembler_kernal(FLAGS.jump, FLAGS.segment_len)
if FLAGS.extension == 'fastq':
consensus, qs_consensus = simple_assembly_qs(bpreads,
qs_list,
js_ratio,
kernal=kernal)
qs_string = qs(consensus, qs_consensus)
else:
consensus = simple_assembly(bpreads, js_ratio, kernal=kernal)
c_bpread = index2base(np.argmax(consensus, axis=0))
assembly_time = time.time() - start_time
list_of_time = [
start_time, reading_time, basecall_time, assembly_time
]
write_output(bpreads,
c_bpread,
list_of_time,
file_pre,
concise=FLAGS.concise,
suffix=FLAGS.extension,
q_score=qs_string,
global_setting=FLAGS)
pbars.end()
def chiron_train():
training = tf.placeholder(tf.bool)
global_step = tf.get_variable('global_step',
trainable=False,
shape=(),
dtype=tf.int32,
initializer=tf.zeros_initializer())
x = tf.placeholder(tf.float32,
shape=[FLAGS.batch_size, FLAGS.sequence_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
y_indexs = tf.placeholder(tf.int64)
y_values = tf.placeholder(tf.int32)
y_shape = tf.placeholder(tf.int64)
y = tf.SparseTensor(y_indexs, y_values, y_shape)
default_config = os.path.join(FLAGS.log_dir, FLAGS.model_name,
'model.json')
if FLAGS.retrain:
if os.path.isfile(default_config):
config_file = default_config
else:
raise ValueError(
"Model Json file has not been found in model log directory")
else:
config_file = FLAGS.configure
config = model.read_config(config_file)
logits, ratio = model.inference(x,
seq_length,
training,
FLAGS.sequence_len,
configure=config)
ctc_loss = model.loss(logits, seq_length, y)
opt = model.train_opt(FLAGS.step_rate,
FLAGS.max_steps,
global_step=global_step,
opt_name=config['opt_method'])
step = opt.minimize(ctc_loss, global_step=global_step)
error = model.prediction(logits, seq_length, y)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
summary = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
if FLAGS.retrain == False:
sess.run(init)
print("Model init finished, begin loading data. \n")
else:
saver.restore(
sess, tf.train.latest_checkpoint(FLAGS.log_dir + FLAGS.model_name))
print("Model loaded finished, begin loading data. \n")
summary_writer = tf.summary.FileWriter(
FLAGS.log_dir + FLAGS.model_name + '/summary/', sess.graph)
model.save_model(default_config, config)
train_ds, valid_ds = generate_train_valid_datasets()
start = time.time()
for i in range(FLAGS.max_steps):
batch_x, seq_len, batch_y = train_ds.next_batch(FLAGS.batch_size)
indxs, values, shape = batch_y
feed_dict = {
x: batch_x,
seq_length: seq_len / ratio,
y_indexs: indxs,
y_values: values,
y_shape: shape,
training: True
}
loss_val, _ = sess.run([ctc_loss, step], feed_dict=feed_dict)
if i % 10 == 0:
global_step_val = tf.train.global_step(sess, global_step)
valid_x, valid_len, valid_y = valid_ds.next_batch(FLAGS.batch_size)
indxs, values, shape = valid_y
feed_dict = {
x: valid_x,
seq_length: valid_len / ratio,
y_indexs: indxs,
y_values: values,
y_shape: shape,
training: True
}
error_val = sess.run(error, feed_dict=feed_dict)
end = time.time()
print(
"Step %d/%d Epoch %d, batch number %d, train_loss: %5.3f validate_edit_distance: %5.3f Elapsed Time/step: %5.3f" \
% (i, FLAGS.max_steps, train_ds.epochs_completed,
train_ds.index_in_epoch, loss_val, error_val,
(end - start) / (i + 1)))
saver.save(sess,
FLAGS.log_dir + FLAGS.model_name + '/model.ckpt',
global_step=global_step_val)
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str,
global_step=global_step_val)
summary_writer.flush()
global_step_val = tf.train.global_step(sess, global_step)
print("Model %s saved." % (FLAGS.log_dir + FLAGS.model_name))
print("Reads number %d" % (train_ds.reads_n))
saver.save(sess,
FLAGS.log_dir + FLAGS.model_name + '/final.ckpt',
global_step=global_step_val)
def train():
default_config = os.path.join(FLAGS.log_dir, FLAGS.model_name,
'model.json')
if FLAGS.retrain:
if os.path.isfile(default_config):
config_file = default_config
else:
raise ValueError(
"Model Json file has not been found in model log directory")
else:
config_file = FLAGS.configure
config = model.read_config(config_file)
