def xdet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
num_anchors_list = labels['num_anchors_list']
num_feature_layers = len(num_anchors_list)
shape = labels['targets'][-1]
glabels = labels['targets'][:num_feature_layers][0]
gtargets = labels['targets'][num_feature_layers:2 * num_feature_layers][0]
#gtargets = tf.Print(gtargets, [gtargets], message='gtargets:', summarize=100)
gscores = labels['targets'][2 * num_feature_layers:3 *
num_feature_layers][0]
with tf.variable_scope(params['model_scope'],
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
backbone = xdet_body.xdet_resnet_v2(params['resnet_size'],
params['data_format'])
multi_merged_feature = backbone(
inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
cls_pred, location_pred = xdet_body.xdet_head(
multi_merged_feature,
params['num_classes'],
num_anchors_list[0], (mode == tf.estimator.ModeKeys.TRAIN),
data_format=params['data_format'])
if params['data_format'] == 'channels_first':
cls_pred = tf.transpose(cls_pred, [0, 2, 3, 1])
location_pred = tf.transpose(location_pred, [0, 2, 3, 1])
bboxes_pred = labels['decode_fn'](
location_pred
) #(tf.reshape(location_pred, tf.shape(location_pred).as_list()[0:-1] + [-1, 4]))
cls_pred = tf.reshape(cls_pred, [-1, params['num_classes']])
location_pred = tf.reshape(location_pred, [-1, 4])
glabels = tf.reshape(glabels, [-1])
gscores = tf.reshape(gscores, [-1])
gtargets = tf.reshape(gtargets, [-1, 4])
# raw mask for positive > 0.5, and for negetive < 0.3
# each positive examples has one label
positive_mask = glabels > 0 #tf.logical_and(glabels > 0, gscores > params['match_threshold'])
fpositive_mask = tf.cast(positive_mask, tf.float32)
n_positives = tf.reduce_sum(fpositive_mask)
batch_glabels = tf.reshape(glabels, [tf.shape(features)[0], -1])
batch_n_positives = tf.count_nonzero(batch_glabels, -1)
batch_negtive_mask = tf.equal(batch_glabels, 0)
batch_n_negtives = tf.count_nonzero(batch_negtive_mask, -1)
batch_n_neg_select = tf.cast(
params['negative_ratio'] * tf.cast(batch_n_positives, tf.float32),
tf.int32)
batch_n_neg_select = tf.minimum(batch_n_neg_select,
tf.cast(batch_n_negtives, tf.int32))
# hard negative mining for classification
predictions_for_bg = tf.nn.softmax(
tf.reshape(cls_pred,
[tf.shape(features)[0], -1, params['num_classes']]))[:, :,
0]
prob_for_negtives = tf.where(
batch_negtive_mask,
0. - predictions_for_bg,
# ignore all the positives
0. - tf.ones_like(predictions_for_bg))
topk_prob_for_bg, _ = tf.nn.top_k(prob_for_negtives,
k=tf.shape(prob_for_negtives)[1])
score_at_k = tf.gather_nd(
topk_prob_for_bg,
tf.stack([tf.range(tf.shape(features)[0]), batch_n_neg_select - 1],
axis=-1))
selected_neg_mask = prob_for_negtives >= tf.expand_dims(score_at_k,
axis=-1)
negtive_mask = tf.reshape(
tf.logical_and(batch_negtive_mask, selected_neg_mask),
[-1]) #tf.logical_and(tf.equal(glabels, 0), gscores > 0.)
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(positive_mask), gscores < params['neg_threshold']), gscores > 0.)
