def __init__(self, checkpoint_path, num_classes, hyper_params):
# Get ops from graph
with tf.device("/gpu:0"):
# Placeholders
pl_sparse_points_centered_batched, _, _ = model.get_placeholders(
hyper_params["num_point"], hyperparams=hyper_params)
pl_is_training = tf.placeholder(tf.bool, shape=())
# Prediction
pred, _ = model.get_model(
pl_sparse_points_centered_batched,
pl_is_training,
num_classes,
hyperparams=hyper_params,
)
sparse_labels_batched = tf.argmax(pred, axis=2)
# (1, num_sparse_points) -> (num_sparse_points,)
sparse_labels = tf.reshape(sparse_labels_batched, [-1])
sparse_labels = tf.cast(sparse_labels, tf.int32)
# Saver
saver = tf.train.Saver()
# Graph for interpolating labels
# Assuming batch_size == 1 for simplicity
pl_sparse_points_batched = tf.placeholder(tf.float32,
(None, None, 3))
sparse_points = tf.reshape(pl_sparse_points_batched, [-1, 3])
pl_dense_points = tf.placeholder(tf.float32, (None, 3))
pl_knn = tf.placeholder(tf.int32, ())
dense_labels, dense_colors = interpolate_label_with_color(
sparse_points, sparse_labels, pl_dense_points, pl_knn)
self.ops = {
"pl_sparse_points_centered_batched":
pl_sparse_points_centered_batched,
"pl_sparse_points_batched": pl_sparse_points_batched,
"pl_dense_points": pl_dense_points,
"pl_is_training": pl_is_training,
"pl_knn": pl_knn,
"dense_labels": dense_labels,
"dense_colors": dense_colors,
}
# Restore checkpoint to session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
self.sess = tf.Session(config=config)
saver.restore(self.sess, checkpoint_path)
print("Model restored")
def __init__(self, checkpoint_path, num_classes, hyper_params):
# Get ops from graph
with tf.device("/gpu:0"):
# Placeholder
pl_points, _, _ = model.get_placeholders(hyper_params["num_point"],
hyperparams=hyper_params)
pl_is_training = tf.placeholder(tf.bool, shape=())
print("pl_points shape", tf.shape(pl_points))
# Prediction
pred, _ = model.get_model(pl_points,
pl_is_training,
num_classes,
hyperparams=hyper_params)
# Saver
saver = tf.train.Saver()
# Graph for interpolating labels
# Assuming batch_size == 1 for simplicity
pl_sparse_points = tf.placeholder(tf.float32, (None, 3))
pl_sparse_labels = tf.placeholder(tf.int32, (None, ))
pl_dense_points = tf.placeholder(tf.float32, (None, 3))
pl_knn = tf.placeholder(tf.int32, ())
sparse_indices = interpolate_label(pl_sparse_points,
pl_sparse_labels,
pl_dense_points, pl_knn)
self.ops = {
"pl_points": pl_points,
"pl_is_training": pl_is_training,
"pred": pred,
"pl_sparse_points": pl_sparse_points,
"pl_sparse_labels": pl_sparse_labels,
"pl_dense_points": pl_dense_points,
"pl_knn": pl_knn,
"sparse_indices": sparse_indices,
}
# Restore checkpoint to session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
self.sess = tf.Session(config=config)
saver.restore(self.sess, checkpoint_path)
print("Model restored")
def test(FLAGS):
# read data
dataset = DataSet(fpath=FLAGS.test_file,
seqlen=FLAGS.seq_len,
n_classes=FLAGS.num_classes,
num_feature=FLAGS.num_feature,
is_raw=FLAGS.is_raw,
need_shuffle=False)
# set character set size
FLAGS.charset_size = dataset.charset_size
with tf.Graph().as_default():
# placeholder
placeholders = get_placeholders(FLAGS)
# get inference
pred, layers = inference(placeholders['data'],