print("Begin training using following setting:")
with open(os.path.join(FLAGS.log_dir, FLAGS.model_name, 'train_config'),
'w+') as log_f:
for pro in dir(FLAGS):
if not pro.startswith('_'):
print("%s:%s" % (pro, getattr(FLAGS, pro)))
log_f.write("%s:%s\n" % (pro, getattr(FLAGS, pro)))
net = compile_train_graph(config, FLAGS)
sess = tf.Session(
config=tf.ConfigProto(inter_op_parallelism_threads=FLAGS.threads,
intra_op_parallelism_threads=FLAGS.threads,
allow_soft_placement=True))
if FLAGS.retrain == False:
sess.run(net.init)
print("Model init finished, begin loading data. \n")
else:
net.saver.restore(
sess, tf.train.latest_checkpoint(FLAGS.log_dir + FLAGS.model_name))
print("Model loaded finished, begin loading data. \n")
summary_writer = tf.summary.FileWriter(
FLAGS.log_dir + FLAGS.model_name + '/summary/', sess.graph)
model.save_model(default_config, config)
train_ds, valid_ds = generate_train_valid_datasets(
initial_offset=DEFAULT_OFFSET)
start = time.time()
resample_n = 0
for i in range(FLAGS.max_steps):
if FLAGS.resample_after_epoch == 0:
pass
elif train_ds.epochs_completed >= FLAGS.resample_after_epoch:
train_ds, valid_ds = generate_train_valid_datasets(
initial_offset=resample_n * FLAGS.offset_increment +
DEFAULT_OFFSET)
batch_x, seq_len, batch_y = train_ds.next_batch(FLAGS.batch_size)
indxs, values, shape = batch_y
feed_dict = {
net.x: batch_x,
net.seq_length: seq_len / net.ratio,
net.y_indexs: indxs,
net.y_values: values,
net.y_shape: shape,
net.training: True
}
loss_val, _ = sess.run([net.ctc_loss, net.step], feed_dict=feed_dict)
if i % 10 == 0:
global_step_val = tf.train.global_step(sess, net.global_step)
valid_x, valid_len, valid_y = valid_ds.next_batch(FLAGS.batch_size)
indxs, values, shape = valid_y
feed_dict = {
net.x: valid_x,
net.seq_length: valid_len / net.ratio,
net.y_indexs: indxs,
net.y_values: values,
net.y_shape: shape,
net.training: True
}
error_val = sess.run(net.error, feed_dict=feed_dict)
# x_val,errors_val,y_predict,y = sess.run([x,errors,y_,y],feed_dict = feed_dict)
# predict_seq,_ = sparse2dense([y_predict,0])
# true_seq,_ = sparse2dense([[y],0])
end = time.time()
print(
"Step %d/%d Epoch %d, batch number %d, train_loss: %5.3f validate_edit_distance: %5.3f Elapsed Time/step: %5.3f" \
% (i, FLAGS.max_steps, train_ds.epochs_completed,
train_ds.index_in_epoch, loss_val, error_val,
(end - start) / (i + 1)))
net.saver.save(sess,
FLAGS.log_dir + FLAGS.model_name + '/model.ckpt',
global_step=global_step_val)
summary_str = sess.run(net.summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str,
global_step=global_step_val)
summary_writer.flush()
global_step_val = tf.train.global_step(sess, net.global_step)
print("Model %s saved." % (FLAGS.log_dir + FLAGS.model_name))
print("Reads number %d" % (train_ds.reads_n))
net.saver.save(sess,
FLAGS.log_dir + FLAGS.model_name + '/final.ckpt',
global_step=global_step_val)
def evaluation():
logger = logging.getLogger(__name__)
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
tqdm.monitor_interval = 0
logits_index = tf.placeholder(tf.int32, shape=())
logits_fname = tf.placeholder(tf.string, shape=())
logits_queue = tf.FIFOQueue(
capacity=1000,
dtypes=[tf.float32, tf.string, tf.int32, tf.int32],
shapes=[
logits.shape, logits_fname.shape, logits_index.shape,
seq_length.shape
])
logits_queue_size = logits_queue.size()
logits_enqueue = logits_queue.enqueue(
(logits, logits_fname, logits_index, seq_length))
logits_queue_close = logits_queue.close()
### Decoding logits into bases
decode_predict_op, decode_prob_op, decoded_fname_op, decode_idx_op, decode_queue_size = decoding_queue(
logits_queue)
saver = tf.train.Saver()
with tf.train.MonitoredSession(
session_creator=tf.train.ChiefSessionCreator(
config=config)) as sess:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model))
if os.path.isdir(FLAGS.input):
file_list = os.listdir(FLAGS.input)
file_dir = FLAGS.input
else:
file_list = [os.path.basename(FLAGS.input)]
file_dir = os.path.abspath(
os.path.join(FLAGS.input, os.path.pardir))