#negtive_mask = tf.logical_and(gscores < params['neg_threshold'], tf.logical_not(positive_mask))
# # random select negtive examples for classification
# selected_neg_mask = tf.random_uniform(tf.shape(gscores), minval=0, maxval=1.) < tf.where(
# tf.greater(n_negtives, 0),
# tf.divide(tf.cast(n_neg_to_select, tf.float32), n_negtives),
# tf.zeros_like(tf.cast(n_neg_to_select, tf.float32)),
# name='rand_select_negtive')
# include both selected negtive and all positive examples
final_mask = tf.stop_gradient(tf.logical_or(negtive_mask, positive_mask))
total_examples = tf.reduce_sum(tf.cast(final_mask, tf.float32))
# add mask for glabels and cls_pred here
glabels = tf.boolean_mask(tf.clip_by_value(glabels, 0, FLAGS.num_classes),
tf.stop_gradient(final_mask))
cls_pred = tf.boolean_mask(cls_pred, tf.stop_gradient(final_mask))
location_pred = tf.boolean_mask(location_pred,
tf.stop_gradient(positive_mask))
gtargets = tf.boolean_mask(gtargets, tf.stop_gradient(positive_mask))
predictions = {
'classes':
tf.argmax(cls_pred, axis=-1),
'probabilities':
tf.reduce_max(tf.nn.softmax(cls_pred, name='softmax_tensor'), axis=-1),
'bboxes_predict':
tf.reshape(bboxes_pred, [-1, 4])
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate loss, which includes softmax cross entropy and L2 regularization.
cross_entropy = tf.cond(
n_positives > 0., lambda: tf.losses.sparse_softmax_cross_entropy(
labels=glabels, logits=cls_pred), lambda: 0.)
#cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=glabels, logits=cls_pred)
# Create a tensor named cross_entropy for logging purposes.
tf.identity(cross_entropy, name='cross_entropy_loss')
tf.summary.scalar('cross_entropy_loss', cross_entropy)
loc_loss = tf.cond(
n_positives > 0., lambda: modified_smooth_l1(
location_pred, tf.stop_gradient(gtargets), sigma=1.),
lambda: tf.zeros_like(location_pred))
#loc_loss = modified_smooth_l1(location_pred, tf.stop_gradient(gtargets))
loc_loss = tf.reduce_mean(tf.reduce_sum(loc_loss, axis=-1))
loc_loss = tf.identity(loc_loss, name='location_loss')
tf.summary.scalar('location_loss', loc_loss)
tf.losses.add_loss(loc_loss)
# Add weight decay to the loss. We exclude the batch norm variables because
# doing so leads to a small improvement in accuracy.
loss = 1.2 * (cross_entropy +
loc_loss) + params['weight_decay'] * tf.add_n([
tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'batch_normalization' not in v.name
])
total_loss = tf.identity(loss, name='total_loss')
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
lr_values = [
params['learning_rate'] * decay
for decay in params['lr_decay_factors']
]
learning_rate = tf.train.piecewise_constant(
tf.cast(global_step, tf.int32),
[int(_) for _ in params['decay_boundaries']], lr_values)
truncated_learning_rate = tf.maximum(
learning_rate,
tf.constant(params['end_learning_rate'],
dtype=learning_rate.dtype))
# Create a tensor named learning_rate for logging purposes.
tf.identity(truncated_learning_rate, name='learning_rate')
tf.summary.scalar('learning_rate', truncated_learning_rate)
optimizer = tf.train.MomentumOptimizer(
learning_rate=truncated_learning_rate, momentum=params['momentum'])
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step)
else:
train_op = None
cls_accuracy = tf.metrics.accuracy(glabels, predictions['classes'])
