def step_fn(batch):
with tf.GradientTape() as tape:
ae_features = self.encoder(self.prep_ae_input(batch))
cvcnn = self.cvcnn(
self.prep_cvcnn_inputs(batch['conditioned_occ'],
ae_features))
ae_output = self.decoder(ae_features)
p_occ = tf.nn.sigmoid(cvcnn['p_occ_logits'])
output = {'predicted_occ': p_occ, 'predicted_free': 1 - p_occ}
metrics = nn.calc_metrics(output, batch)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=cvcnn['p_occ_logits'], labels=batch['gt_occ'])
vcnn_loss = nn.reduce_sum_batch(cross_ent)
ae_loss = nn.reduce_sum_batch(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=ae_output, labels=batch['gt_occ']))
metrics['loss/aux_loss'] = ae_loss
loss = vcnn_loss + 0.1 * ae_loss
variables = self.trainable_variables
gradients = tape.gradient(loss, variables)
clipped_gradients = [
tf.clip_by_value(g, -1e6, 1e6) for g in gradients
]
self.opt.apply_gradients(
list(zip(clipped_gradients, variables)))
return loss, metrics
def step_fn(batch):
with tf.GradientTape() as tape:
output = self(batch)
metrics = nn.calc_metrics(output, batch)
if self.params['loss'] == 'cross_entropy':
cross_ent = tf.keras.losses.binary_crossentropy(
batch['gt_occ'], output['predicted_occ'])
loss = nn.reduce_sum_batch(cross_ent)
if self.params['stacknet_version'] == 'v4':
ae_loss = tf.reduce_sum(
tf.keras.losses.binary_crossentropy(
batch['gt_occ'], output['aux_occ']))
metrics['loss/aux_loss'] = ae_loss
metrics['loss/vcnn_loss'] = loss
loss = loss + 0.1 * ae_loss
variables = self.model.trainable_variables
gradients = tape.gradient(loss, variables)
clipped_gradients = [
tf.clip_by_value(g, -1e6, 1e6) for g in gradients
]
self.opt.apply_gradients(
list(zip(clipped_gradients, variables)))
return loss, metrics
def step_fn(batch):
with tf.GradientTape() as tape:
output_logits = self(batch, training=True)
x = tf.nn.sigmoid(output_logits['predicted_occ'])
output = {'predicted_occ': x, 'predicted_free': 1 - x}
metrics = nn.calc_metrics(output, batch)
if self.params['loss'] == 'mse':
loss = self.mse_loss(metrics)
elif self.params['loss'] == 'cross_entropy':
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=output_logits['predicted_occ'],
labels=batch['gt_occ'])
loss = nn.reduce_sum_batch(cross_ent)
variables = self.trainable_variables
gradients = tape.gradient(loss, variables)
self.opt.apply_gradients(list(zip(gradients, variables)))
metrics.update(self.get_insights(variables, gradients))
return loss, metrics
def step_fn_multiloss(batch):
with tf.GradientTape() as tape:
output_logits = self.model(self.prep_input(batch))
sample = tf.nn.sigmoid(output_logits)
output = {
'predicted_occ': sample,
'predicted_free': 1 - sample
}
metrics = nn.calc_metrics(output, batch)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=output_logits, labels=batch['gt_occ'])
loss = nn.reduce_sum_batch(cross_ent)
metrics['loss/0_step'] = loss
m = metrics
for i in range(5):
# Multistep part
# if True:
# i = 0
b = {
'conditioned_occ': tf.cast(output_logits > 0,
tf.float32)
}
output_logits = self.model(b)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=output_logits, labels=batch['gt_occ'])
step_loss = nn.reduce_sum_batch(cross_ent)
metrics['loss/{}_step'.format(i + 1)] = step_loss
# loss = loss + step_loss
# loss = tf.cond(metrics['pred|gt/p(predicted_occ|gt_occ)'] > 0.95,
# lambda: tf.add(step_loss, loss),
# lambda: loss)
variables = self.model.trainable_variables
gradients = tape.gradient(loss, variables)
clipped_gradients = [
tf.clip_by_value(g, -1e6, 1e6) for g in gradients
]
self.opt.apply_gradients(
list(zip(clipped_gradients, variables)))
return loss, metrics
def step_fn_multiloss(batch):
with tf.GradientTape() as tape:
ae_features = self.encoder(self.prep_ae_input(batch))
ae_output = self.decoder(ae_features)
ae_loss = nn.reduce_sum_batch(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=ae_output, labels=batch['gt_occ']))
cvcnn_inp = self.prep_cvcnn_inputs(batch['conditioned_occ'],
ae_features)
loss = ae_loss
for i in range(6):
cvcnn = self.cvcnn(cvcnn_inp)
if i == 0:
p_occ = tf.nn.sigmoid(cvcnn['p_occ_logits'])
output = {
'predicted_occ': p_occ,
'predicted_free': 1 - p_occ
}
metrics = nn.calc_metrics(output, batch)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=cvcnn['p_occ_logits'], labels=batch['gt_occ'])
step_loss = nn.reduce_sum_batch(cross_ent)
loss = loss + step_loss
metrics['loss/{}_step'.format(i)] = step_loss
cvcnn_inp = self.prep_cvcnn_inputs(
tf.cast(cvcnn['p_occ_logits'] > 0, tf.float32),
ae_features)
metrics['loss/aux_loss'] = ae_loss
variables = self.trainable_variables
gradients = tape.gradient(loss, variables)
clipped_gradients = [
tf.clip_by_value(g, -1e6, 1e6) for g in gradients
]
self.opt.apply_gradients(
list(zip(clipped_gradients, variables)))
return loss, metrics
def step_fn(batch):
with tf.GradientTape() as tape:
output_logits = self.model(self.prep_input(batch))
sample = tf.nn.sigmoid(output_logits)
output = {
'predicted_occ': sample,
'predicted_free': 1 - sample
}
metrics = nn.calc_metrics(output, batch)
cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=output_logits, labels=batch['gt_occ'])
loss = nn.reduce_sum_batch(cross_ent)
variables = self.model.trainable_variables
gradients = tape.gradient(loss, variables)
clipped_gradients = [
tf.clip_by_value(g, -1e6, 1e6) for g in gradients
]
self.opt.apply_gradients(
list(zip(clipped_gradients, variables)))
return loss, metrics
def mse_loss(self, metrics):
l_occ = nn.reduce_sum_batch(metrics['mse/occ'])
l_free = nn.reduce_sum_batch(metrics['mse/free'])
return l_occ + l_free