if not os.path.exists(FLAGS.output):
os.makedirs(FLAGS.output)
if not os.path.exists(os.path.join(FLAGS.output, 'segments')):
os.makedirs(os.path.join(FLAGS.output, 'segments'))
if not os.path.exists(os.path.join(FLAGS.output, 'result')):
os.makedirs(os.path.join(FLAGS.output, 'result'))
if not os.path.exists(os.path.join(FLAGS.output, 'meta')):
os.makedirs(os.path.join(FLAGS.output, 'meta'))
def worker_fn():
for name in tqdm(file_list, desc="Logits inferencing.",
position=0):
if not name.endswith('.signal'):
continue
input_path = os.path.join(file_dir, name)
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
for i in trange(0,
reads_n,
FLAGS.batch_size,
desc="Logits inferencing",
position=1):
batch_x, seq_len, _ = eval_data.next_batch(
FLAGS.batch_size, shuffle=False, sig_norm=False)
batch_x = np.pad(batch_x,
((0, FLAGS.batch_size - len(batch_x)),
(0, 0)),
mode='constant')
seq_len = np.pad(seq_len,
((0, FLAGS.batch_size - len(seq_len))),
mode='constant')
feed_dict = {
x: batch_x,
seq_length: seq_len,
training: False,
logits_index: i,
logits_fname: name,
}
sess.run(logits_enqueue, feed_dict=feed_dict)
sess.run(logits_queue_close)
def run_listener(write_lock):
# This function is used to solve the error when tqdm is used inside thread
# https://github.com/tqdm/tqdm/issues/323
tqdm.set_lock(write_lock)
worker_fn()
write_lock = threading.Lock()
worker = threading.Thread(target=run_listener, args=(write_lock, ))
worker.setDaemon(True)
worker.start()
val = defaultdict(
dict) # We could read vals out of order, that's why it's a dict
for name in tqdm(file_list, desc="CTC decoding.", position=2):
start_time = time.time()
if not name.endswith('.signal'):
continue
file_pre = os.path.splitext(name)[0]
input_path = os.path.join(file_dir, name)
if FLAGS.mode == 'rna':
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump,
reverse=True)
else:
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
reading_time = time.time() - start_time
reads = list()
N = len(range(0, reads_n, FLAGS.batch_size))
with tqdm(total=reads_n, desc="ctc decoding", position=3) as pbar:
while True:
options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
l_sz, d_sz = sess.run(
[logits_queue_size, decode_queue_size],
options=options,
run_metadata=run_metadata)
pbar.set_postfix(logits_q=l_sz,
decoded_q=d_sz,
refresh=False)
decode_ops = [
decoded_fname_op, decode_idx_op, decode_predict_op,
decode_prob_op
]
decoded_fname, i, predict_val, logits_prob = sess.run(
decode_ops,
feed_dict={training: False},
options=options,
run_metadata=run_metadata)
decoded_fname = decoded_fname.decode("UTF-8")
val[decoded_fname][i] = (predict_val, logits_prob)
fetched_timeline = timeline.Timeline(
run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format(
)
with open('timeline_02_step_%d.json' % i, 'w') as f:
f.write(chrome_trace)
if decoded_fname == name:
decoded_cnt = len(val[name])
pbar.update(
min(reads_n, decoded_cnt * FLAGS.batch_size) -
(decoded_cnt - 1) * FLAGS.batch_size)
if decoded_cnt == N:
break
qs_list = np.empty((0, 1), dtype=np.float)
qs_string = None
for i in trange(0,
reads_n,
FLAGS.batch_size,
desc="Output",
position=4):
predict_val, logits_prob = val[name][i]
predict_read, unique = sparse2dense(predict_val)
predict_read = predict_read[0]
unique = unique[0]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[unique]
if i + FLAGS.batch_size > reads_n:
predict_read = predict_read[:reads_n - i]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[:reads_n - i]
if FLAGS.extension == 'fastq':
qs_list = np.concatenate((qs_list, logits_prob))
reads += predict_read
val.pop(name) # Release the memory
# tqdm.write("[%s] Segment reads base calling finished, begin to assembly. %5.2f seconds" % (name, time.time() - start_time))
basecall_time = time.time() - start_time
bpreads = [index2base(read) for read in reads]
if FLAGS.extension == 'fastq':
consensus, qs_consensus = simple_assembly_qs(bpreads, qs_list)
qs_string = qs(consensus, qs_consensus)
else:
consensus = simple_assembly(bpreads)
