def _loss(self, scores, labels):
scores = tf.squeeze(scores, axis=-1)
diff_scores = scores[..., tf.newaxis] - scores[:, tf.newaxis]
diff_labes = labels[..., tf.newaxis] - labels[:, tf.newaxis]
diff_labes = (1.0 + diff_labes / (self._k / 2.0)**2) / 2.0
add_extra_tensor('diff_labes', diff_labes)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=diff_labes,
logits=diff_scores)
return tf.reduce_mean(loss, axis=-1)
def _normalize_labels(self, class_hist, class_hist0, class_hist1,
neg_class_hist):
rank = _pairwise_scores(class_hist0,
class_hist1,
neg_class_hist,
type='sum')
is_fg = tf.equal(rank, int(self._k / 2)**2)
add_extra_tensor('is_fg', is_fg)
add_extra_tensor('rank', rank)
return rank, is_fg
def _normalize_labels(self, class_hist, class_hist0, class_hist1,
neg_class_hist):
# Match happens when at least min_match_frac*k objects
# belongs to the same foreground class.
labels, fg, bg = _onehot_labels(class_hist, self._k, neg_class_hist)
add_extra_tensor('class_hist_{}'.format(self._k), class_hist)
if self._class_agnostic:
return (tf.to_float(fg), tf.squeeze(fg, axis=-1))
else:
return (tf.to_float(labels), tf.squeeze(fg, axis=-1))
def _loss(self, scores, labels):
add_extra_tensor('pw_scores_k{}'.format(self._k), scores)
add_extra_tensor('pw_labels_k{}'.format(self._k), labels)
if self._loss_type == 'cross_entropy':
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=scores)
elif self._loss_type == 'l2':
diff = scores - labels
loss = 0.5 * tf.multiply(diff, diff)
return tf.reduce_sum(loss, axis=-1)
def _clear_negative_classes(class_hist, neg_class_hist):
## "not", "tile", and then "and" with class_hist
if neg_class_hist is not None:
# [MBS, num_classes + 1]
neg_shape = shape_utils.combined_static_and_dynamic_shape(
neg_class_hist)
# [MBS*N, B, num_classes + 1]
class_hist_shape = shape_utils.combined_static_and_dynamic_shape(
class_hist)
N = class_hist_shape[0] // neg_shape[0]
B = class_hist_shape[1]
add_extra_tensor('neg_class_hist_N{}'.format(N), neg_class_hist)
add_extra_tensor('class_hist_N{}'.format(N), class_hist)
# [MBS, 1, 1, num_classes + 1]
neg_class_hist = neg_class_hist[:, tf.newaxis, tf.newaxis]
# [MBS, N, B, num_classes + 1]
neg_class_hist = tf.tile(neg_class_hist, [1, N, B, 1])
neg_class_hist = tf.reshape(neg_class_hist, class_hist_shape)
not_neg_class_hist = tf.logical_not(neg_class_hist)
add_extra_tensor('not_neg_class_hist_tiled_N{}'.format(N),
not_neg_class_hist)
not_neg_class_hist = tf.cast(not_neg_class_hist,
dtype=class_hist.dtype)
class_hist = tf.multiply(class_hist, not_neg_class_hist)
add_extra_tensor('final_class_hist_N{}'.format(N), class_hist)
##
return class_hist
def _subsample_inds(self, cross_co, instance_loss):
cobj_energy = cross_co.get('energy')
if self._is_training:
if self._stratified_subsampling:
## RANDOM/HARD_EXAMPLE SAMPLING FROM STRATA
return self._subsample_stratified(
instance_loss, cross_co.get('matched_class'),
cross_co.get('target_class'))
else:
