def __init__(self, anchors, num_classes, match_threshold=0.7,
unmatched_threshold=0.3, rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
anchors: an instance of class Anchors.
num_classes: integer number representing number of classes in the dataset.
match_threshold: a float number between 0 and 1 representing the
lower-bound threshold to assign positive labels for anchors. An anchor
with a score over the threshold is labeled positive.
unmatched_threshold: a float number between 0 and 1 representing the
upper-bound threshold to assign negative labels for anchors. An anchor
with a score below the threshold is labeled negative.
rpn_batch_size_per_im: an integer number that represents the number of
sampled anchors per image in the first stage (region proposal network).
rpn_fg_fraction: a float number between 0 and 1 representing the fraction
of positive anchors (foreground) in the first stage.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
match_threshold,
unmatched_threshold=unmatched_threshold,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
self._target_assigner = target_assigner.TargetAssigner(
similarity_calc, matcher, box_coder)
self._anchors = anchors
self._match_threshold = match_threshold
self._unmatched_threshold = unmatched_threshold
self._rpn_batch_size_per_im = rpn_batch_size_per_im
self._rpn_fg_fraction = rpn_fg_fraction
self._num_classes = num_classes
def encode_labels(gt_boxes, gt_labels):
"""Labels anchors with ground truth inputs.
Args:
gt_boxes: A float tensor with shape [N, 4] representing groundtruth boxes.
For each row, it stores [y0, x0, y1, x1] for four corners of a box.
gt_labels: A integer tensor with shape [N, 1] representing groundtruth
classes.
Returns:
encoded_classes: a tensor with shape [num_anchors, 1].
encoded_boxes: a tensor with shape [num_anchors, 4].
num_positives: scalar tensor storing number of positives in an image.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
matched_threshold=ssd_constants.MATCH_THRESHOLD,
unmatched_threshold=ssd_constants.MATCH_THRESHOLD,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=ssd_constants.BOX_CODER_SCALES)
default_boxes = box_list.BoxList(
tf.convert_to_tensor(DefaultBoxes()('ltrb')))
target_boxes = box_list.BoxList(gt_boxes)
assigner = target_assigner.TargetAssigner(similarity_calc, matcher,
box_coder)
encoded_classes, _, encoded_boxes, _, matches = assigner.assign(
default_boxes, target_boxes, gt_labels)
num_matched_boxes = tf.reduce_sum(
tf.cast(tf.not_equal(matches.match_results, -1), tf.float32))
return encoded_classes, encoded_boxes, num_matched_boxes
def __init__(self, is_training, num_classes, params=DEFAULT_PARAMS):
"""
Args:
is_training: indicate training or not
num_classes: number of classes for prediction
params: parameters for model definition
resnet_arch: name of which resnet architecture used
"""
self._is_training = is_training
self._num_classes = num_classes
self._nms_fn = post_processing.batch_multiclass_non_max_suppression
self._score_convert_fn = tf.sigmoid
self._params = params
# self._unmatched_class_label = tf.constant([1] + (self._num_classes) * [0], tf.float32)
self._unmatched_class_label = tf.constant(
(self._num_classes + 1) * [0], tf.float32)
self._target_assigner = create_target_assigner(
unmatched_cls_target=self._unmatched_class_label)
self._anchors = None
self._anchor_generator = None
self._box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
def __init__(self, anchors, num_classes, match_threshold=0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
anchors: an instance of class Anchors.
num_classes: integer number representing number of classes in the dataset.
match_threshold: float number between 0 and 1 representing the threshold
to assign positive labels for anchors.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
match_threshold,
unmatched_threshold=match_threshold,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
self._target_assigner = target_assigner.TargetAssigner(
similarity_calc, matcher, box_coder)
self._anchors = anchors
self._match_threshold = match_threshold
self._num_classes = num_classes
def _model_fn(features, labels, mode, params, model):
"""Model defination for the SSD model based on ResNet-50.
