def _cumsum_gradient(score_map, box):
b, n, m, c = utils.get_tensor_shape(score_map)
_, p, _ = utils.get_tensor_shape(box)
expanded_box = _get_expanded_box(box,
img_h=n,
img_w=m,
border_ratio=border_ratio)
(box_h, box_w) = _get_box_shape(box)
(expanded_box_h, expanded_box_w) = _get_box_shape(expanded_box)
cumsum = imgproc.calc_cumsum_2d(
score_map, tf.concat([box, expanded_box], axis=1))
area = tf.expand_dims(tf.cast(box_h * box_w, tf.float32), axis=-1)
area_border = tf.expand_dims(tf.cast(
expanded_box_h * expanded_box_w - box_h * box_w, tf.float32),
axis=-1)
avg_val = tf.div(cumsum[:, :p, :], tf.maximum(_SMALL_NUMBER, area))
avg_val_in_border = tf.div(cumsum[:, p:, :] - cumsum[:, :p, :],
tf.maximum(_SMALL_NUMBER, area_border))
return avg_val - avg_val_in_border
def calc_cumsum_2d(image, box):
"""Computes the cumulative sum give pre-defiend boxes.
i_a (ymin, xmin), ..., i_b (ymin, xmax)
i_c (ymax, xmin), ..., i_d (ymax, xmax)
Args:
image: 4-D float `Tensor` of size [b, n, m, c], representing `b` images with
height `n`, width `m`, and channels `c`.
box: 3-D int64 `Tensor` of size [b, p, 4], representing `b` examples each
with `p` proposals in the format of [ymin, xmin, ymax, xmax].
Returns:
cumsum: 3-D float `Tensor` of size [b, p, c], channel-wise cumulative sum.
"""
b, n, m, c = utils.get_tensor_shape(image)
_, p, _ = utils.get_tensor_shape(box)
cumsum = calc_integral_image(image)
ymin, xmin, ymax, xmax = tf.unstack(box, axis=-1)
i = tf.range(tf.cast(b, tf.int64), dtype=tf.int64)
i = tf.tile(tf.expand_dims(i, axis=-1), [1, p])
i_a = tf.gather_nd(cumsum, tf.stack([i, ymin, xmin], axis=-1))
i_b = tf.gather_nd(cumsum, tf.stack([i, ymin, xmax], axis=-1))
i_c = tf.gather_nd(cumsum, tf.stack([i, ymax, xmin], axis=-1))
i_d = tf.gather_nd(cumsum, tf.stack([i, ymax, xmax], axis=-1))
return i_d + i_a - i_b - i_c
def extract_labels(self, examples):
"""Extracts the pseudo labels.
Args:
examples: A dictionary involving image-level annotations.
Returns:
labels: A [batch, num_classes] tensor denoting the presence of classes.
"""
with tf.name_scope('extend_match_extractor'):
items = self._name2id.items()
keys = [k for k, v in items]
values = [v for k, v in items]
table = tf.contrib.lookup.HashTable(
initializer=tf.contrib.lookup.KeyValueTensorInitializer(keys, values),
default_value=self.
num_classes) # Class ID for Out-of-Vocabulary words.
ids = table.lookup(examples[InputDataFields.concat_caption_string])
labels = tf.one_hot(
indices=ids, depth=1 + self.num_classes, dtype=tf.float32)
batch, num_tokens = utils.get_tensor_shape(
examples[InputDataFields.concat_caption_string])
labels = tf.cond(
num_tokens > 0,
true_fn=lambda: tf.reduce_max(labels, axis=1)[:, :-1],
false_fn=lambda: tf.zeros(shape=[batch, self.num_classes]))
return labels
def _match_labels(class_texts, vocabulary_list):
"""Matches labels from texts.
Args:
class_texts: A [batch, num_tokens] string tensor.
Returns:
A [batch, num_classes] float tensor.
"""
keys = [class_name for class_id, class_name in enumerate(vocabulary_list)]
values = [class_id for class_id, class_name in enumerate(vocabulary_list)]
table = tf.contrib.lookup.HashTable(
initializer=tf.contrib.lookup.KeyValueTensorInitializer(keys, values),
default_value=len(
vocabulary_list)) # Class ID for Out-of-Vocabulary words.
ids = table.lookup(class_texts)
labels = tf.one_hot(
indices=ids, depth=1 + len(vocabulary_list), dtype=tf.float32)
batch, num_tokens = utils.get_tensor_shape(class_texts)
labels = tf.cond(
num_tokens > 0,
true_fn=lambda: tf.reduce_max(labels, axis=1)[:, :-1],
false_fn=lambda: tf.zeros(shape=[batch, len(vocabulary_list)]))
return labels
def resize_image_to_size(image,
new_height=600,
new_width=1024,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False):
"""Resizes images to the given height and width.