## RANDOM/HARD_EXAMPLE (optionally +BALANCED) SAMPLING FROM TOP-K
positive_fraction = self._positive_balance_fraction
return self._subsample_from_topk(cross_co.get('is_target'),
cobj_energy, instance_loss,
positive_fraction)
else:
#if cross_co.has_key('boxes'):
# nms_idx = nms_tf_wrapper.batch_nms(cross_co.get('boxes'),
# cobj_energy,
# self._ncobj_proposals,
# threshold=0.5, use_py_fn=True)
#util.add_extra_tensor('boxes_{}'.format(self._k), cross_co.get('boxes'))
#util.add_extra_tensor('energy_{}'.format(self._k), cobj_energy)
#util.add_extra_tensor('nms_idx_{}'.format(self._k), nms_idx)
#nms_idx2 = nms_tf_wrapper.batch_nms(cross_co.get('boxes'),
# cobj_energy,
# self._ncobj_proposals,
# threshold=0.8,
# use_py_fn=False)
#util.add_extra_tensor('nms_idx2_{}'.format(self._k), nms_idx2)
ncobj = cobj_energy.shape[1]
assert (ncobj is not None and ncobj >= self._ncobj_proposals)
if self._ncobj_proposals == ncobj:
return None
_, topk_idx = tf.nn.top_k(cobj_energy,
k=self._ncobj_proposals,
sorted=False)
#all_true = tf.ones_like(cobj_energy, dtype=tf.bool)
#topk_idx = util.batched_topk_or_pad_inds_with_resampling(
# all_true, cobj_energy,
# self._ncobj_proposals)
#from IPython import embed;embed()
util.add_extra_tensor('topk_idx_{}'.format(self._k), topk_idx)
return topk_idx
def build(self,
fea,
neg_fea,
matched_class0,
neg_matched_class,
reuse_vars=False,
scope=None):
'''
Args:
fea: feature tensor of size [MBS*K, 1, B, 1, d] of positive bags
neg_fea: feature tensor of size [MBS, N*B, 1, 1, d] of negative bags
matched_class0: [MBS*K, B, num_classes+1]
neg_matched_class: [MBS*N, B, num_classes+1]
'''
with tf.variable_scope(scope,
'negative_attention',
reuse=reuse_vars,
values=[fea, neg_fea]):
neg_fea_shape = shape_utils.combined_static_and_dynamic_shape(
neg_fea)
orig_neg_fea = neg_fea
#[MBS*K, 1, B, 1, d] -> [MBS*K, B, d]
orig_fea_shape = shape_utils.combined_static_and_dynamic_shape(fea)
orig_fea = fea
fea = tf.squeeze(fea, [1, 3])
fea_shape = shape_utils.combined_static_and_dynamic_shape(fea)
#[MBS*K, B, d] -> [MBS, K*B, d]
fea = tf.reshape(fea, [neg_fea_shape[0], -1] + fea_shape[2:])
add_extra_tensor('input_fea', fea)
add_extra_tensor('input_neg_fea', neg_fea)
#[MBS, K*B, 1, 1, d]
fea = fea[:, :, tf.newaxis, tf.newaxis, :]
if self._convline is not None:
#[MBS, (K+N)*B, 1, 1, d]
combined_fea = tf.concat([fea, neg_fea], axis=1)
combined_fea = self._convline.build(combined_fea,
scope='combined_convline')
#combined_fea = tf.squeeze(combined_fea, [2,3])
fea = combined_fea[:, :-neg_fea_shape[1], ...]
neg_fea = combined_fea[:, -neg_fea_shape[1]:, ...]
add_extra_tensor('conv_fea', fea)
add_extra_tensor('conv_neg_fea', neg_fea)
fea = tf.squeeze(fea, [2, 3])
neg_fea = tf.squeeze(neg_fea, [2, 3])
gt_alphas = None
loss = None
if neg_matched_class is not None and matched_class0 is not None:
# [MBS, N*B, num_class+1]
neg_matched_class = tf.reshape(neg_matched_class,
neg_fea_shape[:2] + [-1])
neg_cls_shape = shape_utils.combined_static_and_dynamic_shape(
neg_matched_class)
