def eval_op(batch, hparams, config_name):
"""Define a evaluation op.
Args:
batch: Batch produced by NSynthReader.
hparams: Hyperparameters.
config_name: Name of config module.
Returns:
eval_op: A complete evaluation op with summaries.
"""
phase = not (hparams.mag_only or hparams.raw_audio)
config = utils.get_module("baseline.models.ae_configs.%s" % config_name)
if hparams.raw_audio:
x = batch["audio"]
# Add height and channel dims
x = tf.expand_dims(tf.expand_dims(x, 1), -1)
else:
x = batch["spectrogram"]
# Define the model
with tf.name_scope("Model"):
z = config.encode(x, hparams, is_training=False)
xhat = config.decode(z, batch, hparams, is_training=False)
# For interpolation
tf.add_to_collection("x", x)
tf.add_to_collection("pitch", batch["pitch"])
tf.add_to_collection("z", z)
tf.add_to_collection("xhat", xhat)
total_loss = compute_mse_loss(x, xhat, hparams)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
"Loss":
slim.metrics.mean(total_loss),
})
# Define the summaries
for name, value in names_to_values.items():
slim.summaries.add_scalar_summary(value, name, print_summary=True)
# Interpolate
with tf.name_scope("Interpolation"):
xhat = config.decode(z, batch, hparams, reuse=True, is_training=False)
# Linear interpolation
z_shift_one_example = tf.concat([z[1:], z[:1]], 0)
z_linear_half = (z + z_shift_one_example) / 2.0
xhat_linear_half = config.decode(z_linear_half,
batch,
hparams,
reuse=True,
is_training=False)
# Pitch shift
pitch_plus_2 = tf.clip_by_value(batch["pitch"] + 2, 0, 127)
pitch_minus_2 = tf.clip_by_value(batch["pitch"] - 2, 0, 127)
batch["pitch"] = pitch_minus_2
xhat_pitch_minus_2 = config.decode(z,
batch,
hparams,
reuse=True,
is_training=False)
batch["pitch"] = pitch_plus_2
xhat_pitch_plus_2 = config.decode(z,
batch,
hparams,
reuse=True,
is_training=False)
utils.specgram_summaries(x, "Training Examples", hparams, phase=phase)
utils.specgram_summaries(xhat, "Reconstructions", hparams, phase=phase)
utils.specgram_summaries(x - xhat,
"Difference",
hparams,
audio=False,
phase=phase)
utils.specgram_summaries(xhat_linear_half,
"Linear Interp. 0.5",
hparams,
phase=phase)
utils.specgram_summaries(xhat_pitch_plus_2,
"Pitch +2",
hparams,
phase=phase)
utils.specgram_summaries(xhat_pitch_minus_2,
"Pitch -2",
hparams,
phase=phase)
return list(names_to_updates.values())
def eval_op(batch, hparams, config_name):
"""Define a evaluation op.
Args:
batch: Batch produced by NSynthReader.
hparams: Hyperparameters.
config_name: Name of config module.
Returns:
eval_op: A complete evaluation op with summaries.
"""
phase = False if hparams.mag_only or hparams.raw_audio else True
config = utils.get_module("baseline.models.ae_configs.%s" % config_name)
if hparams.raw_audio:
x = batch["audio"]
# Add height and channel dims
x = tf.expand_dims(tf.expand_dims(x, 1), -1)
else:
x = batch["spectrogram"]
# Define the model
with tf.name_scope("Model"):
z = config.encode(x, hparams, is_training=False)
xhat = config.decode(z, batch, hparams, is_training=False)
# For interpolation
tf.add_to_collection("x", x)
tf.add_to_collection("pitch", batch["pitch"])
tf.add_to_collection("z", z)
tf.add_to_collection("xhat", xhat)
total_loss = compute_mse_loss(x, xhat, hparams)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
"Loss": slim.metrics.mean(total_loss),
})
# Define the summaries
for name, value in names_to_values.iteritems():
slim.summaries.add_scalar_summary(value, name, print_summary=True)
# Interpolate
with tf.name_scope("Interpolation"):
xhat = config.decode(z, batch, hparams, reuse=True, is_training=False)
# Linear interpolation
z_shift_one_example = tf.concat([z[1:], z[:1]], 0)
z_linear_half = (z + z_shift_one_example) / 2.0
xhat_linear_half = config.decode(z_linear_half, batch, hparams, reuse=True,
is_training=False)
# Pitch shift
pitch_plus_2 = tf.clip_by_value(batch["pitch"] + 2, 0, 127)
pitch_minus_2 = tf.clip_by_value(batch["pitch"] - 2, 0, 127)
batch["pitch"] = pitch_minus_2
xhat_pitch_minus_2 = config.decode(z, batch, hparams,
reuse=True, is_training=False)
batch["pitch"] = pitch_plus_2
xhat_pitch_plus_2 = config.decode(z, batch, hparams,
reuse=True, is_training=False)
utils.specgram_summaries(x, "Training Examples", hparams, phase=phase)
utils.specgram_summaries(xhat, "Reconstructions", hparams, phase=phase)
utils.specgram_summaries(
x - xhat, "Difference", hparams, audio=False, phase=phase)
utils.specgram_summaries(
xhat_linear_half, "Linear Interp. 0.5", hparams, phase=phase)
utils.specgram_summaries(xhat_pitch_plus_2, "Pitch +2", hparams, phase=phase)
utils.specgram_summaries(xhat_pitch_minus_2, "Pitch -2", hparams, phase=phase)
return names_to_updates.values()