c_bpread = index2base(np.argmax(consensus, axis=0))
np.set_printoptions(threshold=np.nan)
assembly_time = time.time() - start_time
# tqdm.write("[%s] Assembly finished, begin output. %5.2f seconds" % (name, time.time() - start_time))
list_of_time = [
start_time, reading_time, basecall_time, assembly_time
]
write_output(bpreads,
c_bpread,
list_of_time,
file_pre,
concise=FLAGS.concise,
suffix=FLAGS.extension,
q_score=qs_string)
def evaluation():
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = chiron_model.read_config(config_path)
net = build_eval_graph(model_configure)
val = defaultdict(
dict) # We could read vals out of order, that's why it's a dict
for f_i, name in enumerate(net.file_list):
start_time = time.time()
if (not name.endswith('.signal')) and (not name.endswith('.fast5')):
continue
file_pre = os.path.splitext(name)[0]
input_path = os.path.join(net.file_dir, name)
###Other mode (like methylation) may use different read method.
eval_data = read_data_for_eval(input_path,
FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump,
reverse_fast5=FLAGS.reverse_fast5)
reads_n = eval_data.reads_n
net.pbars.update(1, total=reads_n, progress=0)
net.pbars.update_bar()
reading_time = time.time() - start_time
reads = list()
if 'total_count' not in val[name].keys():
val[name]['total_count'] = 0
if 'index_list' not in val[name].keys():
val[name]['index_list'] = []
while True:
l_sz, d_sz = net.sess.run(
[net.logits_queue_size, net.decode_queue_size])
if val[name]['total_count'] == reads_n:
net.pbars.update(1, progress=val[name]['total_count'])
break
decode_ops = [
net.decoded_fname_op, net.decode_idx_op, net.decode_predict_op,
net.decode_prob_op
]
decoded_fname, i, predict_val, logits_prob = net.sess.run(
decode_ops, feed_dict={net.training: False})
decoded_fname = np.asarray(
[x.decode("UTF-8") for x in decoded_fname])
##Have difficulties integrate it into the tensorflow graph, as the number of file names in a batch is uncertain.
##And for loop can't be implemented as the eager execution is disabled due to the use of queue.
uniq_fname, uniq_fn_idx = np.unique(decoded_fname,
return_index=True)
for fn_idx, fn in enumerate(uniq_fname):
i = uniq_fn_idx[fn_idx]
if fn != '':
occurance = np.where(decoded_fname == fn)[0]
start = occurance[0]
end = occurance[-1] + 1
assert (len(occurance) == end - start)
if 'total_count' not in val[fn].keys():
val[fn]['total_count'] = 0
if 'index_list' not in val[fn].keys():
val[fn]['index_list'] = []
val[fn]['total_count'] += (end - start)
val[fn]['index_list'].append(i)
sliced_sparse = slice_ctc_decoding_result(
predict_val, start, end)
val[fn][i] = (sliced_sparse,
logits_prob[decoded_fname == fn])
net.pbars.update(1, progress=val[name]['total_count'])
net.pbars.update_bar()
net.pbars.update(3, progress=f_i + 1)
net.pbars.update_bar()
qs_list = np.empty((0, 1), dtype=np.float)
qs_string = None
for i in np.sort(val[name]['index_list']):
predict_val, logits_prob = val[name][i]
predict_read, unique = sparse2dense(predict_val)
predict_read = predict_read[0]
unique = unique[0]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[unique]
if FLAGS.extension == 'fastq':
qs_list = np.concatenate((qs_list, logits_prob))
reads += predict_read
val.pop(name) # Release the memory
basecall_time = time.time() - start_time
bpreads = [index2base(read) for read in reads]
js_ratio = FLAGS.jump / FLAGS.segment_len
kernal = get_assembler_kernal(FLAGS.jump, FLAGS.segment_len)
if FLAGS.extension == 'fastq':
consensus, qs_consensus = simple_assembly_qs(bpreads,
qs_list,
js_ratio,
kernal=kernal)
qs_string = qs(consensus, qs_consensus)
else:
consensus = simple_assembly(bpreads, js_ratio, kernal=kernal)
c_bpread = index2base(np.argmax(consensus, axis=0))
assembly_time = time.time() - start_time
list_of_time = [start_time, reading_time, basecall_time, assembly_time]
write_output(bpreads,
c_bpread,
list_of_time,
file_pre,
concise=FLAGS.concise,
suffix=FLAGS.extension,
q_score=qs_string,
global_setting=FLAGS)
net.pbars.end()
def train(hparams):
"""Main training function.