Args:
image: A 3D tensor of shape [height, width, channels]
new_height: (optional) (scalar) desired height of the image.
new_width: (optional) (scalar) desired width of the image.
method: (optional) interpolation method used in resizing. Defaults to
BILINEAR.
align_corners: bool. If true, exactly align all 4 corners of the input
and output. Defaults to False.
Returns:
resized_image: A tensor of size [new_height, new_width, channels].
resized_image_shape: A 1D tensor of shape [3] containing the shape of the
resized image.
"""
with tf.name_scope("resize_image_to_size"):
new_image = tf.image.resize_images(image,
tf.stack([new_height, new_width]),
method=method,
align_corners=align_corners)
image_shape = utils.get_tensor_shape(image)
return new_image, tf.stack([new_height, new_width, image_shape[2]])
def _extract_class_label(self, class_texts, vocabulary_list):
"""Extracts class labels.
Args:
class_texts: a [batch, max_num_objects] string tensor.
vocabulary_list: a list of words of length `num_classes`.
Returns:
labels: a [batch, num_classes] float tensor.
"""
with tf.name_scope('extract_class_label'):
batch, _ = utils.get_tensor_shape(class_texts)
categorical_col = tf.feature_column.categorical_column_with_vocabulary_list(
key='name_to_id',
vocabulary_list=vocabulary_list,
num_oov_buckets=1)
indicator_col = tf.feature_column.indicator_column(categorical_col)
indicator = tf.feature_column.input_layer(
{'name_to_id': class_texts}, feature_columns=[indicator_col])
labels = tf.cast(indicator[:, :-1] > 0, tf.float32)
# if isinstance(batch, int):
# labels.set_shape([batch, len(vocabulary_list)])
# else:
# labels.set_shape([None, len(vocabulary_list)])
labels.set_shape([batch, len(vocabulary_list)])
return labels
def _batch_scale_box_fn(examples):
(image, image_shape, object_boxes,
proposal_boxes) = (examples[InputDataFields.image],
examples[InputDataFields.image_shape],
examples[InputDataFields.object_boxes],
examples[InputDataFields.proposals])
_, pad_h, pad_w, _ = utils.get_tensor_shape(image)
img_h, img_w, _ = tf.unstack(image_shape, axis=-1)
def _scale_box(box):
ymin, xmin, ymax, xmax = tf.unstack(box, axis=-1)
ymin = ymin * tf.to_float(tf.expand_dims(img_h,
axis=-1)) / tf.to_float(pad_h)
xmin = xmin * tf.to_float(tf.expand_dims(img_w,
axis=-1)) / tf.to_float(pad_w)
ymax = ymax * tf.to_float(tf.expand_dims(img_h,
axis=-1)) / tf.to_float(pad_h)
xmax = xmax * tf.to_float(tf.expand_dims(img_w,
axis=-1)) / tf.to_float(pad_w)
return tf.stack([ymin, xmin, ymax, xmax], axis=-1)
examples[InputDataFields.object_boxes] = _scale_box(object_boxes)
examples[InputDataFields.proposals] = _scale_box(proposal_boxes)
return examples
def _extract_class_label(self, num_captions, caption_strings,
caption_lengths, vocabulary_list):
"""Encodes labels.
Args:
num_captions: a [batch] int tensor, should always be ONE.
caption_strings: a [batch, num_captions, max_caption_len] string tensor.
caption_lengths: a [batch, num_captions] int tensor.
vocabulary_list: a list of words of length `num_classes`.
Returns:
class_label: a [batch, num_classes] float tensor.
"""
with tf.name_scope('extract_class_label'):
batch, num_captions, max_caption_len = utils.get_tensor_shape(
caption_strings)
caption_string = caption_strings[:, 0, :]
caption_length = caption_lengths[:, 0]
categorical_col = tf.feature_column.categorical_column_with_vocabulary_list(
key='name_to_class_id',
vocabulary_list=vocabulary_list,
num_oov_buckets=1)
indicator_col = tf.feature_column.indicator_column(categorical_col)
indicator = tf.feature_column.input_layer(
{'name_to_class_id': caption_strings},
feature_columns=[indicator_col])
class_label = tf.cast(indicator[:, :-1] > 0, tf.float32)
class_label.set_shape([batch, len(vocabulary_list)])
return class_label
def _calc_anchor_scores(self,
class_activation_map,
anchors,
resize_height=224,
resize_width=224,
num_boxes_per_class=100):
"""Calculates class activation box based on the class activation map.
Args:
class_act_map: A [batch, height, width, num_classes] float tensor.
anchor_boxes: A [batch, number_of_anchors, 4] float tensor.
Returns:
anchor_scores: A [batch, number_of_anchors, num_classes] tensor.