FLAGS,
for_training=False)
prob = tf.nn.softmax(pred)
# calculate prediction
_hit_op = tf.equal(tf.argmax(pred, 1),
tf.argmax(placeholders['labels'], 1))
hit_op = tf.reduce_sum(tf.cast(_hit_op, tf.float32))
# create saver
saver = tf.train.Saver()
# summary
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
# load model
ckpt = tf.train.latest_checkpoint(
os.path.dirname(FLAGS.checkpoint_path))
if tf.train.checkpoint_exists(ckpt):
saver.restore(sess, ckpt)
global_step = ckpt.split('/')[-1].split('-')[-1]
logging(
'Succesfully loaded model from %s at step=%s.' %
(ckpt, global_step), FLAGS)
else:
logging("[ERROR] Checkpoint not exist", FLAGS)
return
# iter batch
hit_count = 0.0
total_count = 0
pred_list = []
label_list = []
logging("%s: starting test." % (datetime.now()), FLAGS)
start_time = time.time()
total_batch_size = math.ceil(dataset._num_data / FLAGS.batch_size)
for step, (data, labels) in enumerate(
dataset.iter_once(FLAGS.batch_size)):
hits, pred_val = sess.run([hit_op, prob],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels
})
hit_count += np.sum(hits)
total_count += len(data)
for i, p in enumerate(pred_val):
pred_list.append(p[0])
label_list.append(labels[i][0])
if step % FLAGS.log_interval == 0:
duration = time.time() - start_time
sec_per_batch = duration / FLAGS.log_interval
examples_per_sec = FLAGS.batch_size / sec_per_batch
logging(
'%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' % (datetime.now(), step, total_batch_size,
examples_per_sec, sec_per_batch),
FLAGS)
start_time = time.time()
# micro precision
# logging("%s: micro-precision = %.5f" %
# (datetime.now(), (hit_count/total_count)), FLAGS)
auc_val = roc_auc_score(label_list, pred_list)
logging(
"%s: micro-precision = %.5f, auc = %.5f" %
(datetime.now(), (hit_count / total_count), auc_val), FLAGS)
pred_y = [1 if i > FLAGS.threshold else 0 for i in pred_list]
TN, FP, FN, TP = confusion_matrix(label_list,
pred_y,
labels=[1, 0]).ravel()
Sensitivity = round((TP / (TP + FN)), 4) if TP + FN > 0 else 0
Specificity = round(TN / (FP + TN), 4) if FP + TN > 0 else 0
Precision = round(TP / (TP + FP), 4) if TP + FP > 0 else 0
Accuracy = round((TP + TN) / (TP + FP + TN + FN), 4)
MCC = round(
((TP * TN) - (FP * FN)) / (math.sqrt(
(TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))), 4
) if TP + FP > 0 and FP + TN > 0 and TP + FN and TN + FN else 0
F1 = round((2 * TP) / ((2 * TP) + FP + FN), 4)
fout = open(FLAGS.out_file, 'a')
Prec = round(hit_count / total_count, 4)
AUC = round(auc_val, 4)
fout.write(
f"{global_step},{datetime.now()},{TP},{FP},{TN},{FN},{Sensitivity},{Specificity},{Precision},{Accuracy},{MCC},{F1},{AUC}\n"
)
logging(
f"TP={TP}, FP={FP}, TN={TN}, FN={FN}, Sens={Sensitivity}, Spec={Specificity}, Prec={Precision}, Acc={Accuracy}, MCC={MCC}, F1={F1}, AUC={auc_val}",
FLAGS)
fout.close()
def train():
"""Train the model on a single GPU
"""
with tf.Graph().as_default():
stacker, stack_validation, stack_train = init_stacking()
with tf.device("/gpu:" + str(PARAMS["gpu"])):
pointclouds_pl, labels_pl, smpws_pl = model.get_placeholders(
PARAMS["num_point"], hyperparams=PARAMS)
is_training_pl = tf.compat.v1.placeholder(tf.bool, shape=())