This will train a Neural Network with the given dataset.
Args:
hparams: hyper parameter for training the neural network
data-dir: String, the path of the data(binary batch files) directory.
log-dir: String, the path to save the trained model.
sequence-len: Int, length of input signal.
batch-size: Int.
step-rate: Float, step rate of the optimizer.
max-steps: Int, max training steps.
kmer: Int, size of the dna kmer.
model-name: String, model will be saved at log-dir/model-name.
retrain: Boolean, if True, the model will be reload from log-dir/model-name.
"""
with tf.Graph().as_default(), tf.device('/cpu:0'):
training = tf.placeholder(tf.bool)
global_step = tf.get_variable('global_step',
trainable=False,
shape=(),
dtype=tf.int32,
initializer=tf.zeros_initializer())
opt = model.train_opt(hparams.step_rate,
hparams.max_steps,
global_step=global_step)
x, seq_length, train_labels = inputs(hparams.data_dir,
int(hparams.batch_size *
hparams.ngpus),
for_valid=False)
split_y = tf.split(train_labels, hparams.ngpus, axis=0)
split_seq_length = tf.split(seq_length, hparams.ngpus, axis=0)
split_x = tf.split(x, hparams.ngpus, axis=0)
tower_grads = []
default_config = os.path.join(hparams.log_dir, hparams.model_name,
'model.json')
if hparams.retrain:
if os.path.isfile(default_config):
config_file = default_config
else:
raise ValueError(
"Model Json file has not been found in model log directory"
)
else:
config_file = hparams.configure
config = model.read_config(config_file)
with tf.variable_scope(tf.get_variable_scope()):
for i in range(hparams.ngpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % ('gpu_tower', i)) as scope:
loss, error = tower_loss(
scope,
split_x[i],
split_seq_length[i],
split_y[i],
full_seq_len=hparams.sequence_len,
config=config)
tf.get_variable_scope().reuse_variables()
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES,
scope)
grads = opt.compute_gradients(loss)
tower_grads.append(grads)
grads = average_gradients(tower_grads)
for grad, var in grads:
if grad is not None:
summaries.append(
tf.summary.histogram(var.op.name + '/gradients', grad))
for var in tf.trainable_variables():
summaries.append(tf.summary.histogram(var.op.name, var))
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
var_averages = tf.train.ExponentialMovingAverage(
decay=model.MOVING_AVERAGE_DECAY)
var_averages_op = var_averages.apply(tf.trainable_variables())
train_op = tf.group(apply_gradient_op, var_averages_op)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
summary = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
model.save_model(default_config, config)
if not hparams.retrain:
sess.run(init)
print("Model init finished, begin training. \n")
else:
saver.restore(
sess,
tf.train.latest_checkpoint(hparams.log_dir +
hparams.model_name))
print("Model loaded finished, begin training. \n")
summary_writer = tf.summary.FileWriter(
hparams.log_dir + hparams.model_name + '/summary/', sess.graph)
_ = tf.train.start_queue_runners(sess=sess)
start = time.time()
for i in range(hparams.max_steps):
feed_dict = {training: True}
loss_val, _ = sess.run([loss, train_op], feed_dict=feed_dict)
if i % 10 == 0:
global_step_val = tf.train.global_step(sess, global_step)
feed_dict = {training: True}
error_val = sess.run(error, feed_dict=feed_dict)
end = time.time()
print(
"Step %d/%d , loss: %5.3f edit_distance: %5.3f Elapsed Time/batch: %5.3f" \
% (i, hparams.max_steps, loss_val, error_val,
(end - start) / (i + 1)))
saver.save(sess,
hparams.log_dir + hparams.model_name +
'/model.ckpt',
global_step=global_step_val)
