def eval(fps, args):
eval_dir = os.path.join(args.train_dir, 'eval_valid')
if not os.path.isdir(eval_dir):
os.makedirs(eval_dir)
model = WaveAE(Modes.EVAL)
model, summary = override_model_attrs(model, args.model_overrides)
print('-' * 80)
print(summary)
print('-' * 80)
# Load data
with tf.name_scope('loader'):
clean, x = waveform_decoder(
fps=fps,
batch_size=model.eval_batch_size,
subseq_len=model.subseq_len,
audio_fs=model.audio_fs,
audio_mono=True,
audio_normalize=True,
decode_fastwav=args.data_fastwav,
decode_parallel_calls=1,
repeat=False,
shuffle=False,
shuffle_buffer_size=None,
subseq_randomize_offset=False,
subseq_overlap_ratio=0.,
subseq_pad_end=True,
prefetch_size=None,
gpu_num=None)
model.build_denoiser(clean, x)
saver = tf.train.Saver(var_list=model.restore_vars, max_to_keep=1)
summary_writer = tf.summary.FileWriter(eval_dir)
ckpt_fp = None
while True:
latest_ckpt_fp = tf.train.latest_checkpoint(args.train_dir)
if latest_ckpt_fp != ckpt_fp:
ckpt_fp = latest_ckpt_fp
print('Evaluating {}'.format(ckpt_fp))
with tf.Session() as sess:
model.eval_ckpt(ckpt_fp, sess, summary_writer, saver, eval_dir)
print('Done!')
time.sleep(1)
def train(fps, args):
# Initialize model
if args.ae_model == "supervised":
print ("supervised Model")
model = supervisedWaveAE(Modes.TRAIN)
else:
model = WaveAE(Modes.TRAIN)
model, summary = override_model_attrs(model, args.model_overrides)
print('-' * 80)
print(summary)
print('-' * 80)
# Load data
with tf.name_scope('loader'):
clean, x = waveform_decoder(
fps=fps,
batch_size=model.train_batch_size,
subseq_len=model.subseq_len,
audio_fs=model.audio_fs,
audio_mono=True,
audio_normalize=True,
decode_fastwav=args.data_fastwav,
decode_parallel_calls=4,
repeat=True,
shuffle=True,
shuffle_buffer_size=4096,
subseq_randomize_offset=args.data_randomize_offset,
subseq_overlap_ratio=args.data_overlap_ratio,
subseq_pad_end=True,
prefetch_size=64,
gpu_num=0)
# Create model
model(clean, x)
# Train
# model_dir_path = "/data2/paarth/TrainDir/WaveAE/WaveAEsc09_l1batchnormFalse/eval_sc09_valid"
# ckpt = 253802
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_ckpt_every_nsecs,
save_summaries_secs=args.train_summary_every_nsecs) as sess:
while not sess.should_stop():
model.train_loop(sess)
def infer(fps, args):
infer_dir = os.path.join(args.train_dir, 'infer_valid')
if not os.path.isdir(infer_dir):
os.makedirs(infer_dir)
model = WaveAE(Modes.INFER)
model, summary = override_model_attrs(model, args.model_overrides)
print('-' * 80)
print(summary)
print('-' * 80)
with tf.name_scope('loader'):
clean, x = waveform_decoder(
fps=fps,
batch_size=model.eval_batch_size,
subseq_len=model.subseq_len,
audio_fs=model.audio_fs,
audio_mono=True,
audio_normalize=True,
decode_fastwav=args.data_fastwav,
decode_parallel_calls=1,
repeat=False,
shuffle=False,
shuffle_buffer_size=None,
subseq_randomize_offset=False,
subseq_overlap_ratio=0.,
subseq_pad_end=True,
prefetch_size=None,
gpu_num=None)
model.build_inference(clean, x)
saver = tf.train.Saver(var_list=model.G_vars, max_to_keep=1)
summary_writer = tf.summary.FileWriter(infer_dir)
ckpt_fp = args.infer_ckpt_fp
with tf.Session() as sess:
model.infer(ckpt_fp, sess, summary_writer, saver, infer_dir)
print ("Done")
import tensorflow as tf
from AudioModel.util import override_model_attrs
import EncDecModel
import supervisedModel
from AudioModel.model import Model, Modes
ckpt = 16384
a = tf.placeholder('float32', (32, 16384, 1, 1))
b = tf.placeholder('float32', (32, 16384, 1, 1))
model = supervisedModel.WaveAE(mode=Modes.TRAIN)
oberrides = "objective=l1,batchnorm=False,train_batch_size=32,alpha=100.0,enc_length=16,stride=4,kernel_len=25,subseq_len=16384"
model, summary = override_model_attrs(model, oberrides)
model(a, b)
# with tf.variable_scope('AE'):
# with tf.variable_scope('E'):
# enc = waveAE.WaveEncoderFactor256(batchnorm=False)
# E_x = enc(a, training=False)
# e_flat = tf.reshape(E_x, [32, -1])
# rd_tensor = tf.layers.dense(e_flat, 5)
# model_dir_path = "/data2/paarth/TrainDir/WaveAE/WaveAEsc09_l1batchnormFalse/eval_sc09_valid"
# e_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='AE/E')
# print (e_vars)
# saver = tf.train.Saver(var_list=e_vars)
# sess = tf.InteractiveSession()
# saver.restore(sess, '{}/best_valid_l2-{}'.format(model_dir_path, ckpt))