Args:
features: the input image tensor with shape [batch_size, height, width, 3].
The height and width are fixed and equal.
labels: the input labels in a dictionary. The labels include class targets
and box targets which are dense label maps. The labels are generated from
get_input_fn function in data/dataloader.py
mode: the mode of TPUEstimator including TRAIN, EVAL, and PREDICT.
params: the dictionary defines hyperparameters of model. The default
settings are in default_hparams function in this file.
model: the SSD model outputs class logits and box regression outputs.
Returns:
spec: the EstimatorSpec or TPUEstimatorSpec to run training, evaluation,
or prediction.
"""
if mode == tf.estimator.ModeKeys.PREDICT:
labels = features
features = labels.pop('image')
features -= tf.constant(constants.NORMALIZATION_MEAN,
shape=[1, 1, 3],
dtype=features.dtype)
COEF_STD = 1.0 / tf.constant(
constants.NORMALIZATION_STD, shape=[1, 1, 3], dtype=features.dtype)
features *= COEF_STD
def _model_outputs():
return model(features,
params,
is_training_bn=(mode == tf.estimator.ModeKeys.TRAIN))
if params['dtype'] == 'bf16':
with tf.compat.v1.tpu.bfloat16_scope():
cls_outputs, box_outputs = _model_outputs()
levels = cls_outputs.keys()
for level in levels:
cls_outputs[level] = tf.cast(cls_outputs[level], tf.float32)
box_outputs[level] = tf.cast(box_outputs[level], tf.float32)
else:
cls_outputs, box_outputs = _model_outputs()
levels = cls_outputs.keys()
# First check if it is in PREDICT mode.
if mode == tf.estimator.ModeKeys.PREDICT:
flattened_cls, flattened_box = concat_outputs(cls_outputs, box_outputs,
True)
ssd_box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=constants.BOX_CODER_SCALES)
anchors = box_list.BoxList(
tf.convert_to_tensor(dataloader.DefaultBoxes()('ltrb')))
decoded_boxes = box_coder.batch_decode(encoded_boxes=flattened_box,
box_coder=ssd_box_coder,
anchors=anchors)
pred_scores = tf.nn.softmax(flattened_cls, axis=2)
pred_scores, indices = select_top_k_scores(
pred_scores, constants.MAX_NUM_EVAL_BOXES)
predictions = dict(
labels,
indices=indices,
pred_scores=pred_scores,
pred_box=decoded_boxes,
)
if params['visualize_dataloader']:
# this is for inference visualization.
predictions['image'] = features
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Load pretrained model from checkpoint.
if params['resnet_checkpoint'] and mode == tf.estimator.ModeKeys.TRAIN:
def scaffold_fn():
"""Loads pretrained model through scaffold function."""
tf.train.init_from_checkpoint(
params['resnet_checkpoint'], {
'/': 'resnet%s/' % constants.RESNET_DEPTH,
})
return tf.train.Scaffold()
else:
scaffold_fn = None
# Set up training loss and learning rate.
update_learning_rate_schedule_parameters(params)
global_step = tf.train.get_or_create_global_step()
learning_rate = learning_rate_schedule(params, global_step)
# cls_loss and box_loss are for logging. only total_loss is optimized.
loss, cls_loss, box_loss = detection_loss(cls_outputs, box_outputs, labels)
total_loss = loss + params['weight_decay'] * tf.add_n(
[tf.nn.l2_loss(v) for v in tf.trainable_variables()])
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.MomentumOptimizer(learning_rate,
momentum=constants.MOMENTUM)
if params['distributed_optimizer']:
optimizer = params['distributed_optimizer'](optimizer)
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group(optimizer.minimize(total_loss, global_step),
update_ops)
return model_fn_lib.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
scaffold=scaffold_fn())
if mode == tf.estimator.ModeKeys.EVAL:
raise NotImplementedError
def _parse_example(data):
with tf.name_scope('augmentation'):
source_id = data['source_id']
image = tf.image.convert_image_dtype(data['image'],
dtype=tf.float32)
raw_shape = tf.shape(image)
boxes = data['groundtruth_boxes']
classes = tf.reshape(data['groundtruth_classes'], [-1, 1])
