def _compute_losses_and_predictions_dicts(model,
features,
labels,
add_regularization_loss=True):
model_lib.provide_groundtruth(model, labels)
preprocessed_images = features[fields.InputDataFields.image]
prediction_dict = model.predict(
preprocessed_images, features[fields.InputDataFields.true_image_shape],
**model.get_side_inputs(features))
prediction_dict = ops.bfloat16_to_float32_nested(prediction_dict)
losses_dict = model.loss(prediction_dict,
features[fields.InputDataFields.true_image_shape])
losses = [loss_tensor for loss_tensor in losses_dict.values()]
if add_regularization_loss:
regularization_losses = model.regularization_losses()
if regularization_losses:
regularization_losses = ops.bfloat16_to_float32_nested(
regularization_losses)
regularization_loss = tf.add_n(regularization_losses,
name="regularization_loss")
losses.append(regularization_loss)
losses_dict["Loss/regularization_loss"] = regularization_loss
total_loss = tf.add_n(losses, name="total_loss")
losses_dict["Loss/total_loss"] = total_loss
return losses_dict, prediction_dict
def _compute_losses_and_predictions_dicts(model,
features,
labels,
add_regularization_loss=True,
use_tpu=False,
use_bfloat16=False):
model_lib.provide_groundtruth(model, labels)
preprocessed_images = features[fields.InputDataFields.image]
# TODO(kaftan): Check how we're supposed to do this mixed precision stuff
## in TF2 TPUStrategy + Keras
if use_tpu and use_bfloat16:
with tf.contrib.tpu.bfloat16_scope():
prediction_dict = model.predict(
preprocessed_images,
features[fields.InputDataFields.true_image_shape])
prediction_dict = ops.bfloat16_to_float32_nested(prediction_dict)
else:
prediction_dict = model.predict(
preprocessed_images,
features[fields.InputDataFields.true_image_shape])
losses_dict = model.loss(prediction_dict,
features[fields.InputDataFields.true_image_shape])
losses = [loss_tensor for loss_tensor in losses_dict.values()]
if add_regularization_loss:
# TODO(kaftan): As we figure out mixed precision & bfloat 16, we may
## need to convert these regularization losses from bfloat16 to float32
## as well.
regularization_losses = model.regularization_losses()
if regularization_losses:
regularization_loss = tf.add_n(regularization_losses,
name='regularization_loss')
losses.append(regularization_loss)
losses_dict['Loss/regularization_loss'] = regularization_loss
total_loss = tf.add_n(losses, name='total_loss')
losses_dict['Loss/total_loss'] = total_loss
return losses_dict, prediction_dict
def _compute_losses_and_predictions_dicts(
model, features, labels,
add_regularization_loss=True):
"""Computes the losses dict and predictions dict for a model on inputs.
Args:
model: a DetectionModel (based on Keras).
features: Dictionary of feature tensors from the input dataset.
Should be in the format output by `inputs.train_input` and
`inputs.eval_input`.
features[fields.InputDataFields.image] is a [batch_size, H, W, C]
float32 tensor with preprocessed images.
features[HASH_KEY] is a [batch_size] int32 tensor representing unique
identifiers for the images.
features[fields.InputDataFields.true_image_shape] is a [
batch_size, 3]
int32 tensor representing the true image shapes, as preprocessed
images could be padded.
features[fields.InputDataFields.original_image] (optional) is a
[batch_size, H, W, C] float32 tensor with original images.
labels: A dictionary of groundtruth tensors post-unstacking. The original
labels are of the form returned by `inputs.train_input` and
`inputs.eval_input`. The shapes may have been modified by unstacking with
`model_lib.unstack_batch`. However, the dictionary includes the following
fields.
labels[fields.InputDataFields.num_groundtruth_boxes] is a
int32 tensor indicating the number of valid groundtruth boxes
per image.
labels[fields.InputDataFields.groundtruth_boxes] is a float32 tensor
containing the corners of the groundtruth boxes.
labels[fields.InputDataFields.groundtruth_classes] is a float32
one-hot tensor of classes.
labels[fields.InputDataFields.groundtruth_weights] is a float32 tensor
containing groundtruth weights for the boxes.
-- Optional --
labels[fields.InputDataFields.groundtruth_instance_masks] is a
float32 tensor containing only binary values, which represent
instance masks for objects.
labels[fields.InputDataFields.groundtruth_keypoints] is a
float32 tensor containing keypoints for each box.
labels[fields.InputDataFields.groundtruth_dp_num_points] is an int32
tensor with the number of sampled DensePose points per object.
labels[fields.InputDataFields.groundtruth_dp_part_ids] is an int32
tensor with the DensePose part ids (0-indexed) per object.
labels[fields.InputDataFields.groundtruth_dp_surface_coords] is a
float32 tensor with the DensePose surface coordinates.
labels[fields.InputDataFields.groundtruth_group_of] is a tf.bool tensor
containing group_of annotations.
labels[fields.InputDataFields.groundtruth_labeled_classes] is a float32
k-hot tensor of classes.
labels[fields.InputDataFields.groundtruth_track_ids] is a int32
tensor of track IDs.
labels[fields.InputDataFields.groundtruth_keypoint_depths] is a
float32 tensor containing keypoint depths information.
labels[fields.InputDataFields.groundtruth_keypoint_depth_weights] is a
float32 tensor containing the weights of the keypoint depth feature.
add_regularization_loss: Whether or not to include the model's
regularization loss in the losses dictionary.
