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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. | |
| """Definition of target gather, which gathers targets from indices.""" | |
| import tensorflow as tf, tf_keras | |
| class TargetGather: | |
| """Targer gather for dense object detector.""" | |
| def __call__(self, labels, match_indices, mask=None, mask_val=0.0): | |
| """Labels anchors with ground truth inputs. | |
| B: batch_size | |
| N: number of groundtruth boxes. | |
| Args: | |
| labels: An integer tensor with shape [N, dims] or [B, N, ...] representing | |
| groundtruth labels. | |
| match_indices: An integer tensor with shape [M] or [B, M] representing | |
| match label index. | |
| mask: An boolean tensor with shape [M, dims] or [B, M,...] representing | |
| match labels. | |
| mask_val: An integer to fill in for mask. | |
| Returns: | |
| target: An integer Tensor with shape [M] or [B, M] | |
| Raises: | |
| ValueError: If `labels` is higher than rank 3. | |
| """ | |
| if len(labels.shape) <= 2: | |
| return self._gather_unbatched(labels, match_indices, mask, mask_val) | |
| elif len(labels.shape) == 3: | |
| return self._gather_batched(labels, match_indices, mask, mask_val) | |
| else: | |
| raise ValueError("`TargetGather` does not support `labels` with rank " | |
| "larger than 3, got {}".format(len(labels.shape))) | |
| def _gather_unbatched(self, labels, match_indices, mask, mask_val): | |
| """Gather based on unbatched labels and boxes.""" | |
| num_gt_boxes = tf.shape(labels)[0] | |
| def _assign_when_rows_empty(): | |
| if len(labels.shape) > 1: | |
| mask_shape = [match_indices.shape[0], labels.shape[-1]] | |
| else: | |
| mask_shape = [match_indices.shape[0]] | |
| return tf.cast(mask_val, labels.dtype) * tf.ones( | |
| mask_shape, dtype=labels.dtype) | |
| def _assign_when_rows_not_empty(): | |
| targets = tf.gather(labels, match_indices) | |
| if mask is None: | |
| return targets | |
| else: | |
| masked_targets = tf.cast(mask_val, labels.dtype) * tf.ones_like( | |
| mask, dtype=labels.dtype) | |
| return tf.where(mask, masked_targets, targets) | |
| return tf.cond(tf.greater(num_gt_boxes, 0), | |
| _assign_when_rows_not_empty, | |
| _assign_when_rows_empty) | |
| def _gather_batched(self, labels, match_indices, mask, mask_val): | |
| """Gather based on batched labels.""" | |
| batch_size = labels.shape[0] | |
| if batch_size == 1: | |
| if mask is not None: | |
| result = self._gather_unbatched( | |
| tf.squeeze(labels, axis=0), tf.squeeze(match_indices, axis=0), | |
| tf.squeeze(mask, axis=0), mask_val) | |
| else: | |
| result = self._gather_unbatched( | |
| tf.squeeze(labels, axis=0), tf.squeeze(match_indices, axis=0), | |
| None, mask_val) | |
| return tf.expand_dims(result, axis=0) | |
| else: | |
| indices_shape = tf.shape(match_indices) | |
| indices_dtype = match_indices.dtype | |
| batch_indices = (tf.expand_dims( | |
| tf.range(indices_shape[0], dtype=indices_dtype), axis=-1) * | |
| tf.ones([1, indices_shape[-1]], dtype=indices_dtype)) | |
| gather_nd_indices = tf.stack( | |
| [batch_indices, match_indices], axis=-1) | |
| targets = tf.gather_nd(labels, gather_nd_indices) | |
| if mask is None: | |
| return targets | |
| else: | |
| masked_targets = tf.cast(mask_val, labels.dtype) * tf.ones_like( | |
| mask, dtype=labels.dtype) | |
| return tf.where(mask, masked_targets, targets) | |