# Only 80 of the 90 COCO classes are used.
class_map = tf.convert_to_tensor(ssd_constants.CLASS_MAP)
classes = tf.gather(class_map, classes)
classes = tf.cast(classes, dtype=tf.float32)
if self._is_training:
image, boxes, classes = ssd_crop(image, boxes, classes)
# random_horizontal_flip() is hard coded to flip with 50% chance.
mlperf_log.ssd_print(
key=mlperf_log.RANDOM_FLIP_PROBABILITY, value=0.5)
image, boxes = preprocessor.random_horizontal_flip(
image=image, boxes=boxes)
# TODO(shibow): Investigate the parameters for color jitter.
image = color_jitter(image,
brightness=0.125,
contrast=0.5,
saturation=0.5,
hue=0.05)
image = normalize_image(image)
if params['use_bfloat16']:
image = tf.cast(image, dtype=tf.bfloat16)
encoded_classes, encoded_boxes, num_matched_boxes = encode_labels(
boxes, classes)
# TODO(taylorrobie): Check that this cast is valid.
encoded_classes = tf.cast(encoded_classes, tf.int32)
labels = {
ssd_constants.NUM_MATCHED_BOXES: num_matched_boxes,
ssd_constants.BOXES: encoded_boxes,
ssd_constants.CLASSES: encoded_classes,
}
# This is for dataloader visualization; actual model doesn't use this.
if params['visualize_dataloader']:
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=ssd_constants.BOX_CODER_SCALES)
decoded_boxes = tf.expand_dims(box_coder.decode(
rel_codes=tf.squeeze(encoded_boxes),
anchors=box_list.BoxList(
tf.convert_to_tensor(
DefaultBoxes()('ltrb')))).get(),
axis=0)
labels['decoded_boxes'] = tf.squeeze(decoded_boxes)
return image, labels
else:
mlperf_log.ssd_print(key=mlperf_log.INPUT_SIZE,
value=ssd_constants.IMAGE_SIZE)
image = tf.image.resize_images(
image[tf.newaxis, :, :, :],
size=(ssd_constants.IMAGE_SIZE,
ssd_constants.IMAGE_SIZE))[0, :, :, :]
image = normalize_image(image)
if params['use_bfloat16']:
image = tf.cast(image, dtype=tf.bfloat16)
def trim_and_pad(inp_tensor, dim_1):
"""Limit the number of boxes, and pad if necessary."""
inp_tensor = inp_tensor[:ssd_constants.
MAX_NUM_EVAL_BOXES]
num_pad = ssd_constants.MAX_NUM_EVAL_BOXES - tf.shape(
inp_tensor)[0]
inp_tensor = tf.pad(inp_tensor, [[0, num_pad], [0, 0]])
return tf.reshape(
inp_tensor,
[ssd_constants.MAX_NUM_EVAL_BOXES, dim_1])
boxes, classes = trim_and_pad(boxes,
4), trim_and_pad(classes, 1)
return {
ssd_constants.IMAGE:
image,
ssd_constants.BOXES:
boxes,
ssd_constants.CLASSES:
classes,
ssd_constants.SOURCE_ID:
tf.string_to_number(source_id, tf.int32),
ssd_constants.RAW_SHAPE:
raw_shape,
}
def _model_fn(features, labels, mode, params, model):
"""Model defination for the SSD model based on ResNet-50.
Args:
features: the input image tensor with shape [batch_size, height, width, 3].
The height and width are fixed and equal.
labels: the input labels in a dictionary. The labels include class targets
and box targets which are dense label maps. The labels are generated from
get_input_fn function in data/dataloader.py
mode: the mode of TPUEstimator including TRAIN, EVAL, and PREDICT.
params: the dictionary defines hyperparameters of model. The default
settings are in default_hparams function in this file.
model: the SSD model outputs class logits and box regression outputs.