Returns:
A tuple containing the losses dictionary (with the total loss under
the key 'Loss/total_loss'), and the predictions dictionary produced by
`model.predict`.
"""
model_lib.provide_groundtruth(model, labels)
preprocessed_images = features[fields.InputDataFields.image]
prediction_dict = model.predict(
preprocessed_images,
features[fields.InputDataFields.true_image_shape],
**model.get_side_inputs(features))
prediction_dict = ops.bfloat16_to_float32_nested(prediction_dict)
losses_dict = model.loss(
prediction_dict, features[fields.InputDataFields.true_image_shape])
losses = [loss_tensor for loss_tensor in losses_dict.values()]
if add_regularization_loss:
# TODO(kaftan): As we figure out mixed precision & bfloat 16, we may
# need to convert these regularization losses from bfloat16 to float32
# as well.
regularization_losses = model.regularization_losses()
if regularization_losses:
regularization_losses = ops.bfloat16_to_float32_nested(
regularization_losses)
regularization_loss = tf.add_n(
regularization_losses, name='regularization_loss')
losses.append(regularization_loss)
losses_dict['Loss/regularization_loss'] = regularization_loss
total_loss = tf.add_n(losses, name='total_loss')
losses_dict['Loss/total_loss'] = total_loss
return losses_dict, prediction_dict
def _compute_losses_and_predictions_dicts(model,
features,
labels,
add_regularization_loss=True,
use_tpu=False,
use_bfloat16=False):
"""Computes the losses dict and predictions dict for a model on inputs.
Args:
model: a DetectionModel (based on Keras).
features: Dictionary of feature tensors from the input dataset.
Should be in the format output by `inputs.train_input` and
`inputs.eval_input`.
features[fields.InputDataFields.image] is a [batch_size, H, W, C]
float32 tensor with preprocessed images.
features[HASH_KEY] is a [batch_size] int32 tensor representing unique
identifiers for the images.
features[fields.InputDataFields.true_image_shape] is a [batch_size, 3]
int32 tensor representing the true image shapes, as preprocessed
images could be padded.
features[fields.InputDataFields.original_image] (optional) is a
[batch_size, H, W, C] float32 tensor with original images.
labels: A dictionary of groundtruth tensors post-unstacking. The original
labels are of the form returned by `inputs.train_input` and
`inputs.eval_input`. The shapes may have been modified by unstacking with
`model_lib.unstack_batch`. However, the dictionary includes the following
fields.
labels[fields.InputDataFields.num_groundtruth_boxes] is a
int32 tensor indicating the number of valid groundtruth boxes
per image.
labels[fields.InputDataFields.groundtruth_boxes] is a float32 tensor
containing the corners of the groundtruth boxes.
labels[fields.InputDataFields.groundtruth_classes] is a float32
one-hot tensor of classes.
labels[fields.InputDataFields.groundtruth_weights] is a float32 tensor
containing groundtruth weights for the boxes.
-- Optional --
labels[fields.InputDataFields.groundtruth_instance_masks] is a
float32 tensor containing only binary values, which represent
instance masks for objects.
labels[fields.InputDataFields.groundtruth_keypoints] is a
float32 tensor containing keypoints for each box.
add_regularization_loss: Whether or not to include the model's
regularization loss in the losses dictionary.
use_tpu: Whether computation should happen on a TPU.
use_bfloat16: Whether computation on a TPU should use bfloat16.
Returns:
A tuple containing the losses dictionary (with the total loss under
the key 'Loss/total_loss'), and the predictions dictionary produced by
`model.predict`.
"""
model_lib.provide_groundtruth(model, labels)
preprocessed_images = features[fields.InputDataFields.image]
# TODO(kaftan): Check how we're supposed to do this mixed precision stuff
## in TF2 TPUStrategy + Keras
if use_tpu and use_bfloat16:
with tf.contrib.tpu.bfloat16_scope():
prediction_dict = model.predict(
preprocessed_images,
features[fields.InputDataFields.true_image_shape])
prediction_dict = ops.bfloat16_to_float32_nested(prediction_dict)
else:
prediction_dict = model.predict(
preprocessed_images,
features[fields.InputDataFields.true_image_shape])
losses_dict = model.loss(prediction_dict,
features[fields.InputDataFields.true_image_shape])
losses = [loss_tensor for loss_tensor in losses_dict.values()]
if add_regularization_loss:
# TODO(kaftan): As we figure out mixed precision & bfloat 16, we may
## need to convert these regularization losses from bfloat16 to float32
## as well.
regularization_losses = model.regularization_losses()
if regularization_losses:
regularization_loss = tf.add_n(regularization_losses,
name='regularization_loss')
losses.append(regularization_loss)
losses_dict['Loss/regularization_loss'] = regularization_loss
total_loss = tf.add_n(losses, name='total_loss')
losses_dict['Loss/total_loss'] = total_loss
return losses_dict, prediction_dict