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for
# you every time it trains.
batch = tf.Variable(0)
bn_decay = get_bn_decay(batch)
tf.summary.scalar("bn_decay", bn_decay)
print("--- Get model and loss")
# Get model and loss
pred, end_points = model.get_model(
pointclouds_pl,
is_training_pl,
NUM_CLASSES,
hyperparams=PARAMS,
bn_decay=bn_decay,
)
loss = model.get_loss(pred, labels_pl, smpws_pl, end_points)
tf.summary.scalar("loss", loss)
# Compute accuracy
correct = tf.equal(tf.argmax(pred, 2),
tf.compat.v1.to_int64(labels_pl))
accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(
PARAMS["batch_size"] * PARAMS["num_point"])
tf.summary.scalar("accuracy", accuracy)
# Computer mean intersection over union
mean_intersection_over_union, update_iou_op = tf.compat.v1.metrics.mean_iou(
tf.compat.v1.to_int32(labels_pl),
tf.compat.v1.to_int32(tf.argmax(pred, 2)), NUM_CLASSES)
tf.summary.scalar(
"mIoU", tf.compat.v1.to_float(mean_intersection_over_union))
print("--- Get training operator")
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar("learning_rate", learning_rate)
if PARAMS["optimizer"] == "momentum":
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=PARAMS["momentum"])
else:
assert PARAMS["optimizer"] == "adam"
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.compat.v1.train.Saver()
# Create a session
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.compat.v1.Session(config=config)
# Add summary writers
merged = tf.compat.v1.summary.merge_all()
train_writer = tf.compat.v1.summary.FileWriter(
os.path.join(PARAMS["logdir"], "train"), sess.graph)
validation_writer = tf.compat.v1.summary.FileWriter(
os.path.join(PARAMS["logdir"], "validation"), sess.graph)
# Init variables
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(
tf.compat.v1.local_variables_initializer()) # important for mIoU
ops = {
"pointclouds_pl": pointclouds_pl,
"labels_pl": labels_pl,
"smpws_pl": smpws_pl,
"is_training_pl": is_training_pl,
"pred": pred,
"loss": loss,
"train_op": train_op,
"merged": merged,
"step": batch,
"end_points": end_points,
"update_iou": update_iou_op,
}
# Train for hyper_params["max_epoch"] epochs
best_acc = 0
for epoch in range(PARAMS["max_epoch"]):
print("in epoch", epoch)
print("max_epoch", PARAMS["max_epoch"])
log_string("**** EPOCH %03d ****" % (epoch))
sys.stdout.flush()
# Train one epoch
train_one_epoch(sess, ops, train_writer, stack_train)
# Evaluate, save, and compute the accuracy
if epoch % 5 == 0:
acc = eval_one_epoch(sess, ops, validation_writer,
stack_validation)
if acc > best_acc:
best_acc = acc
save_path = saver.save(
sess,
os.path.join(PARAMS["logdir"],
"best_model_epoch_%03d.ckpt" % (epoch)),
)
log_string("Model saved in file: %s" % save_path)
print("Model saved in file: %s" % save_path)
# Save the variables to disk.
if epoch % 10 == 0:
save_path = saver.save(
sess, os.path.join(PARAMS["logdir"], "model.ckpt"))
log_string("Model saved in file: %s" % save_path)
print("Model saved in file: %s" % save_path)
# Kill the process, close the file and exit
stacker.terminate()
LOG_FOUT.close()
sys.exit()
def train(FLAGS):
# first make bag of words
worddict = WordDict(files=[FLAGS.train_file, FLAGS.test_file],
k=FLAGS.k,
logpath=FLAGS.log_dir)
FLAGS.word_size = worddict.size
# read data
dataset = DataSet(fpath=FLAGS.train_file,
n_classes=FLAGS.num_classes,
wd=worddict,
need_shuffle=True)
with tf.Graph().as_default():
global_step = tf.placeholder(tf.int32)
placeholders = get_placeholders(FLAGS)
pred = inference(placeholders['data'], FLAGS, for_training=True)
tf.losses.softmax_cross_entropy(placeholders['labels'], pred)
loss = tf.losses.get_total_loss()
_acc_op = tf.equal(tf.argmax(pred, 1),
tf.argmax(placeholders['labels'], 1))
acc_op = tf.reduce_mean(tf.cast(_acc_op, tf.float32))
train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)