metrics = {'cls_accuracy': cls_accuracy}
# Create a tensor named train_accuracy for logging purposes.
tf.identity(cls_accuracy[1], name='cls_accuracy')
tf.summary.scalar('cls_accuracy', cls_accuracy[1])
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics,
scaffold=tf.train.Scaffold(
init_fn=train_helper.get_init_fn_for_scaffold(FLAGS)))
def xdet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
num_anchors_list = labels['num_anchors_list']
num_feature_layers = len(num_anchors_list)
shape = labels['targets'][-1]
if mode != tf.estimator.ModeKeys.TRAIN:
org_image = labels['targets'][-2]
isdifficult = labels['targets'][-3]
bbox_img = labels['targets'][-4]
gbboxes_raw = labels['targets'][-5]
glabels_raw = labels['targets'][-6]
glabels = labels['targets'][:num_feature_layers][0]
gtargets = labels['targets'][num_feature_layers:2 * num_feature_layers][0]
gscores = labels['targets'][2 * num_feature_layers:3 *
num_feature_layers][0]
with tf.variable_scope(params['model_scope'],
default_name=None,
values=[features],
reuse=tf.AUTO_REUSE):
backbone = xdet_body.xdet_resnet_v2(params['resnet_size'],
params['data_format'])
multi_merged_feature = backbone(
inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
cls_pred, location_pred = xdet_body.xdet_head(
multi_merged_feature,
params['num_classes'],
num_anchors_list[0], (mode == tf.estimator.ModeKeys.TRAIN),
data_format=params['data_format'])
if params['data_format'] == 'channels_first':
cls_pred = tf.transpose(cls_pred, [0, 2, 3, 1])
location_pred = tf.transpose(location_pred, [0, 2, 3, 1])
#org_image = tf.transpose(org_image, [0, 2, 3, 1])
# batch size is 1
shape = tf.squeeze(shape, axis=0)
glabels = tf.squeeze(glabels, axis=0)
gtargets = tf.squeeze(gtargets, axis=0)
gscores = tf.squeeze(gscores, axis=0)
cls_pred = tf.squeeze(cls_pred, axis=0)
location_pred = tf.squeeze(location_pred, axis=0)
if mode != tf.estimator.ModeKeys.TRAIN:
org_image = tf.squeeze(org_image, axis=0)
isdifficult = tf.squeeze(isdifficult, axis=0)
gbboxes_raw = tf.squeeze(gbboxes_raw, axis=0)
glabels_raw = tf.squeeze(glabels_raw, axis=0)
bbox_img = tf.squeeze(bbox_img, axis=0)
bboxes_pred = labels['decode_fn'](
location_pred
) #(tf.reshape(location_pred, location_pred.get_shape().as_list()[:-1] + [-1, 4]))#(location_pred)#
eval_ops, save_image_op = bboxes_eval(org_image, shape, bbox_img, cls_pred,
bboxes_pred, glabels_raw,
gbboxes_raw, isdifficult,
params['num_classes'])
_ = tf.identity(save_image_op, name='save_image_with_bboxes_op')
cls_pred = tf.reshape(cls_pred, [-1, params['num_classes']])
location_pred = tf.reshape(location_pred, [-1, 4])
glabels = tf.reshape(glabels, [-1])
gscores = tf.reshape(gscores, [-1])
gtargets = tf.reshape(gtargets, [-1, 4])
# raw mask for positive > 0.5, and for negetive < 0.3
# each positive examples has one label
positive_mask = glabels > 0 #tf.logical_and(glabels > 0, gscores > 0.)
fpositive_mask = tf.cast(positive_mask, tf.float32)
n_positives = tf.reduce_sum(fpositive_mask)
# negtive examples are those max_overlap is still lower than neg_threshold, note that some positive may also has lower jaccard
# note those gscores is 0 is either be ignored during anchors encode or anchors have 0 overlap with all ground truth
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(tf.logical_or(positive_mask, glabels < 0)), gscores < params['neg_threshold']), gscores > 0.)
negtive_mask = tf.logical_and(tf.equal(glabels, 0), gscores > 0.)
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(positive_mask), gscores < params['neg_threshold']), gscores > 0.)