"""
with tf.name_scope('calc_anchor_scores'):
class_activation_map = tf.image.resize_images(
class_activation_map, [resize_height, resize_width])
batch, height, width, num_classes = utils.get_tensor_shape(
class_activation_map)
ymin, xmin, ymax, xmax = tf.unstack(anchors, axis=-1)
anchors_absolute = tf.stack([
tf.to_int64(tf.round(ymin * tf.to_float(height))),
tf.to_int64(tf.round(xmin * tf.to_float(width))),
tf.to_int64(tf.round(ymax * tf.to_float(height))),
tf.to_int64(tf.round(xmax * tf.to_float(width)))
],
axis=-1)
fn = model_utils.build_proposal_saliency_fn(func_name='wei',
border_ratio=0.2,
purity_weight=1.0)
anchor_scores = fn(class_activation_map, anchors_absolute)
return anchor_scores
def _encode_captions(self,
caption_strings,
vocabulary_list,
common_dimensions=300,
scope="coco_word_embedding",
is_training=False):
"""Builds caption model.
Args:
caption_strings: captions in the batch, a [num_captions_in_batch,
max_caption_length] string tensor.
vocabulary_list: words in the vocabulary, a list of python strings.
common_dimensions: dimensions of the word embedding.
is_training: if True, training graph is built.
Returns:
text_feature: embedding of each word, a [num_captions_in_batch,
max_caption_length, common_dimensions] tensor.
"""
(num_captions_in_batch,
max_caption_length) = utils.get_tensor_shape(caption_strings)
caption_strings_flattened = tf.reshape(caption_strings, [-1])
text_feature_flattened = self._encode_words(caption_strings_flattened,
common_dimensions,
vocabulary_list)
text_feature = tf.reshape(
text_feature_flattened,
[num_captions_in_batch, max_caption_length, common_dimensions])
return text_feature
def build_loss(self, predictions, examples, **kwargs):
"""Build tf graph to compute loss.
Args:
predictions: dict of prediction results keyed by name.
examples: dict of inputs keyed by name.
Returns:
loss_dict: dict of loss tensors keyed by name.
"""
loss_dict = {}
with tf.name_scope('losses'):
# Extract image-level labels.
labels = self._extract_class_label(
class_texts=examples[InputDataFields.caption_strings],
vocabulary_list=self._vocabulary_list)
# Loss of the multi-instance detection network.
midn_logits = predictions[OICRPredictions.midn_logits]
losses = tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=midn_logits)
loss_dict['midn_cross_entropy_loss'] = tf.reduce_mean(losses)
# Losses of the online instance classifier refinement network.
options = self._model_proto
(num_proposals, proposals, proposal_scores_0) = (
predictions[DetectionResultFields.num_proposals],
predictions[DetectionResultFields.proposal_boxes],
predictions[OICRPredictions.midn_proba_r_given_c])
batch, max_num_proposals, _ = utils.get_tensor_shape(
proposal_scores_0)
proposal_scores_0 = tf.concat([
tf.fill([batch, max_num_proposals, 1], 0.0), proposal_scores_0
],
axis=-1)
for i in range(options.oicr_iterations):
proposal_scores_1 = predictions[
OICRPredictions.oicr_proposal_scores +
'_at_{}'.format(i + 1)]
loss_dict['oicr_cross_entropy_loss_at_{}'.format(
i + 1)] = self._calc_oicr_loss(
labels,
num_proposals,
proposals,
proposal_scores_0,
proposal_scores_1,
scope='oicr_{}'.format(i + 1),
iou_threshold=options.oicr_iou_threshold)
proposal_scores_0 = proposal_scores_1
return loss_dict
def gaussian_filter(inputs, ksize=3):
"""Applies Gaussian filter to the inputs.
Args:
inputs: input images, a [batch, height, width, channels] float tensor.
ksize: aperture size of the Gaussian kernel.
Returns:
outputs: output images, a [batch, height, width, channels] float tensor.
"""
batch, height, width, channels = utils.get_tensor_shape(inputs)
kernel = gaussian_kernel(ksize)
kernel = tf.reshape(tf.constant(kernel), [ksize, ksize, 1, 1])
outputs = []
channel_images = tf.split(inputs, num_or_size_splits=channels, axis=-1)
for channel_image in channel_images:
outputs.append(
tf.nn.conv2d(
channel_image,
kernel, [1, 1, 1, 1],
padding='SAME',
data_format="NHWC",
name="gaussian_filter"))
return tf.concat(outputs, axis=-1)
def _extract_text_feature(self,
text_strings,
text_lengths,
vocabulary_list,
initial_embedding=None,
embedding_dims=50,
trainable=True,
max_norm=None):
"""Extracts text feature.
Args:
text_strings: A [batch, max_text_length] string tensor.
text_lengths: A [batch] int tensor.
vocabulary_list: A list of words.
Returns:
text_features: a [batch, max_text_length, feature_dims] float tensor.