# Create a saver.
saver = tf.train.Saver(max_to_keep=None)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if tf.train.checkpoint_exists(FLAGS.prev_checkpoint_path):
restorer = tf.train.Saver()
restorer.restore(sess, FLAGS.prev_checkpoint_path)
logging(
'%s: Pre-trained model restored from %s' %
(datetime.now(), FLAGS.prev_checkpoint_path), FLAGS)
step = int(
FLAGS.prev_checkpoint_path.split('/')[-1].split('-')
[-1]) + 1
else:
step = 0
for data, labels in dataset.iter_once(FLAGS.batch_size):
start_time = time.time()
_, loss_val, acc_val = sess.run(
[train_op, loss, acc_op],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels,
global_step: step
})
duration = time.time() - start_time
assert not np.isnan(loss_val), 'Model diverge'
if step > 0 and step % FLAGS.log_interval == 0:
examples_per_sec = FLAGS.batch_size / float(duration)
format_str = (
'%s: step %d, loss = %.2f, acc = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
logging(
format_str % (datetime.now(), step, loss_val, acc_val,
examples_per_sec, duration), FLAGS)
if step > 0 and step % FLAGS.save_interval == 0:
saver.save(sess, FLAGS.checkpoint_path, global_step=step)
# counter
step += 1
# save for last
saver.save(sess, FLAGS.checkpoint_path, global_step=step - 1)
def test(FLAGS):
# first make bag of words
worddict = WordDict(FLAGS.embedding_file)
FLAGS.word_size = worddict.size
# read data
dataset = DataSet(fpath=FLAGS.test_file,
n_classes=FLAGS.num_classes,
wd=worddict,
need_shuffle=False)
with tf.Graph().as_default():
# placeholder
placeholders = get_placeholders(FLAGS)
# get inference
pred = inference(placeholders['data'], FLAGS, for_training=False)
# calculate prediction
pred_label_op = tf.argmax(pred, 1)
label_op = tf.argmax(placeholders['labels'], 1)
_hit_op = tf.equal(pred_label_op, label_op)
hit_op = tf.reduce_sum(tf.cast(_hit_op, tf.float32))
# create saver
saver = tf.train.Saver()
# summary
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
# load model
ckpt = tf.train.latest_checkpoint(
os.path.dirname(FLAGS.checkpoint_path))
if tf.train.checkpoint_exists(ckpt):
saver.restore(sess, ckpt)
global_step = ckpt.split('/')[-1].split('-')[-1]
logging(
'Succesfully loaded model from %s at step=%s.' %
(ckpt, global_step), FLAGS)
else:
logging("[ERROR] Checkpoint not exist", FLAGS)
return
# summary writer
summary_writer = tf.summary.FileWriter(FLAGS.log_dir,
graph=sess.graph)
# iter batch
hit_count = 0.0
total_count = 0
results = []
logging("%s: starting test." % (datetime.now()), FLAGS)
start_time = time.time()
total_batch_size = math.ceil(dataset._num_data / FLAGS.batch_size)
for step, (data, labels) in enumerate(
dataset.iter_once(FLAGS.batch_size)):
hits, pred, lb = sess.run([hit_op, pred_label_op, label_op],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels
})
hit_count += np.sum(hits)
total_count += len(data)
for i, p in enumerate(pred):
results.append((p, lb[i]))
if step % FLAGS.log_interval == 0:
duration = time.time() - start_time
sec_per_batch = duration / FLAGS.log_interval
examples_per_sec = FLAGS.batch_size / sec_per_batch
logging(
'%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' % (datetime.now(), step, total_batch_size,
examples_per_sec, sec_per_batch),
FLAGS)
start_time = time.time()
# micro precision
logging(
"%s: micro-precision = %.5f" % (datetime.now(),
(hit_count / total_count)),
FLAGS)
