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 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 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 data_iterator(file_list, start=0, segment_len=400, jump_step=30):
for f_p in file_list:
data_set = read_data_for_eval(f_p,
step=jump_step,
seg_length=segment_len)
reads_n = data_set.reads_n
index = -1
for i in range(0, reads_n, FLAGS.batch_size):
batch_x, seq_len, _ = data_set.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')
index += 1
yield batch_x, seq_len, index, f_p, len(
range(0, reads_n, FLAGS.batch_size)), reads_n
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: True,
logits_index: i,
logits_fname: name,
}
#Training: 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)
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)
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)
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 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 _worker_fn(self):
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(self.file_list):
if (not name.endswith('.signal')) and (
not name.endswith('.fast5')):
continue
input_path = os.path.join(self.file_dir, name)
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
self.pbars.update(0, total=reads_n, progress=0)
self.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)
print(current_batch)
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:
self.pbars.update(0, progress=i)
self.pbars.update_bar()
continue
feed_dict = {
self.x.name:
batch_x,
self.seq_length.name:
np.round(seq_len / self.ratio).astype(np.int32),
self.training.name:
False,
self.logits_index.name:
logits_idx,
self.logits_fname.name:
logits_fn,
}
self.sess.run(self.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([])
self.pbars.update(0, progress=i)
self.pbars.update_bar()
self.pbars.update(2, progress=f_i + 1)
self.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='')
self.sess.run(self.logits_enqueue,
feed_dict={
self.x.name:
batch_x,
self.seq_length.name:
np.round(seq_len / self.ratio).astype(
np.int32),
self.training.name:
False,
self.logits_index.name:
logits_idx,
self.logits_fname.name:
logits_fn,
})
self.sess.run(self.logits_queue_close)
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,
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
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]
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()
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)
logits, ratio = chiron_model.inference(x,
seq_length,
training=training,
full_sequence_len=FLAGS.segment_len)
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_queue = tf.FIFOQueue(
capacity=FLAGS.batch_size * 100,
dtypes=[tf.float32, tf.int32, tf.int32],
shapes=[logits.shape, logits_index.shape, seq_length.shape])
logits_queue_size = logits_queue.size()
logits_enqueue = logits_queue.enqueue((logits, logits_index, seq_length))
### Decoding logits into bases
decode_predict_op, decode_prob_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'))
for name in tqdm(file_list, desc="basecalling fast5s"):
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))
i_logits = 0
decoded_cnt = 0
val = {} # We could read vals out of order, that's why it's a dict
with tqdm(total=reads_n, desc="signal processing") as pbar:
while decoded_cnt < N:
l_sz, d_sz = sess.run(
[logits_queue_size, decode_queue_size])
# Flow control
# Either we have something beam decoded, or we've pushed all data into the queue
pbar.set_postfix(logits_q=l_sz, decoded_q=d_sz)
if d_sz > 0 or i_logits >= reads_n:
i, predict_val, logits_prob = sess.run(
[decode_idx_op, decode_predict_op, decode_prob_op],
feed_dict={training: False})
val[i] = (predict_val, logits_prob)
decoded_cnt += 1
pbar.update(
min(reads_n, decoded_cnt * FLAGS.batch_size) -
(decoded_cnt - 1) * FLAGS.batch_size)
else:
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 / ratio,
training: False,
logits_index: i_logits
}
sess.run(logits_enqueue, feed_dict=feed_dict)
i_logits += FLAGS.batch_size
qs_list = np.empty((0, 1), dtype=np.float)
qs_string = None
for i in trange(0,
reads_n,
FLAGS.batch_size,
desc="further decoding"):
predict_val, logits_prob = val[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
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)
config.gpu_options.allow_growth = True
saver = tf.train.Saver(var_list=tf.trainable_variables() +
tf.moving_average_variables())
sess = tf.train.MonitoredSession(
session_creator=tf.train.ChiefSessionCreator(config=config))
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))
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)
batch_x = np.pad(batch_x,
((0, FLAGS.batch_size - len(batch_x)), (0, 0)),
mode='wrap')
seq_len = np.pad(seq_len, ((0, FLAGS.batch_size - len(seq_len))),
mode='wrap')
batch_x[0, :] = batch_x[0, :]
feed_dict = {
x: batch_x,
seq_length: np.round(seq_len / ratio).astype(np.int32),
training: True,