"""
batch, max_text_length = utils.get_tensor_shape(text_strings)
text_strings_flattented = tf.reshape(text_strings, [-1])
token_ids_flatterned, text_features_flattened = self._encode_tokens(
text_strings_flattented, embedding_dims, vocabulary_list,
initial_embedding, trainable)
token_ids = tf.reshape(token_ids_flatterned, [batch, max_text_length])
text_features = tf.reshape(text_features_flattened,
[batch, max_text_length, embedding_dims])
return token_ids, text_features
def _batch_resize_image_fn(examples):
# Resize image, height and width denote the padding size.
image = examples[InputDataFields.image]
_, height, width, channels = utils.get_tensor_shape(image)
index = tf.random_uniform([],
minval=0,
maxval=len(options.batch_resize_scale_value),
dtype=tf.int32)
scale_h = scale_w = tf.gather([x for x in options.batch_resize_scale_value],
index)
new_height = tf.to_int32(tf.round(scale_h * tf.to_float(height)))
new_width = tf.to_int32(tf.round(scale_w * tf.to_float(width)))
new_image = tf.image.resize_images(image, tf.stack([new_height, new_width]))
examples[InputDataFields.image] = new_image
# Modify the image_shape, height and width denote the image size.
image_shape = examples[InputDataFields.image_shape]
height, width, channels = tf.unstack(image_shape, axis=-1)
new_height = tf.to_int32(tf.round(scale_h * tf.to_float(height)))
new_width = tf.to_int32(tf.round(scale_w * tf.to_float(width)))
new_image_shape = tf.stack([new_height, new_width, channels], axis=-1)
examples[InputDataFields.image_shape] = new_image_shape
return examples
def sample_negatives_randomly(num_captions, caption_strings, caption_lengths):
"""Samples negative examples randomly.
Args:
num_pos_captions: number of captions of each example, a [batch] int tensor.
caption_strings: caption data, a
[batch, max_num_captions, max_caption_length] string tensor.
caption_lengths: length of each caption, a [batch, max_num_captions] int
tensor.
Returns:
num_neg_captions: number of captions of each example, a [batch] int tensor.
neg_caption_strings: caption data, a
[batch, max_num_captions, max_caption_length] string tensor.
neg_caption_lengths: length of each caption, a [batch, max_num_captions] int
tensor.
"""
batch = utils.get_tensor_shape(num_captions)[0]
offset = tf.random_uniform([batch], maxval=batch - 1, dtype=tf.int32)
index = tf.range(batch, dtype=tf.int32)
sampled_index = tf.mod(index + offset, batch)
return (tf.gather(num_captions, sampled_index),
tf.gather(caption_strings,
sampled_index), tf.gather(caption_lengths,
sampled_index))
def _predict_image_score_map(self, examples):
"""Builds tf graph for .
Args:
examples: dict of input tensors keyed by name.
Returns:
predictions: dict of prediction results keyed by name.
"""
options = self._model_proto
is_training = self._is_training
image = examples[InputDataFields.image]
# Keep image size for resizing saliency and activation map later.
batch, height, width, channels = utils.get_tensor_shape(image)
class_act_map_predictions = self._calc_class_act_map(examples)
class_act_map = class_act_map_predictions[VOCPredictions.class_act_map]
def _resize_fn(image, ksize=32):
resized_image = tf.image.resize_images(image, [height, width])
if ksize:
smoothed_image = imgproc.gaussian_filter(resized_image, ksize=ksize)
else:
smoothed_image = resized_image
return smoothed_image
predictions = {
VOCPredictionTasks.image_saliency: tf.zeros([batch, height, width, 1]),
VOCPredictionTasks.image_score_map: _resize_fn(class_act_map),
}
return predictions
def _build_midn_network(self,
num_proposals,
proposal_features,
num_classes=20):
"""Builds the Multiple Instance Detection Network.
MIDN: An attention network.
Args:
num_proposals: A [batch] int tensor.
proposal_features: A [batch, max_num_proposals, features_dims]
float tensor.
num_classes: Number of classes.
Returns:
logits: A [batch, num_classes] float tensor.
proba_r_given_c: A [batch, max_num_proposals, num_classes] float tensor.
"""
with tf.name_scope('multi_instance_detection'):
batch, max_num_proposals, _ = utils.get_tensor_shape(proposal_features)
mask = tf.sequence_mask(
num_proposals, maxlen=max_num_proposals, dtype=tf.float32)
mask = tf.expand_dims(mask, axis=-1)
# Calculates the attention score: proposal `r` given class `c`.
# proba_r_given_c shape = [batch, max_num_proposals, num_classes].
logits_r_given_c = slim.fully_connected(
proposal_features,
num_outputs=num_classes,
activation_fn=None,
scope='midn/proba_r_given_c')
logits_r_given_c = tf.multiply(mask, logits_r_given_c)
proba_r_given_c = utils.masked_softmax(
data=logits_r_given_c, mask=mask, dim=1)
proba_r_given_c = tf.multiply(mask, proba_r_given_c)
tf.summary.histogram('midn/logits_r_given_c', logits_r_given_c)