# write result
outpath = os.path.join(FLAGS.log_dir, "out.txt")
with open(outpath, 'w') as fw:
for p, l in results:
fw.write("%d\t%d\n" % (int(l), int(p)))
def test(FLAGS):
# read data
dataset = DataSet(fpath=FLAGS.test_file,
seqlen=FLAGS.seq_len,
n_classes=FLAGS.num_classes,
need_shuffle=False)
FLAGS.charset_size = dataset.charset_size
with tf.Graph().as_default():
# placeholder
placeholders = get_placeholders(FLAGS)
# get inference
pred, layers = inference(placeholders['data'],
FLAGS,
for_training=False)
# calculate prediction
_hit_op = tf.equal(tf.argmax(pred, 1),
tf.argmax(placeholders['labels'], 1))
hit_op = tf.reduce_sum(tf.cast(_hit_op, tf.float32))
# create saver
saver = tf.train.Saver()
# argmax of hidden1
h1_argmax_ops = []
for op in layers['conv']:
h1_argmax_ops.append(tf.argmax(op, axis=2))
with tf.Session() as sess:
# load model
ckpt = tf.train.latest_checkpoint(
os.path.dirname(FLAGS.checkpoint_path))
if tf.train.checkpoint_exists(ckpt):
saver.restore(sess, ckpt)
global_step = ckpt.split('/')[-1].split('-')[-1]
print('Succesfully loaded model from %s at step=%s.' %
(ckpt, global_step))
else:
print("[ERROR] Checkpoint not exist")
return
# iter batch
hit_count = 0.0
total_count = 0
# top_matches = [ ([], []) ] * FLAGS.hidden1 # top 100 matching proteins
wlens = [4, 8, 12, 16, 20]
hsize = int(FLAGS.hidden1 / 5)
motif_matches = (defaultdict(list), defaultdict(list))
print("%s: starting test." % (datetime.now()))
start_time = time.time()
total_batch_size = math.ceil(dataset._num_data / FLAGS.batch_size)
for step, (data, labels, raws) in enumerate(
dataset.iter_once(FLAGS.batch_size, with_raw=True)):
res_run = sess.run([hit_op, h1_argmax_ops] + layers['conv'],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels
})
hits = res_run[0]
max_idxs = res_run[1] # shape = (wlens, N, 1, # of filters)
motif_filters = res_run[2:]
# mf.shape = (N, 1, l-w+1, # of filters)
for i in range(len(motif_filters)):
s = motif_filters[i].shape
motif_filters[i] = np.transpose(motif_filters[i],
(0, 1, 3, 2)).reshape(
(s[0], s[3], s[2]))
# mf.shape = (N, # of filters, l-w+1)
for gidx, mf in enumerate(motif_filters):
wlen = wlens[gidx]
for ridx, row in enumerate(mf):
for fidx, vals in enumerate(row):
# for each filter, get max value and it's index
max_idx = max_idxs[gidx][ridx][0][fidx]
# max_idx = np.argmax(vals)
max_val = vals[max_idx]
hidx = gidx * hsize + fidx
if max_val > 0:
# get sequence
rawseq = raws[ridx][1]
subseq = rawseq[max_idx:max_idx + wlen]
# heappush( top_matches[hidx], (max_val, subseq) )
motif_matches[0][hidx].append(max_val)
motif_matches[1][hidx].append(subseq)
# motif_matches[gidx][fidx][0].append( max_val )
# motif_matches[gidx][fidx][1].append( subseq )
hit_count += np.sum(hits)
total_count += len(data)
# print("total:%d" % total_count)
if step % FLAGS.log_interval == 0:
duration = time.time() - start_time
sec_per_batch = duration / FLAGS.log_interval
examples_per_sec = FLAGS.batch_size / sec_per_batch
print('%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' %
(datetime.now(), step, total_batch_size,
examples_per_sec, sec_per_batch))
start_time = time.time()
# if step > 10:
# break
# # micro precision
# print("%s: micro-precision = %.5f" %
# (datetime.now(), (hit_count/total_count)))
### sort top lists
print('%s: write result to file' % (datetime.now()))
for fidx in motif_matches[0]:
val_lst = motif_matches[0][fidx]
seq_lst = motif_matches[1][fidx]