Returns:
spec: the EstimatorSpec or TPUEstimatorSpec to run training, evaluation,
or prediction.
"""
if mode == tf.estimator.ModeKeys.PREDICT:
labels = features
features = labels.pop('image')
# Manually apply the double transpose trick for training data.
if params['transpose_input'] and mode != tf.estimator.ModeKeys.PREDICT:
features = tf.transpose(features, [3, 0, 1, 2])
labels[ssd_constants.BOXES] = tf.transpose(labels[ssd_constants.BOXES],
[2, 0, 1])
labels[ssd_constants.CLASSES] = tf.transpose(
labels[ssd_constants.CLASSES], [2, 0, 1])
# Normalize the image to zero mean and unit variance.
mlperf_log.ssd_print(key=mlperf_log.DATA_NORMALIZATION_MEAN,
value=ssd_constants.NORMALIZATION_MEAN)
mlperf_log.ssd_print(key=mlperf_log.DATA_NORMALIZATION_STD,
value=ssd_constants.NORMALIZATION_STD)
features -= tf.constant(ssd_constants.NORMALIZATION_MEAN,
shape=[1, 1, 3],
dtype=features.dtype)
features /= tf.constant(ssd_constants.NORMALIZATION_STD,
shape=[1, 1, 3],
dtype=features.dtype)
def _model_outputs():
return model(features,
params,
is_training_bn=(mode == tf.estimator.ModeKeys.TRAIN))
if params['use_bfloat16']:
with bfloat16.bfloat16_scope():
cls_outputs, box_outputs = _model_outputs()
levels = cls_outputs.keys()
for level in levels:
cls_outputs[level] = tf.cast(cls_outputs[level], tf.float32)
box_outputs[level] = tf.cast(box_outputs[level], tf.float32)
else:
cls_outputs, box_outputs = _model_outputs()
levels = cls_outputs.keys()
# First check if it is in PREDICT mode.
if mode == tf.estimator.ModeKeys.PREDICT:
flattened_cls, flattened_box = concat_outputs(cls_outputs, box_outputs)
mlperf_log.ssd_print(key=mlperf_log.SCALES,
value=ssd_constants.BOX_CODER_SCALES)
ssd_box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=ssd_constants.BOX_CODER_SCALES)
anchors = box_list.BoxList(
tf.convert_to_tensor(dataloader.DefaultBoxes()('ltrb')))
decoded_boxes = box_coder.batch_decode(encoded_boxes=flattened_box,
box_coder=ssd_box_coder,
anchors=anchors)
pred_scores = tf.nn.softmax(flattened_cls, axis=2)
pred_scores, indices = select_top_k_scores(
pred_scores, ssd_constants.MAX_NUM_EVAL_BOXES)
predictions = dict(
labels,
indices=indices,
pred_scores=pred_scores,
pred_box=decoded_boxes,
)
if params['visualize_dataloader']:
# this is for inference visualization.
predictions['image'] = features
if params['use_tpu']:
return tpu_estimator.TPUEstimatorSpec(mode=mode,
predictions=predictions)
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Load pretrained model from checkpoint.
if params['resnet_checkpoint'] and mode == tf.estimator.ModeKeys.TRAIN:
def scaffold_fn():
"""Loads pretrained model through scaffold function."""