# [MBS, K*B, num_class+1] (k is 1 always...)
matched_class0 = tf.reshape(
matched_class0, [neg_cls_shape[0], -1, neg_cls_shape[2]])
# [MBS, K*B, N*B]
gt_alphas = tf.matmul(matched_class0[..., 1:],
neg_matched_class[..., 1:],
transpose_b=True)
#[MBS, N*B+1, d]
neg_fea = tf.pad(neg_fea, [[0, 0], [1, 0], [0, 0]])
if self._use_gt_labels and gt_alphas is not None:
#[MBS, K*B, N*B]
alphas = gt_alphas / (
tf.reduce_sum(gt_alphas, axis=-1, keep_dims=True) + 1e-7)
#[MBS, K*B, N*B+1] TODO: not sure about this
alphas = tf.pad(alphas, [[0, 0], [0, 0], [1, 0]])
else:
scores, pairs, joined_fea, _ = self._similarity_cal.build(
fea, neg_fea, None, None, 2, None, reuse_vars)
#[MBS, K*B, N*B+1] scores
scores = tf.reshape(
scores[...,
1], [neg_fea_shape[0], -1, neg_fea_shape[1] + 1])
add_extra_tensor('scores', scores)
if self._add_loss and gt_alphas is not None:
gt_alphas = tf.cast(gt_alphas, tf.bool)
is_in_neg = tf.reduce_any(gt_alphas,
axis=-1,
keep_dims=True)
not_in_neg = tf.logical_not(is_in_neg)
labels = tf.to_float(
tf.concat([not_in_neg, gt_alphas], axis=-1))
labels = labels / tf.reduce_sum(
labels, axis=-1, keep_dims=True)
logits = scores
loss = tf.nn.softmax_cross_entropy_with_logits(
labels=labels, logits=logits)
loss = tf.reduce_mean(loss)
#[MBS, K*B, N*B+1]
alphas = tf.nn.softmax(scores, dim=2)
add_extra_tensor('alphas', alphas)
neg_fea_to_attend, pos_fea = None, None
if self._concat_type == 'NEGATIVE_IN_ORIG':
# orig_neg_fea is [MBS, N*B, 1, 1, d] -> [MBS, N*B, d]
orig_neg_fea = tf.squeeze(orig_neg_fea, [2, 3])
# [MBS, N*B+1, d]
orig_neg_fea = tf.pad(orig_neg_fea, [[0, 0], [1, 0], [0, 0]])
neg_fea_to_attend = orig_neg_fea
pos_fea = orig_fea
elif self._concat_type == 'NEGATIVE_IN_FEA':
neg_fea_to_attend = neg_fea
pos_fea = orig_fea
elif self._concat_type == 'CONCAT_IN_FEA':
neg_fea_to_attend = neg_fea
pos_fea = tf.reshape(fea, orig_fea_shape[:4] + [-1])
else:
raise ValueError('concat type {} is not defined'.format(
self._concat_type))
# [MBS, K*B, N*B+1] * [MBS, N*B+1, d] -> [MBS, K*B, d]
attended_neg_feas = tf.matmul(alphas, neg_fea_to_attend)
add_extra_tensor('attended_neg_feas', attended_neg_feas)
attended_neg_feas = tf.reshape(attended_neg_feas,
orig_fea_shape[:4] + [-1])
fea01 = tf.concat((pos_fea, attended_neg_feas), axis=-1)
return fea01, loss
def _loss(self, scores, labels):
def _target_vals(tensor):
if self._bag_target_class is None:
return tensor
score_inds = tf.cast(self._bag_target_class[..., tf.newaxis],
tf.int32) - 1
tr_tensor = tf.transpose(tensor, [0, 2, 1])
tr_tensor = util.batched_gather(score_inds, tr_tensor)
return tf.transpose(tr_tensor, [0, 2, 1])
add_extra_tensor('scores_b{}'.format(self._k), scores)
add_extra_tensor('labels_b{}'.format(self._k), labels)
scores = _target_vals(scores)
labels = _target_vals(labels)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=scores)