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str,
global_step=global_step_val)
summary_writer.flush()
global_step_val = tf.train.global_step(sess, global_step)
print("Model %s saved." % (hparams.log_dir + hparams.model_name))
saver.save(sess,
hparams.log_dir + hparams.model_name + '/final.ckpt',
global_step=global_step_val)
def evaluation():
pbars = multi_pbars(["Logits(batches)","ctc(batches)","logits(files)","ctc(files)"])
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model,'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(
x,
seq_length,
training=training,
full_sequence_len = FLAGS.segment_len,
configure = model_configure)
config = tf.ConfigProto(allow_soft_placement=True, intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
logits_index = tf.placeholder(tf.int32, shape=())
logits_fname = tf.placeholder(tf.string, shape=())
logits_queue = tf.FIFOQueue(
capacity=1000,
dtypes=[tf.float32, tf.string, tf.int32, tf.int32],
shapes=[logits.shape, logits_fname.shape, logits_index.shape, seq_length.shape]
)
logits_queue_size = logits_queue.size()
logits_enqueue = logits_queue.enqueue((logits, logits_fname, logits_index, seq_length))
logits_queue_close = logits_queue.close()
### Decoding logits into bases
decode_predict_op, decode_prob_op, decoded_fname_op, decode_idx_op, decode_queue_size = decoding_queue(logits_queue)
saver = tf.train.Saver()
with tf.train.MonitoredSession(session_creator=tf.train.ChiefSessionCreator(config=config)) as sess:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model))
if os.path.isdir(FLAGS.input):
file_list = os.listdir(FLAGS.input)
file_dir = FLAGS.input
else:
file_list = [os.path.basename(FLAGS.input)]
file_dir = os.path.abspath(
os.path.join(FLAGS.input, os.path.pardir))
file_n = len(file_list)
pbars.update(2,total = file_n)
pbars.update(3,total = file_n)
if not os.path.exists(FLAGS.output):
os.makedirs(FLAGS.output)
if not os.path.exists(os.path.join(FLAGS.output, 'segments')):
os.makedirs(os.path.join(FLAGS.output, 'segments'))
if not os.path.exists(os.path.join(FLAGS.output, 'result')):
os.makedirs(os.path.join(FLAGS.output, 'result'))
if not os.path.exists(os.path.join(FLAGS.output, 'meta')):
os.makedirs(os.path.join(FLAGS.output, 'meta'))
def worker_fn():
for f_i, name in enumerate(file_list):
if not name.endswith('.signal'):
continue
input_path = os.path.join(file_dir, name)
eval_data = read_data_for_eval(input_path, FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
pbars.update(0,total = reads_n,progress = 0)
pbars.update_bar()
for i in range(0, reads_n, FLAGS.batch_size):
batch_x, seq_len, _ = eval_data.next_batch(
FLAGS.batch_size, shuffle=False, sig_norm=False)
batch_x = np.pad(
batch_x, ((0, FLAGS.batch_size - len(batch_x)), (0, 0)), mode='constant')
seq_len = np.pad(
seq_len, ((0, FLAGS.batch_size - len(seq_len))), mode='constant')
feed_dict = {
x: batch_x,
seq_length: seq_len,
training: False,
logits_index:i,
logits_fname: name,
}
def train(hparam):
"""Main training function.
This will train a Neural Network with the given dataset.
Args:
hparam: hyper parameter for training the neural network
data_dir: String, the path of the data(binary batch files) directory.
log-dir: String, the path to save the trained model.
sequence-len: Int, length of input signal.
batch-size: Int.
step-rate: Float, step rate of the optimizer.
max-steps: Int, max training steps.
kmer: Int, size of the dna kmer.
model-name: String, model will be saved at log-dir/model-name.
retrain: Boolean, if True, the model will be reload from log-dir/model-name.