tf.train.init_from_checkpoint(
params['resnet_checkpoint'], {
'/': 'resnet%s/' % ssd_constants.RESNET_DEPTH,
})
return tf.train.Scaffold()
else:
scaffold_fn = None
# Set up training loss and learning rate.
update_learning_rate_schedule_parameters(params)
global_step = tf.train.get_or_create_global_step()
learning_rate = learning_rate_schedule(params, global_step)
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, deferred=True)
# cls_loss and box_loss are for logging. only total_loss is optimized.
total_loss, cls_loss, box_loss = detection_loss(cls_outputs, box_outputs,
labels)
total_loss += params['weight_decay'] * tf.add_n(
[tf.nn.l2_loss(v) for v in tf.trainable_variables()])
host_call = None
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.MomentumOptimizer(learning_rate,
momentum=ssd_constants.MOMENTUM)
if params['use_tpu']:
optimizer = tpu_optimizer.CrossShardOptimizer(optimizer)
mlperf_log.ssd_print(key=mlperf_log.OPT_NAME,
value='tf.train.MomentumOptimizer')
# TODO(wangtao): figure out how to log learning rate.
# mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=learning_rate)
mlperf_log.ssd_print(key=mlperf_log.OPT_MOMENTUM,
value=ssd_constants.MOMENTUM)
mlperf_log.ssd_print(key=mlperf_log.OPT_WEIGHT_DECAY,
value=params['weight_decay'])
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if params['device'] == 'gpu':
# GPU uses tf.group to avoid dependency overhead on update_ops; also,
# multi-GPU requires a different EstimatorSpec class object
train_op = tf.group(optimizer.minimize(total_loss, global_step),
update_ops)
return model_fn_lib.EstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op,
scaffold=scaffold_fn())
else:
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(total_loss, global_step)
if params['use_host_call']:
def host_call_fn(global_step, total_loss, cls_loss, box_loss,
learning_rate):
"""Training host call. Creates scalar summaries for training metrics.
This function is executed on the CPU and should not directly reference
any Tensors in the rest of the `model_fn`. To pass Tensors from the
model to the `metric_fn`, provide as part of the `host_call`. See
https://www.tensorflow.org/api_docs/python/tf/contrib/tpu/TPUEstimatorSpec
for more information.
Arguments should match the list of `Tensor` objects passed as the second
element in the tuple passed to `host_call`.
Args:
global_step: `Tensor with shape `[batch, ]` for the global_step.
total_loss: `Tensor` with shape `[batch, ]` for the training loss.
cls_loss: `Tensor` with shape `[batch, ]` for the training cls loss.
box_loss: `Tensor` with shape `[batch, ]` for the training box loss.
learning_rate: `Tensor` with shape `[batch, ]` for the learning_rate.
Returns:
List of summary ops to run on the CPU host.
"""
# Outfeed supports int32 but global_step is expected to be int64.
global_step = tf.reduce_mean(global_step)
# Host call fns are executed FLAGS.iterations_per_loop times after one
# TPU loop is finished, setting max_queue value to the same as number of
# iterations will make the summary writer only flush the data to storage
# once per loop.
with (tf.contrib.summary.create_file_writer(
params['model_dir'],
max_queue=params['iterations_per_loop']).as_default()):
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('total_loss',
tf.reduce_mean(total_loss),
step=global_step)
tf.contrib.summary.scalar('cls_loss',
tf.reduce_mean(cls_loss),
step=global_step)
tf.contrib.summary.scalar('box_loss',
tf.reduce_mean(box_loss),
step=global_step)
tf.contrib.summary.scalar(
'learning_rate',
tf.reduce_mean(learning_rate),
step=global_step)
return tf.contrib.summary.all_summary_ops()