# Calculates the weighted logits:
# logits_c_given_r shape = [batch, max_num_proposals, num_classes].
# logits shape = [batch, num_classes].
logits_c_given_r = slim.fully_connected(
proposal_features,
num_outputs=num_classes,
activation_fn=None,
scope='midn/proba_c_given_r')
proba_c_given_r = tf.nn.softmax(logits_c_given_r)
proba_c_given_r = tf.multiply(mask, proba_c_given_r)
tf.summary.histogram('midn/logits_c_given_r', logits_c_given_r)
# Aggregates the logits.
logits = tf.multiply(logits_c_given_r, proba_r_given_c)
logits = tf.reduce_sum(logits, axis=1)
tf.summary.histogram('midn/logits', logits)
return logits, proba_r_given_c
def _cumsum_gradient(score_map, box):
b, n, m, c = utils.get_tensor_shape(score_map)
_, p, _ = utils.get_tensor_shape(box)
# Leave a border for the image border.
ymin, xmin, ymax, xmax = tf.unstack(box, axis=-1)
ymin, xmin = tf.maximum(ymin, 2), tf.maximum(xmin, 2)
ymax, xmax = tf.minimum(ymax, tf.to_int64(n - 2)), tf.minimum(
xmax, tf.to_int64(m - 2))
box = tf.stack([ymin, xmin, ymax, xmax], axis=-1)
box_exp = _get_expanded_box(box,
img_h=n,
img_w=m,
border_ratio=border_ratio)
box_list = [box, box_exp]
area_list = [
tf.cast(_get_box_area(b), tf.float32) for b in box_list
]
cumsum = imgproc.calc_cumsum_2d(score_map,
tf.concat(box_list, axis=1))
cumsum_list = [
cumsum[:, i * p:(i + 1) * p, :] for i in range(len(box_list))
]
# The main box has to be valid, including the four shrinked boxes.
assert_op = tf.Assert(
tf.reduce_all(tf.greater(area_list[0], 0)),
["Check area of the main box failed:", area_list[0]])
with tf.control_dependencies([assert_op]):
border_area = area_list[1] - area_list[0]
border_cumsum = cumsum_list[1] - cumsum_list[0]
border_avg = tf.div(
border_cumsum,
tf.maximum(_SMALL_NUMBER,
tf.expand_dims(border_area, axis=-1)))
box_avg = tf.div(
cumsum_list[0],
tf.maximum(_SMALL_NUMBER,
tf.expand_dims(area_list[0], axis=-1)))
return purity_weight * box_avg - border_avg
def _predict_image_saliency(self, examples):
"""Builds tf graph for prediction.
Args:
examples: dict of input tensors keyed by name.
Returns:
predictions: dict of prediction results keyed by name.
"""
options = self._model_proto
is_training = self._is_training
if not options.use_saliency_score:
raise ValueError("The flag of `use_saliency_score` should be set.")
image = examples[InputDataFields.image]
# Extract image feature, shape =
# [batch, feature_height * feature_width, common_dimensions].
image_feature = self._encode_images(
image,
cnn_name=options.cnn_name,
cnn_trainable=options.cnn_trainable,
cnn_weight_decay=options.cnn_weight_decay,
cnn_feature_map=options.cnn_feature_map,
cnn_dropout_keep_prob=options.cnn_dropout_keep_prob,
cnn_checkpoint=options.cnn_checkpoint,
cnn_scope=GAPVariableScopes.cnn,
is_training=is_training)
image_feature = self._project_images(
image_feature,
common_dimensions=options.common_dimensions,
scope=GAPVariableScopes.image_proj,
hyperparams=options.image_proj_hyperparams,
is_training=is_training)
(batch, feature_height, feature_width,
common_dimensions) = utils.get_tensor_shape(image_feature)
image_feature = tf.reshape(image_feature,
[batch, -1, common_dimensions])
# Predict saliency score.
# image_saliency shape = [batch, num_regions].
# caption_saliency shape = [num_captions_in_batch, max_caption_length].
image_saliency = self._calc_saliency_score(
image_feature,
scope=GAPVariableScopes.image_saliency,
hyperparams=options.image_saliency_hyperparams,
is_training=is_training)
return {
GAPPredictions.image_saliency:
tf.reshape(image_saliency, [-1, feature_height, feature_width]),
}
def encode(self, feature, length, scope=None):
"""Encodes sequence features into representation.
Args:
feature: A [batch, max_sequence_len, dims] float tensor.
length: A [batch] int tensor.
Returns:
A [batch, dims] float tensor.