"""
training = tf.placeholder(tf.bool)
global_step = tf.get_variable('global_step',
trainable=False,
shape=(),
dtype=tf.int32,
initializer=tf.zeros_initializer())
x, seq_length, train_labels = inputs(hparam.data_dir,
hparam.batch_size,
hparam.sequence_len,
for_valid=False)
y = dense2sparse(train_labels)
default_config = os.path.join(hparam.log_dir, hparam.model_name,
'model.json')
if hparam.retrain:
if os.path.isfile(default_config):
config_file = default_config
else:
raise ValueError(
"Model Json file has not been found in model log directory")
else:
config_file = hparam.configure
config = model.read_config(config_file)
logits, ratio = model.inference(x,
seq_length,
training,
hparam.sequence_len,
configure=config,
apply_ratio=True)
seq_length = tf.cast(tf.ceil(tf.cast(seq_length, tf.float32) / ratio),
tf.int32)
ctc_loss = model.loss(logits, seq_length, y)
opt = model.train_opt(hparam.step_rate,
hparam.max_steps,
global_step=global_step)
step = opt.minimize(ctc_loss, global_step=global_step)
error = model.prediction(logits, seq_length, y)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
summary = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
model.save_model(default_config, config)
if not hparam.retrain:
sess.run(init)
print("Model init finished, begin training. \n")
else:
saver.restore(
sess,
tf.train.latest_checkpoint(hparam.log_dir + hparam.model_name))
print("Model loaded finished, begin training. \n")
summary_writer = tf.summary.FileWriter(
hparam.log_dir + hparam.model_name + '/summary/', sess.graph)
_ = tf.train.start_queue_runners(sess=sess)
start = time.time()
for i in range(hparam.max_steps):
feed_dict = {training: True}
loss_val, _ = sess.run([ctc_loss, step], feed_dict=feed_dict)
if i % 10 == 0:
global_step_val = tf.train.global_step(sess, global_step)
feed_dict = {training: True}
error_val = sess.run(error, feed_dict=feed_dict)
end = time.time()
print(
"Step %d/%d , loss: %5.3f edit_distance: %5.3f Elapsed Time/batch: %5.3f" \
% (i, hparam.max_steps, loss_val, error_val,
(end - start) / (i + 1)))
saver.save(sess,
hparam.log_dir + hparam.model_name + '/model.ckpt',
global_step=global_step_val)
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str,
global_step=global_step_val)
summary_writer.flush()
global_step_val = tf.train.global_step(sess, global_step)
print("Model %s saved." % (hparam.log_dir + hparam.model_name))
saver.save(sess,
hparam.log_dir + hparam.model_name + '/final.ckpt',
global_step=global_step_val)
def evaluation():
pbars = multi_pbars(["Logits(batches)","ctc(batches)","logits(files)","ctc(files)"])
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model,'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(
x,
seq_length,
training=training,
full_sequence_len = FLAGS.segment_len,
configure = model_configure)
config = tf.ConfigProto(allow_soft_placement=True, intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
logits_index = tf.placeholder(tf.int32, shape=())
logits_fname = tf.placeholder(tf.string, shape=())
logits_queue = tf.FIFOQueue(
capacity=1000,
dtypes=[tf.float32, tf.string, tf.int32, tf.int32],
shapes=[logits.shape,logits_fname.shape,logits_index.shape, seq_length.shape]
)
logits_queue_size = logits_queue.size()
logits_enqueue = logits_queue.enqueue((logits, logits_fname, logits_index, seq_length))
logits_queue_close = logits_queue.close()
### Decoding logits into bases
decode_predict_op, decode_prob_op, decoded_fname_op, decode_idx_op, decode_queue_size = decoding_queue(logits_queue)
saver = tf.train.Saver()
with tf.train.MonitoredSession(session_creator=tf.train.ChiefSessionCreator(config=config)) as sess:
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model))
if os.path.isdir(FLAGS.input):
file_list = os.listdir(FLAGS.input)
file_dir = FLAGS.input
else:
file_list = [os.path.basename(FLAGS.input)]
file_dir = os.path.abspath(
os.path.join(FLAGS.input, os.path.pardir))
file_n = len(file_list)
pbars.update(2,total = file_n)
pbars.update(3,total = file_n)
if not os.path.exists(FLAGS.output):
os.makedirs(FLAGS.output)
if not os.path.exists(os.path.join(FLAGS.output, 'segments')):
os.makedirs(os.path.join(FLAGS.output, 'segments'))
if not os.path.exists(os.path.join(FLAGS.output, 'result')):
os.makedirs(os.path.join(FLAGS.output, 'result'))
if not os.path.exists(os.path.join(FLAGS.output, 'meta')):
os.makedirs(os.path.join(FLAGS.output, 'meta'))
def worker_fn():
for f_i, name in enumerate(file_list):