add_extra_tensor('scores_{}'.format(self._k), scores)
add_extra_tensor('labels_{}'.format(self._k), labels)
add_extra_tensor('loss_{}'.format(self._k), loss)
if self._focal_loss:
gamma = 2.0
alpha = 1.0
sigm = tf.nn.sigmoid(scores)
weights = tf.where(tf.cast(labels, dtype=tf.bool), 1.0 - sigm,
sigm)
weights = alpha * tf.pow(weights, gamma)
loss = weights * loss
add_extra_tensor('weights_{}'.format(self._k), weights)
add_extra_tensor('sigm_{}'.format(self._k), sigm)
return tf.reduce_sum(loss, axis=-1)
def _loss(self, scores, labels):
add_extra_tensor('pw_scores_k{}'.format(self._k), scores)
add_extra_tensor('pw_labels_k{}'.format(self._k), labels)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=scores)
if True:
gamma = 2.0
alpha = 1.0
sigm = tf.nn.sigmoid(scores)
weights = tf.where(tf.cast(labels, dtype=tf.bool), 1.0 - sigm,
sigm)
weights = alpha * tf.pow(weights, gamma)
loss = weights * loss
add_extra_tensor('weights', weights)
add_extra_tensor('sigm', sigm)
add_extra_tensor('labels', labels)
add_extra_tensor('loss_k{}'.format(self._k), loss)
return tf.reduce_sum(loss, axis=-1)
def _normalize_labels(self, class_hist, class_hist0, class_hist1,
neg_class_hist):
add_extra_tensor('class_hist_k{}'.format(self._k), class_hist)
add_extra_tensor('class_hist0_k{}'.format(self._k), class_hist0)
add_extra_tensor('class_hist1_k{}'.format(self._k), class_hist1)
neg_class_hist = None
energy = _pairwise_scores(class_hist0,
class_hist1,
neg_class_hist,
type='max')
labels = []
for i in range(self.score_size):
labs = tf.greater(energy, i)
labels.append(labs)
labels = tf.cast(tf.stack(labels, axis=-1), dtype=tf.float32)
add_extra_tensor('labels_k{}'.format(self._k), labels)
add_extra_tensor('labs_k{}'.format(self._k), labs)
add_extra_tensor('energy_k{}'.format(self._k), energy)
return labels, labs
def _build(self,
fea0,
fea1,
ind0,
ind1,
score_size,
neg_fea,
matched_class0,
neg_matched_class,
reuse_vars,
scope,
tile_fea1=True):
fea0_shape = shape_utils.combined_static_and_dynamic_shape(fea0)
assert (len(fea0_shape) == 3)
m = fea0_shape[1]
if fea1 is None:
# shape: [N, M, 1, K]
fea01 = fea0[:, :, tf.newaxis]
else:
fea1_shape = shape_utils.combined_static_and_dynamic_shape(fea1)
if tile_fea1:
assert (len(fea1_shape) == 3)
l = fea1_shape[1]
# shape = [N, M, L, K]
fea1 = tf.tile(fea1[:, tf.newaxis], [1, m, 1, 1])
else:
assert (len(fea1_shape) == 4)
l = fea1_shape[2]
# shape = [N, M, L, K]
fea0 = tf.tile(fea0[:, :, tf.newaxis], [1, 1, l, 1])
# shape = [N, M, L, 2K]
fea01 = tf.concat((fea0, fea1), axis=-1)
### Rearange the order of the input features
### This is required to have a symmetric funciton
if False: #neg_fea is None:
fea01_shape = shape_utils.combined_static_and_dynamic_shape(
fea01)
rfea01 = tf.reshape(fea01, fea0_shape[:2] + [l, 2, -1])
absum = tf.reduce_sum(tf.abs(rfea01), axis=-1)
order = tf.argmax(absum, axis=-1)