"""
options = self._model_proto
is_training = self._is_training
def lstm_cell():
cell = tf.nn.rnn_cell.BasicLSTMCell(
num_units=options.hidden_units, forget_bias=1.0)
if is_training:
cell = tf.nn.rnn_cell.DropoutWrapper(
cell,
input_keep_prob=options.input_keep_prob,
output_keep_prob=options.output_keep_prob,
state_keep_prob=options.state_keep_prob)
return cell
rnn_cell = tf.contrib.rnn.MultiRNNCell(
[lstm_cell() for _ in range(options.number_of_layers)])
with tf.variable_scope(scope):
outputs, state = tf.nn.bidirectional_dynamic_rnn(
cell_fw=rnn_cell,
cell_bw=rnn_cell,
inputs=feature,
sequence_length=length,
parallel_iterations=options.parallel_iterations,
dtype=tf.float32)
mask = tf.sequence_mask(
length, maxlen=utils.get_tensor_shape(feature)[1], dtype=tf.float32)
# outputs = tf.multiply(0.5, outputs[0] + outputs[1])
# feature = utils.masked_avg_nd(data=outputs, mask=mask, dim=1)
# return tf.squeeze(feature, axis=1)
state_list = []
for state_per_direction in state:
for state_per_layer in state_per_direction:
state_list.extend([state_per_layer.c, state_per_layer.h])
state_final = tf.contrib.layers.fully_connected(
inputs=tf.concat(state_list, axis=-1),
num_outputs=options.output_units,
activation_fn=None,
scope='bilstm_output')
return state_final
def _calc_spp_feature(self, inputs, spp_bins=[1, 2, 3, 6], max_pool=True):
"""Apply SPP layer to get the multi-resolutional feature.
LIMITATION: the inputs has to have static shape.
Args:
inputs: A [batch, feature_height, feature_width, feature_dims]
float tensor.
spp_bins: A python list representing the number of bins at each SPP
level.
Returns:
spp_pool: A [batch, spp_feature_dims] fixed-length feature tensor.
Raises:
ValueError: If any of the parameters are invalid.
"""
batch, height, width, _ = utils.get_tensor_shape(inputs)
if not type(height) == type(width) == int:
raise ValueError('The inputs should have static shape.')
pool_fn = tf.nn.avg_pool
if max_pool:
pool_fn = tf.nn.max_pool
with tf.name_scope('calc_spp_feature'):
pool_outputs = []
for bins in spp_bins:
if height % bins or width % bins:
raise ValueError('Reminder should be ZERO.')
pool_h, pool_w = height // bins, width // bins
stride_h, stride_w = height // bins, width // bins
pool = pool_fn(inputs,
ksize=[1, pool_h, pool_w, 1],
strides=[1, stride_h, stride_w, 1],
padding='SAME')
tf.summary.histogram('oicr/spp_bins_{}'.format(bins), pool)
tf.summary.scalar('oicr/spp_bins_min_{}'.format(bins),
tf.reduce_min(pool))
tf.summary.scalar('oicr/spp_bins_max_{}'.format(bins),
tf.reduce_max(pool))
tf.summary.scalar('oicr/spp_bins_avg_{}'.format(bins),
tf.reduce_mean(pool))
pool_outputs.append(tf.reshape(pool, [batch, -1]))
tf.logging.info(
'SPP bins=%i, bin_size=(%i,%i), strides=(%i, %i), output=%s',
bins, pool_h, pool_w, stride_h, stride_w,
pool.get_shape().as_list())
spp_pool = tf.concat(pool_outputs, axis=-1)
tf.logging.info('Final SPP shape=%s',
spp_pool.get_shape().as_list())
return spp_pool
def gather_in_batch_captions(image_id, num_captions, caption_strings,
caption_lengths):
"""Gathers all of the in-batch captions into a caption batch.
Args:
image_id: image_id, a [batch] int64 tensor.
num_captions: number of captions of each example, a [batch] int tensor.
caption_strings: caption data, a [batch, max_num_captions,
max_caption_length] string tensor.
caption_lengths: length of each caption, a [batch, max_num_captions] int
tensor.
Returns:
image_ids_gathered: associated image_id of each caption in the new batch, a
[num_captions_in_batch] string tensor.
caption_strings_gathered: caption data, a [num_captions_in_batch,
max_caption_length] string tensor.
caption_lengths_gathered: length of each caption, a [num_captions_in_batch]
int tensor.