#negtive_mask = tf.logical_and(gscores < params['neg_threshold'], tf.logical_not(positive_mask))
fnegtive_mask = tf.cast(negtive_mask, tf.float32)
n_negtives = tf.reduce_sum(fnegtive_mask)
n_neg_to_select = tf.cast(params['negative_ratio'] * n_positives, tf.int32)
n_neg_to_select = tf.minimum(n_neg_to_select,
tf.cast(n_negtives, tf.int32))
# hard negative mining for classification
predictions_for_bg = tf.nn.softmax(cls_pred)[:, 0]
#negtive_mask = tf.Print(negtive_mask,[n_positives])
prob_for_negtives = tf.where(
negtive_mask,
0. - predictions_for_bg,
# ignore all the positives
0. - tf.ones_like(predictions_for_bg))
topk_prob_for_bg, _ = tf.nn.top_k(prob_for_negtives, k=n_neg_to_select)
selected_neg_mask = prob_for_negtives > topk_prob_for_bg[-1]
# # random select negtive examples for classification
# selected_neg_mask = tf.random_uniform(tf.shape(gscores), minval=0, maxval=1.) < tf.where(
# tf.greater(n_negtives, 0),
# tf.divide(tf.cast(n_neg_to_select, tf.float32), n_negtives),
# tf.zeros_like(tf.cast(n_neg_to_select, tf.float32)),
# name='rand_select_negtive')
# include both selected negtive and all positive examples
final_mask = tf.stop_gradient(
tf.logical_or(tf.logical_and(negtive_mask, selected_neg_mask),
positive_mask))
total_examples = tf.reduce_sum(tf.cast(final_mask, tf.float32))
# add mask for glabels and cls_pred here
glabels = tf.boolean_mask(tf.clip_by_value(glabels, 0, FLAGS.num_classes),
tf.stop_gradient(final_mask))
cls_pred = tf.boolean_mask(cls_pred, tf.stop_gradient(final_mask))
location_pred = tf.boolean_mask(location_pred,
tf.stop_gradient(positive_mask))
gtargets = tf.boolean_mask(gtargets, tf.stop_gradient(positive_mask))
# Calculate loss, which includes softmax cross entropy and L2 regularization.
cross_entropy = tf.cond(
n_positives > 0., lambda: tf.losses.sparse_softmax_cross_entropy(
labels=glabels, logits=cls_pred), lambda: 0.)
#cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=glabels, logits=cls_pred)
# Create a tensor named cross_entropy for logging purposes.
tf.identity(cross_entropy, name='cross_entropy_loss')
tf.summary.scalar('cross_entropy_loss', cross_entropy)
loc_loss = tf.cond(
n_positives > 0., lambda: modified_smooth_l1(
location_pred, tf.stop_gradient(gtargets), sigma=1.),
lambda: tf.zeros_like(location_pred))
#loc_loss = modified_smooth_l1(location_pred, tf.stop_gradient(gtargets))
loc_loss = tf.reduce_mean(tf.reduce_sum(loc_loss, axis=-1))
loc_loss = tf.identity(loc_loss, name='location_loss')
tf.summary.scalar('location_loss', loc_loss)
tf.losses.add_loss(loc_loss)
with tf.control_dependencies([save_image_op]):
# Add weight decay to the loss. We exclude the batch norm variables because
# doing so leads to a small improvement in accuracy.
loss = 1.2 * (cross_entropy +
loc_loss) + params['weight_decay'] * tf.add_n([
tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'batch_normalization' not in v.name
])
total_loss = tf.identity(loss, name='total_loss')
predictions = {
'classes':
tf.argmax(cls_pred, axis=-1),
'probabilities':
tf.reduce_max(tf.nn.softmax(cls_pred, name='softmax_tensor'), axis=-1),
'bboxes_predict':
tf.reshape(bboxes_pred, [-1, 4])
}
summary_hook = tf.train.SummarySaverHook(
save_secs=FLAGS.save_summary_steps,
output_dir=FLAGS.model_dir,
summary_op=tf.summary.merge_all())
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
evaluation_hooks=[summary_hook],
loss=loss,
eval_metric_ops=eval_ops) #=eval_ops)
else:
raise ValueError('This script only support predict mode!')