rf = tf.reshape(rfea01, [-1, 2, fea0_shape[-1]])
oo = tf.reshape(order, [-1, 1])
xfea0 = util.batched_gather(oo, rf)
xfea1 = util.batched_gather(1 - oo, rf)
xfea01 = tf.concat((xfea0, xfea1), axis=1)
fea01 = tf.reshape(xfea01, fea01_shape)
# shape = [N, 1, M, L, 2K] or [N, 1, M, 1, K]
fea01 = fea01[:, tf.newaxis]
if self._joining_convline is not None:
fea01 = self._joining_convline.build(fea01, scope='join')
self._joined_fea = tf.squeeze(fea01, 1)
if self._stop_gradient:
fea01 = tf.stop_gradient(fea01)
#### ADD BACKGROUND FEATURE
negative_loss = None
if neg_fea is not None:
if self._negative_attention is None: #just averaged
#neg_fea is [MBS, 1, 1, 1, d]
#fea01 is [MBS*k_shot, 1, M, P, C] where P is 1 or L
fea01_shape = shape_utils.combined_static_and_dynamic_shape(
fea01)
neg_fea_shape = shape_utils.combined_static_and_dynamic_shape(
neg_fea)
mbs = neg_fea_shape[0]
k = fea01_shape[0] // mbs
util.add_extra_tensor('fea01_before', fea01)
# list of size MBS with [1,1,1,1,d] shape tensors
util.add_extra_tensor('neg_fea_before', neg_fea)
neg_fea_mbs = tf.split(neg_fea, mbs, axis=0)
# list of size MBS with [k, 1, M, P, d] shape tensors
util.add_extra_tensor('neg_fea_mbs', neg_fea_mbs)
neg_fea_mbs_tiled = [
tf.tile(n, [k, 1, fea01_shape[2], fea01_shape[3], 1])
for n in neg_fea_mbs
]
util.add_extra_tensor('neg_fea_mbs_tiled', neg_fea_mbs_tiled)
# neg_fea will be [MBS*k, 1, M, P, d] tensor
neg_fea = tf.concat(neg_fea_mbs_tiled, axis=0)
util.add_extra_tensor('neg_fea', neg_fea)
#fea01 becomes [MBS*k_shot, 1, M, P, C+d]
fea01 = tf.concat((fea01, neg_fea), axis=-1)
util.add_extra_tensor('fea01_after', fea01)
else:
#[MBS*k_shot, 1, M, 1, C]
#fea01 = tf.Print(fea01, tf.nn.moments(fea01, axes=[0,1,2,3,4]) +
# tf.nn.moments(neg_fea, axes=[0,1,2,3,4]))
fea01, negative_loss = self._negative_attention.build(
fea01, neg_fea, matched_class0, neg_matched_class,
reuse_vars)
####
# shape = [N, 1, M', M, 2K] or [N, 1, M, 1, K]
if self._convline is not None:
fea01 = self._convline.build(fea01, scope='convline')
with slim.arg_scope(self._fc_hyperparameters):
with slim.arg_scope([slim.fully_connected],
activation_fn=None,
normalizer_fn=None,
normalizer_params=None):
scores = slim.fully_connected(fea01, score_size, scope='score')
if self._sum_output:
with tf.variable_scope('score', reuse=True):
w = tf.get_variable('weights')
b = tf.get_variable('biases')
sw = tf.reduce_sum(w, axis=-1)
sb = tf.reduce_sum(b)
scores = (tf.tensordot(fea01, w[:, 0], [[4], [0]]) +
sb)[..., tf.newaxis]
scores = tf.squeeze(scores, 1)
if self._target_score_inds is not None and score_size > 1:
score_inds = self._target_score_inds[..., tf.newaxis]
scores_trans = tf.transpose(scores, [0, 3, 1, 2])
scores_trans = util.batched_gather(score_inds,
scores_trans)
scores = tf.transpose(scores_trans, [0, 2, 3, 1])
return scores, negative_loss