"""
if not image_id.dtype in [tf.int32, tf.int64]:
raise ValueError('The image_id has to be int32 or int64')
(batch, max_num_captions,
max_caption_length) = utils.get_tensor_shape(caption_strings)
# caption_mask denotes the validity of each caption in the flattened batch.
# caption_mask shape = [batch * max_num_captions],
caption_mask = tf.sequence_mask(num_captions,
maxlen=max_num_captions,
dtype=tf.bool)
caption_mask = tf.reshape(caption_mask, [-1])
# image_id shape = [batch, max_num_captions].
image_id = tf.tile(tf.expand_dims(image_id, axis=1), [1, max_num_captions])
# Reshape the tensors to make their first dimensions to be [batch * max_num_captions].
image_id_reshaped = tf.reshape(image_id, [-1])
caption_strings_reshaped = tf.reshape(caption_strings,
[-1, max_caption_length])
caption_lengths_reshaped = tf.reshape(caption_lengths, [-1])
# Apply the caption_mask.
image_ids_gathered = tf.boolean_mask(image_id_reshaped, caption_mask)
caption_strings_gathered = tf.boolean_mask(caption_strings_reshaped,
caption_mask)
caption_lengths_gathered = tf.boolean_mask(caption_lengths_reshaped,
caption_mask)
return image_ids_gathered, caption_strings_gathered, caption_lengths_gathered
def draw_rectangles(image,
boxes,
scores=None,
labels=None,
color=GREEN,
thickness=1,
fontscale=1.0):
"""Draws rectangle to the image.
Args:
image: a [batch, height, width, 3] uint8 tensor.
boxes: a [batch, num_boxes, 4] float tensor representing normalized boxes,
i.e.: [ymin, xmin, ymax, xmax], values are ranging from 0.0 to 1.0.
scores: a [batch, num_boxes] float tensor representing the scores to be
drawn on the image.
labels: a [batch, num_boxes] string or float tensor representing the labels
to be drawn on the image.
color: color to be used.
thickness: the line thickness.
fontscale: size of the font.
Returns:
canvas: a [batch, height, width, 3] uint8 tensor with information drawn.
"""
def _draw_fn(inputs):
"""Draws the box on the image.
Args:
image: a [height, width, 3] float tensor.
box: a [num_boxes, 4] float tensor representing [ymin, xmin, ymax, xmax].
score: a [num_boxes] float tensor representing box scores.
label: a [num_boxes] string tensor denoting the text to be drawn.
Returns:
canvas: a [height, width, 3] float tensor with box drawn.
"""
image, boxes, scores, labels = inputs
canvas = tf.py_func(
func=lambda x, y, z, w: _py_draw_rectangles(x, y, z, w, color=color, thickness=thickness, fontscale=fontscale),
inp=[image, boxes, scores, labels], Tout=tf.uint8)
canvas.set_shape(tf.TensorShape([None, None, 3]))
return canvas
batch, num_boxes, _ = utils.get_tensor_shape(boxes)
if scores is None:
scores = tf.constant(-9999.0, shape=[batch, num_boxes], dtype=tf.float32)
if labels is None:
labels = tf.constant("", shape=[batch, num_boxes], dtype=tf.string)
return tf.map_fn(
_draw_fn, elems=[image, boxes, scores, labels], dtype=tf.uint8)
def encode(self, feature, length, scope=None):
"""Encodes sequence features into representation.
Args:
feature: A [batch, max_sequence_len, dims] float tensor.
length: A [batch] int tensor.
Returns:
A [batch, dims] float tensor.
"""
with tf.name_scope('avg_pooling_encoder'):
mask = tf.sequence_mask(
length, maxlen=utils.get_tensor_shape(feature)[-2], dtype=tf.float32)
feature = utils.masked_avg_nd(data=feature, mask=mask, dim=1)
return tf.squeeze(feature, axis=1)
def _calc_vgg_proposal_feature(self, image_feature_cropped):
"""Calculates proposal feature using vgg fc layers.
Args:
image_feature_cropped: A [batch, crop_size, crop_size, feature_dims]
float tensor.
Returns:
proposal_feature: A [batch, proposal_feature_dims] float tensor.
"""
options = self._model_proto
is_training = self._is_training
# SPP.
bins = 7
batch, height, width, _ = utils.get_tensor_shape(image_feature_cropped)
if height % bins or width % bins:
raise ValueError('Reminder should be ZERO.')
pool_h, pool_w = height // bins, width // bins
stride_h, stride_w = height // bins, width // bins
net = tf.nn.max_pool(image_feature_cropped,
ksize=[1, pool_h, pool_w, 1],
strides=[1, stride_h, stride_w, 1],
padding='SAME')
with tf.variable_scope(options.cnn.scope, reuse=True):
with tf.variable_scope(options.cnn.name, reuse=True):
net = slim.conv2d(net,
4096, [7, 7],
padding='VALID',
scope='fc6')
net = slim.dropout(net,
options.cnn.dropout_keep_prob,
is_training=is_training
and options.cnn.trainable,
scope='dropout6')
net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
net = slim.dropout(net,
options.hidden_dropout_keep_prob,
is_training=is_training,
scope='dropout7')
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
return net
def _encode_labels(self,
num_captions,
caption_strings,
caption_lengths,
vocabulary_list,
is_training=False):
"""Encodes labels.