# To log the loss, current learning rate, and epoch for Tensorboard, the
# summary op needs to be run on the host CPU via host_call. host_call
# expects [batch_size, ...] Tensors, thus reshape to introduce a batch
# dimension. These Tensors are implicitly concatenated to
# [params['batch_size']].
global_step_t = tf.reshape(global_step, [1])
total_loss_t = tf.reshape(total_loss, [1])
cls_loss_t = tf.reshape(cls_loss, [1])
box_loss_t = tf.reshape(box_loss, [1])
learning_rate_t = tf.reshape(learning_rate, [1])
host_call = (host_call_fn, [
global_step_t, total_loss_t, cls_loss_t, box_loss_t,
learning_rate_t
])
else:
train_op = None
eval_metrics = None
if mode == tf.estimator.ModeKeys.EVAL:
raise NotImplementedError
return tpu_estimator.TPUEstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op,
host_call=host_call,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
def _parse_example(data):
with tf.name_scope('augmentation'):
source_id = data['source_id']
image = data['image'] # dtype uint8
raw_shape = tf.shape(image)
boxes = data['groundtruth_boxes']
classes = tf.reshape(data['groundtruth_classes'], [-1, 1])
# Only 80 of the 90 COCO classes are used.
class_map = tf.convert_to_tensor(ssd_constants.CLASS_MAP)
classes = tf.gather(class_map, classes)
classes = tf.cast(classes, dtype=tf.float32)
if self._is_training:
image, boxes, classes = ssd_crop(image, boxes, classes)
# ssd_crop resizes and returns image of dtype float32 and does not
# change its range (i.e., value in between 0--255). Divide by 255.
# converts it to [0, 1] range. Not doing this before cropping to
# avoid dtype cast (which incurs additional memory copy).
image /= 255.0
# random_horizontal_flip() is hard coded to flip with 50% chance.
image, boxes = preprocessor.random_horizontal_flip(
image=image, boxes=boxes)
# TODO(shibow): Investigate the parameters for color jitter.
image = color_jitter(image,
brightness=0.125,
contrast=0.5,
saturation=0.5,
hue=0.05)
if params['use_bfloat16']:
image = tf.cast(image, dtype=tf.bfloat16)
encoded_classes, encoded_boxes, num_matched_boxes = encode_labels(
boxes, classes)
# TODO(taylorrobie): Check that this cast is valid.
encoded_classes = tf.cast(encoded_classes, tf.int32)
labels = {
ssd_constants.NUM_MATCHED_BOXES: num_matched_boxes,
ssd_constants.BOXES: encoded_boxes,
ssd_constants.CLASSES: tf.squeeze(encoded_classes,
axis=1),
}
# This is for dataloader visualization; actual model doesn't use this.
if params['visualize_dataloader']:
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=ssd_constants.BOX_CODER_SCALES)
decoded_boxes = tf.expand_dims(box_coder.decode(
rel_codes=tf.squeeze(encoded_boxes),
anchors=box_list.BoxList(
tf.convert_to_tensor(
DefaultBoxes()('ltrb')))).get(),
axis=0)
labels['decoded_boxes'] = tf.squeeze(decoded_boxes)
return image, labels
else:
image = tf.image.resize_images(
image,
size=(ssd_constants.IMAGE_SIZE,
ssd_constants.IMAGE_SIZE))
# resize_image returns image of dtype float32 and does not change its
# range. Divide by 255 to convert image to [0, 1] range.
image /= 255.
if params['use_bfloat16']:
image = tf.cast(image, dtype=tf.bfloat16)
def trim_and_pad(inp_tensor, dim_1):
"""Limit the number of boxes, and pad if necessary."""
inp_tensor = inp_tensor[:ssd_constants.
MAX_NUM_EVAL_BOXES]
num_pad = ssd_constants.MAX_NUM_EVAL_BOXES - tf.shape(
inp_tensor)[0]
inp_tensor = tf.pad(inp_tensor, [[0, num_pad], [0, 0]])
return tf.reshape(
inp_tensor,
[ssd_constants.MAX_NUM_EVAL_BOXES, dim_1])
boxes, classes = trim_and_pad(boxes,
4), trim_and_pad(classes, 1)
sample = {
ssd_constants.IMAGE:
image,
ssd_constants.BOXES:
boxes,
ssd_constants.CLASSES:
classes,
ssd_constants.SOURCE_ID:
tf.string_to_number(source_id, tf.int32),
ssd_constants.RAW_SHAPE:
raw_shape,
}
if not self._is_training and self._count > params[
'eval_samples']:
sample[ssd_constants.IS_PADDED] = data[
ssd_constants.IS_PADDED]
return sample