# top k
k = wlens[int(fidx / hsize)] * 25
l = min(k, len(val_lst)) * -1
tidxs = np.argpartition(val_lst, l)[l:]
with open(
"/home/kimlab/project/CCC/tmp/logos/test/p%d.txt" %
fidx, 'w') as fw:
for idx in tidxs:
fw.write("%f\t%s\n" % (val_lst[idx], seq_lst[idx]))
if fidx % 50 == 0:
print('%s: [%d filters out of %d]' %
(datetime.now(), fidx, FLAGS.hidden1))
def test(FLAGS):
# read data
dataset = DataSet(fpath=FLAGS.test_file,
seqlen=FLAGS.seq_len,
n_classes=FLAGS.num_classes,
need_shuffle=False)
# set character set size
FLAGS.charset_size = dataset.charset_size
with tf.Graph().as_default():
# placeholder
placeholders = get_placeholders(FLAGS)
# get inference
pred, layers = inference(placeholders['data'],
FLAGS,
for_training=False)
prob = tf.nn.softmax(pred)
# calculate prediction
_hit_op = tf.equal(tf.argmax(pred, 1),
tf.argmax(placeholders['labels'], 1))
hit_op = tf.reduce_sum(tf.cast(_hit_op, tf.float32))
# create saver
saver = tf.train.Saver()
# summary
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
# load model
ckpt = tf.train.latest_checkpoint(
os.path.dirname(FLAGS.checkpoint_path))
if tf.train.checkpoint_exists(ckpt):
saver.restore(sess, ckpt)
global_step = ckpt.split('/')[-1].split('-')[-1]
logging(
'Succesfully loaded model from %s at step=%s.' %
(ckpt, global_step), FLAGS)
else:
logging("[ERROR] Checkpoint not exist", FLAGS)
return
# iter batch
hit_count = 0.0
total_count = 0
pred_list = []
label_list = []
logging("%s: starting test." % (datetime.now()), FLAGS)
start_time = time.time()
total_batch_size = math.ceil(dataset._num_data / FLAGS.batch_size)
for step, (data, labels) in enumerate(
dataset.iter_once(FLAGS.batch_size)):
hits, pred_val = sess.run([hit_op, prob],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels
})
hit_count += np.sum(hits)
total_count += len(data)
for i, p in enumerate(pred_val):
pred_list.append(p[0])
label_list.append(labels[i][0])
if step % FLAGS.log_interval == 0:
duration = time.time() - start_time
sec_per_batch = duration / FLAGS.log_interval
examples_per_sec = FLAGS.batch_size / sec_per_batch
logging(
'%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' % (datetime.now(), step, total_batch_size,
examples_per_sec, sec_per_batch),
FLAGS)
start_time = time.time()
# micro precision
# logging("%s: micro-precision = %.5f" %
# (datetime.now(), (hit_count/total_count)), FLAGS)
auc_val = roc_auc_score(label_list, pred_list)
logging(
"%s: micro-precision = %.5f, auc = %.5f" %
(datetime.now(), (hit_count / total_count), auc_val), FLAGS)
if FLAGS.save_prediction:
with open(FLAGS.save_prediction, 'w') as fout:
with open(FLAGS.test_file) as f:
for i, line in enumerate(f.readlines()):
line = line.strip()
id = line.split('\t')[2]
label = label_list[i]
predict = pred_list[i]
print('\t'.join([id, predict, label]), file=fout)
def test( FLAGS ):
# read data
dataset = DataSet( fpath = FLAGS.test_file,
seqlen = FLAGS.seq_len,
n_classes = FLAGS.num_classes,
need_shuffle = False )
FLAGS.charset_size = dataset.charset_size
with tf.Graph().as_default():
# placeholder
placeholders = get_placeholders(FLAGS)
# get inference
pred, layers = inference( placeholders['data'], FLAGS,
for_training=False )
# calculate prediction
label_op = tf.argmax(pred, 1)
prob_op = tf.nn.softmax(pred)
# _hit_op = tf.equal( tf.argmax(pred, 1), tf.argmax(placeholders['labels'], 1))
# hit_op = tf.reduce_sum( tf.cast( _hit_op ,tf.float32 ) )
# create saver
saver = tf.train.Saver()