if not name.endswith('.signal'):
continue
input_path = os.path.join(file_dir, name)
eval_data = read_data_for_eval(input_path, FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
pbars.update(0,total = reads_n,progress = 0)
pbars.update_bar()
for i in range(0, reads_n, FLAGS.batch_size):
batch_x, seq_len, _ = eval_data.next_batch(
FLAGS.batch_size, shuffle=False, sig_norm=False)
batch_x = np.pad(
batch_x, ((0, FLAGS.batch_size - len(batch_x)), (0, 0)), mode='constant')
seq_len = np.pad(
seq_len, ((0, FLAGS.batch_size - len(seq_len))), mode='constant')
feed_dict = {
x: batch_x,
seq_length: np.round(seq_len/ratio).astype(np.int32),
training: False,
logits_index:i,
logits_fname: name,
}
sess.run(logits_enqueue,feed_dict=feed_dict)
pbars.update(0,progress=i+FLAGS.batch_size)
pbars.update_bar()
pbars.update(2,progress = f_i+1)
pbars.update_bar()
sess.run(logits_queue_close)
worker = threading.Thread(target=worker_fn,args=() )
worker.setDaemon(True)
worker.start()
val = defaultdict(dict) # We could read vals out of order, that's why it's a dict
for f_i, name in enumerate(file_list):
start_time = time.time()
if not name.endswith('.signal'):
continue
file_pre = os.path.splitext(name)[0]
input_path = os.path.join(file_dir, name)
if FLAGS.mode == 'rna':
eval_data = read_data_for_eval(input_path, FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
else:
eval_data = read_data_for_eval(input_path, FLAGS.start,
seg_length=FLAGS.segment_len,
step=FLAGS.jump)
reads_n = eval_data.reads_n
pbars.update(1,total = reads_n,progress = 0)
pbars.update_bar()
reading_time = time.time() - start_time
reads = list()
N = len(range(0, reads_n, FLAGS.batch_size))
while True:
l_sz, d_sz = sess.run([logits_queue_size, decode_queue_size])
decode_ops = [decoded_fname_op, decode_idx_op, decode_predict_op, decode_prob_op]
decoded_fname, i, predict_val, logits_prob = sess.run(decode_ops, feed_dict={training: False})
decoded_fname = decoded_fname.decode("UTF-8")
val[decoded_fname][i] = (predict_val, logits_prob)
pbars.update(1,progress = len(val[name])*FLAGS.batch_size)
pbars.update_bar()
if len(val[name]) == N:
break
pbars.update(3,progress = f_i+1)
pbars.update_bar()
qs_list = np.empty((0, 1), dtype=np.float)
qs_string = None
for i in range(0, reads_n, FLAGS.batch_size):
predict_val, logits_prob = val[name][i]
predict_read, unique = sparse2dense(predict_val)
predict_read = predict_read[0]
unique = unique[0]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[unique]
if i + FLAGS.batch_size > reads_n:
predict_read = predict_read[:reads_n - i]
if FLAGS.extension == 'fastq':
logits_prob = logits_prob[:reads_n - i]
if FLAGS.extension == 'fastq':
qs_list = np.concatenate((qs_list, logits_prob))
reads += predict_read
val.pop(name) # Release the memory
basecall_time = time.time() - start_time
bpreads = [index2base(read) for read in reads]
js_ratio = FLAGS.jump/FLAGS.segment_len
if FLAGS.extension == 'fastq':
consensus, qs_consensus = simple_assembly_qs(bpreads, qs_list,js_ratio)
qs_string = qs(consensus, qs_consensus)
else:
consensus = simple_assembly(bpreads,js_ratio)
c_bpread = index2base(np.argmax(consensus, axis=0))
assembly_time = time.time() - start_time
list_of_time = [start_time, reading_time,
basecall_time, assembly_time]
write_output(bpreads, c_bpread, list_of_time, file_pre, concise=FLAGS.concise, suffix=FLAGS.extension,
q_score=qs_string,global_setting=FLAGS)
pbars.end()
action='store_true',
help=
"Concisely output the result, the meta and segments files will not be output."
)
parser.add_argument('--mode',
default='dna',
help="Output mode, can be chosen from dna or rna.")
FLAGS = parser.parse_args(sys.argv[1:])
pbars = multi_pbars(
["Logits(batches)", "ctc(batches)", "logits(files)", "ctc(files)"])
x = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.segment_len])
seq_length = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
training = tf.placeholder(tf.bool)
config_path = os.path.join(FLAGS.model, 'model.json')
model_configure = chiron_model.read_config(config_path)
logits, ratio = chiron_model.inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len,
configure=model_configure)
prorbs = tf.nn.softmax(logits)
predict = tf.nn.ctc_beam_search_decoder(tf.transpose(logits,
perm=[1, 0, 2]),
seq_length,
merge_repeated=False,
beam_width=FLAGS.beam)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=FLAGS.threads,
inter_op_parallelism_threads=FLAGS.threads)
config.gpu_options.allow_growth = True