Args:
num_captions: a [batch] int tensor.
caption_strings: a [batch, num_captions, max_caption_len] string tensor.
caption_lengths: a [batch, num_captions] int tensor.
vocabulary_list: a list of words of length ``num_classes''''.
is_training: if True, training graph is built.
Returns:
classes: a [batch, num_classes] int tensor.
"""
with tf.name_scope('encode_labels'):
batch, num_captions, max_caption_len = utils.get_tensor_shape(
caption_strings)
caption_string = caption_strings[:, 0, :]
caption_length = caption_lengths[:, 0]
categorical_col = tf.feature_column.categorical_column_with_vocabulary_list(
key='name_to_class_id',
vocabulary_list=vocabulary_list,
num_oov_buckets=1)
indicator_col = tf.feature_column.indicator_column(categorical_col)
indicator = tf.feature_column.input_layer(
{
'name_to_class_id': caption_strings
},
feature_columns=[indicator_col])
classes = tf.cast(indicator[:, :-1] > 0, tf.int64)
tf.summary.histogram('num_gt_boxes_per_image', caption_length)
tf.summary.histogram('num_gt_labels_per_image',
tf.reduce_sum(classes, axis=-1))
classes.set_shape([batch, len(vocabulary_list)])
return classes
def _average_encoding(sequence_feature, sequence_length):
"""Encodes sequence using Average pooling.
Args:
sequence_feature: a [batch_sequence, max_sequence_length, feature_dimensions].
float tensor.
sequence_length: a [batch_sequence] int tensor.
Returns:
sequence_emb: A [batch_sequence, common_dimensions] float tensor,
representing the embedding vectors.
"""
(_, max_sequence_length, _) = utils.get_tensor_shape(sequence_feature)
mask = tf.sequence_mask(
sequence_length, maxlen=max_sequence_length, dtype=tf.float32)
sequence_emb = utils.masked_avg_nd(sequence_feature, mask, dim=1)
sequence_emb = tf.squeeze(sequence_emb, axis=1)
return sequence_emb
def _midn_loss_mine_hardest_negative(self, labels, losses):
"""Hardest negative mining of the MIDN loss.
Args:
labels: A [batch, num_classes] float tensor, where `1` denotes the
presence of a class.
losses: A [batch, num_classes] float tensor, the losses predicted by
the model.
Returns:
mask: A [batch, num_classes] float tensor where `1` denotes the
selected entry.
"""
batch, num_classes = utils.get_tensor_shape(labels)
indices_0 = tf.range(batch, dtype=tf.int64)
indices_1 = utils.masked_argmax(data=losses, mask=1.0 - labels, dim=1)
indices = tf.stack([indices_0, indices_1], axis=-1)
negative_masks = tf.sparse_to_dense(indices, [batch, num_classes],
sparse_values=1.0)
return tf.add(labels, negative_masks)
def _calc_graph_node_scores(node,
hidden_layers=None,
hidden_units=None,
dropout_keep_prob=1.0,
is_training=False,
scope='calc_graph_node_scores'):
"""Calculates the node scores [from node to node].
Args:
node: A [batch, max_num_node, dims] float tensor.
hidden_layers: An integer denoting number of MLP layers.
hidden_units: An integer denoting MLP hidden units.
dropout_keep_prob: Keep probability of the dropout layers.
is_training: If true, build the training graph.
scope: Variable scope name.
Returns:
A [batch, max_num_node] float tensor.
"""
with tf.variable_scope(scope):
batch = utils.get_tensor_shape(node)[0]
# Concatenate the node features, inputs = [node].
hiddens = node
for layer_i in range(hidden_layers):
hiddens = tf.contrib.layers.fully_connected(
inputs=hiddens,
num_outputs=hidden_units,
activation_fn=tf.nn.relu,
scope='hidden_{}'.format(layer_i))
hiddens = slim.dropout(hiddens,
dropout_keep_prob,
is_training=is_training)
outputs = tf.contrib.layers.fully_connected(inputs=hiddens,
num_outputs=1,
activation_fn=None,
scope='output')
outputs = tf.squeeze(outputs, axis=-1)
return outputs
def visualize(self,
image,
saliency,
interpolation=tf.image.ResizeMethod.NEAREST_NEIGHBOR):
"""Visualizes images to tensorboard.
Args:
image: a [batch, height, width, channels] float tensor, in [0, 255].
saliency: a [batch, feature_height, feature_width] float tensor.
"""
(batch, height, width, channels) = utils.get_tensor_shape(image)
image = image / 255.0
heatmap = plotlib.convert_to_heatmap(saliency, normalize=True)
heatmap = tf.image.resize_images(heatmap, [height, width],
interpolation)
heatmap = plotlib.gaussian_filter(heatmap, ksize=32)
image = tf.maximum(0.0, tf.concat([image, heatmap], axis=2))
tf.summary.image("images", image, max_outputs=10)