# argmax of hidden1
h1_argmax_ops = []
for op in layers['conv']:
h1_argmax_ops.append(tf.argmax(op, axis=2))
with tf.Session() as sess:
# load model
# ckpt = tf.train.latest_checkpoint( os.path.dirname( FLAGS.checkpoint_path ) )
ckpt = FLAGS.checkpoint_path
if tf.train.checkpoint_exists( ckpt ):
saver.restore( sess, ckpt )
global_step = ckpt.split('/')[-1].split('-')[-1]
print('Succesfully loaded model from %s at step=%s.' %
(ckpt, global_step))
else:
print("[ERROR] Checkpoint not exist")
return
# iter batch
hit_count = 0.0
total_count = 0
wlens = FLAGS.window_lengths
hsizes = FLAGS.num_windows
motif_matches = (defaultdict(list), defaultdict(list))
pred_labels = []
pred_prob = []
print("%s: starting test." % (datetime.now()))
start_time = time.time()
total_batch_size = math.ceil( dataset._num_data / FLAGS.batch_size )
for step, (data, labels, raws) in enumerate(dataset.iter_once( FLAGS.batch_size, with_raw=True )):
res_run = sess.run( [label_op, prob_op, h1_argmax_ops] + layers['conv'], feed_dict={
placeholders['data']: data,
placeholders['labels']: labels
})
pred_label = res_run[0]
pred_arr = res_run[1]
max_idxs = res_run[2] # shape = (wlens, N, 1, # of filters)
motif_filters = res_run[3:]
for i, l in enumerate(pred_label):
pred_labels.append(l)
pred_prob.append( pred_arr[i][ 388 ] )
# mf.shape = (N, 1, l-w+1, # of filters)
for i in range(len(motif_filters)):
s = motif_filters[i].shape
motif_filters[i] = np.transpose( motif_filters[i], (0, 1, 3, 2) ).reshape( (s[0], s[3], s[2]) )
# mf.shape = (N, # of filters, l-w+1)
for gidx, mf in enumerate(motif_filters):
wlen = wlens[gidx]
hsize = hsizes[gidx]
for ridx, row in enumerate(mf):
for fidx, vals in enumerate(row):
# for each filter, get max value and it's index
max_idx = max_idxs[gidx][ridx][0][fidx]
# max_idx = np.argmax(vals)
max_val = vals[ max_idx ]
hidx = gidx * hsize + fidx
if max_val > 0:
# get sequence
rawseq = raws[ridx][1]
subseq = rawseq[ max_idx : max_idx+wlen ]
# heappush( top_matches[hidx], (max_val, subseq) )
motif_matches[0][hidx].append( max_val )
motif_matches[1][hidx].append( subseq )
# motif_matches[gidx][fidx][0].append( max_val )
# motif_matches[gidx][fidx][1].append( subseq )
# hit_count += np.sum( hits )
total_count += len( data )
# print("total:%d" % total_count)
if step % FLAGS.log_interval == 0:
duration = time.time() - start_time
sec_per_batch = duration / FLAGS.log_interval
examples_per_sec = FLAGS.batch_size / sec_per_batch
print('%s: [%d batches out of %d] (%.1f examples/sec; %.3f'
'sec/batch)' % (datetime.now(), step, total_batch_size,
examples_per_sec, sec_per_batch))
start_time = time.time()
# if step > 10:
# break
# # micro precision
# print("%s: micro-precision = %.5f" %
# (datetime.now(), (hit_count/total_count)))
print(pred_labels)
### sort top lists
# report whose activation was higher
mean_acts = {}
on_acts = {}
print('%s: write result to file' % (datetime.now()) )
for fidx in motif_matches[0]:
val_lst = motif_matches[0][fidx]
seq_lst = motif_matches[1][fidx]
# top k
# k = wlens[ int(fidx / hsize) ] * 25
k = 30
# l = min(k, len(val_lst)) * -1
l = len(val_lst) * -1
tidxs = np.argpartition(val_lst, l)[l:]
# tracking acts
acts = 0.0
opath = os.path.join(FLAGS.motif_outpath, "p%d.txt"%fidx)
with open(opath, 'w') as fw:
for idx in tidxs:
fw.write("%f\t%s\n" % (val_lst[idx], seq_lst[idx]) )
