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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains definitions of box sampler."""
# Import libraries
import tensorflow as tf, tf_keras
from official.vision.ops import sampling_ops
@tf_keras.utils.register_keras_serializable(package='Vision')
class BoxSampler(tf_keras.layers.Layer):
"""Creates a BoxSampler to sample positive and negative boxes."""
def __init__(self,
num_samples: int = 512,
foreground_fraction: float = 0.25,
**kwargs):
"""Initializes a box sampler.
Args:
num_samples: An `int` of the number of sampled boxes per image.
foreground_fraction: A `float` in [0, 1], what percentage of boxes should
be sampled from the positive examples.
**kwargs: Additional keyword arguments passed to Layer.
"""
self._config_dict = {
'num_samples': num_samples,
'foreground_fraction': foreground_fraction,
}
super(BoxSampler, self).__init__(**kwargs)
def call(self, positive_matches: tf.Tensor, negative_matches: tf.Tensor,
ignored_matches: tf.Tensor):
"""Samples and selects positive and negative instances.
Args:
positive_matches: A `bool` tensor of shape of [batch, N] where N is the
number of instances. For each element, `True` means the instance
corresponds to a positive example.
negative_matches: A `bool` tensor of shape of [batch, N] where N is the
number of instances. For each element, `True` means the instance
corresponds to a negative example.
ignored_matches: A `bool` tensor of shape of [batch, N] where N is the
number of instances. For each element, `True` means the instance should
be ignored.
Returns:
A `tf.tensor` of shape of [batch_size, K], storing the indices of the
sampled examples, where K is `num_samples`.
"""
sample_candidates = tf.logical_and(
tf.logical_or(positive_matches, negative_matches),
tf.logical_not(ignored_matches))
sampler = sampling_ops.BalancedPositiveNegativeSampler(
positive_fraction=self._config_dict['foreground_fraction'],
is_static=True)
batch_size = sample_candidates.shape[0]
sampled_indicators = []
for i in range(batch_size):
sampled_indicator = sampler.subsample(
sample_candidates[i],
self._config_dict['num_samples'],
positive_matches[i])
sampled_indicators.append(sampled_indicator)
sampled_indicators = tf.stack(sampled_indicators)
_, selected_indices = tf.nn.top_k(
tf.cast(sampled_indicators, dtype=tf.int32),
k=self._config_dict['num_samples'],
sorted=True)
return selected_indices
def get_config(self):
return self._config_dict
@classmethod
def from_config(cls, config):
return cls(**config)
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