acts += val_lst[idx]
mean_acts[fidx] = acts / l * -1
on_acts[fidx] = len(val_lst)
if fidx % 50 == 0:
print('%s: [%d filters out of %d]' % (datetime.now(), fidx, sum(FLAGS.num_windows)))
# print(len(val_lst))
# report mean acts
with open(os.path.join(FLAGS.motif_outpath, "report.txt"), 'w') as fw:
for i in sorted(on_acts, key=on_acts.get, reverse=True):
fw.write("%f\t%f\t%d\n" % (on_acts[i] / total_count, mean_acts[i], i))
with open(os.path.join(FLAGS.motif_outpath, "predictions.txt"), 'w') as fw:
for i, p in enumerate(pred_labels):
fw.write("%s\t%f\n" % (str(p), pred_prob[i]))
def train(FLAGS):
# read data
dataset = DataSet(fpath=FLAGS.train_file,
seqlen=FLAGS.seq_len,
n_classes=FLAGS.num_classes,
need_shuffle=True)
# set character set size
FLAGS.charset_size = dataset.charset_size
with tf.Graph().as_default():
# get placeholders
global_step = tf.placeholder(tf.int32)
placeholders = get_placeholders(FLAGS)
# prediction
pred, layers = inference(placeholders['data'],
FLAGS,
for_training=True)
# loss
# slim.losses.softmax_cross_entropy(pred, placeholders['labels'])
# class_weight = tf.constant([[1.0, 5.0]])
# weight_per_label = tf.transpose( tf.matmul(placeholders['labels']
# , tf.transpose(class_weight)) )
# loss = tf.multiply(weight_per_label,
# tf.nn.softmax_cross_entropy_with_logits(labels=placeholders['labels'], logits=pred))
# loss = tf.losses.compute_weighted_loss(loss)
tf.losses.softmax_cross_entropy(placeholders['labels'], pred)
loss = tf.losses.get_total_loss()
# accuracy
_acc_op = tf.equal(tf.argmax(pred, 1),
tf.argmax(placeholders['labels'], 1))
acc_op = tf.reduce_mean(tf.cast(_acc_op, tf.float32))
# optimization
train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)
# train_op = tf.train.RMSPropOptimizer( FLAGS.learning_rate ).minimize( loss )
# Create a saver.
saver = tf.train.Saver(max_to_keep=None)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if tf.train.checkpoint_exists(FLAGS.prev_checkpoint_path):
if FLAGS.fine_tuning:
logging('%s: Fine Tuning Experiment!' % (datetime.now()),
FLAGS)
restore_variables = slim.get_variables_to_restore(
exclude=FLAGS.fine_tuning_layers)
restorer = tf.train.Saver(restore_variables)
else:
restorer = tf.train.Saver()
restorer.restore(sess, FLAGS.prev_checkpoint_path)
logging(
'%s: Pre-trained model restored from %s' %
(datetime.now(), FLAGS.prev_checkpoint_path), FLAGS)
step = int(
FLAGS.prev_checkpoint_path.split('/')[-1].split('-')
[-1]) + 1
else:
step = 0
# iter epoch
# for data, labels in dataset.iter_batch( FLAGS.batch_size, 5 ):
for data, labels in dataset.iter_once(FLAGS.batch_size):
start_time = time.time()
_, loss_val, acc_val = sess.run(
[train_op, loss, acc_op],
feed_dict={
placeholders['data']: data,
placeholders['labels']: labels,
global_step: step
})
duration = time.time() - start_time
assert not np.isnan(loss_val), 'Model diverge'
# logging
if step > 0 and step % FLAGS.log_interval == 0:
examples_per_sec = FLAGS.batch_size / float(duration)
format_str = (
'%s: step %d, loss = %.2f, acc = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
logging(
format_str % (datetime.now(), step, loss_val, acc_val,
examples_per_sec, duration), FLAGS)
# save model
if step > 0 and step % FLAGS.save_interval == 0:
saver.save(sess, FLAGS.checkpoint_path, global_step=step)
# counter
step += 1
# save for last
saver.save(sess, FLAGS.checkpoint_path, global_step=step - 1)