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@ -210,53 +210,68 @@ def bipartite_match(dist_matrix,
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dist_threshold=None,
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name=None):
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"""
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**Bipartite matchint operator**
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This operator is a greedy bipartite matching algorithm, which is used to
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obtain the matching with the maximum distance based on the input
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This operator implements a greedy bipartite matching algorithm, which is
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used to obtain the matching with the maximum distance based on the input
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distance matrix. For input 2D matrix, the bipartite matching algorithm can
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find the matched column for each row, also can find the matched row for
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each column. And this operator only calculate matched indices from column
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to row. For each instance, the number of matched indices is the number of
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of columns of the input ditance matrix.
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There are two outputs to save matched indices and distance.
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A simple description, this algothrim matched the best (maximum distance)
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find the matched column for each row (matched means the largest distance),
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also can find the matched row for each column. And this operator only
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calculate matched indices from column to row. For each instance,
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the number of matched indices is the column number of the input distance
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matrix.
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There are two outputs, matched indices and distance.
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A simple description, this algorithm matched the best (maximum distance)
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row entity to the column entity and the matched indices are not duplicated
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in each row of ColToRowMatchIndices. If the column entity is not matched
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any row entity, set -1 in ColToRowMatchIndices.
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Please note that the input DistMat can be LoDTensor (with LoD) or Tensor.
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NOTE: the input DistMat can be LoDTensor (with LoD) or Tensor.
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If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
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If Tensor, the height of ColToRowMatchIndices is 1.
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NOTE: This API is a very low level API. It is used by :code:`ssd_loss`
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layer. Please consider to use :code:`ssd_loss` instead.
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Args:
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dist_matrix(Variable): This input is a 2-D LoDTensor with shape
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[K, M]. It is pair-wise distance matrix between the entities
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represented by each row and each column. For example, assumed one
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entity is A with shape [K], another entity is B with shape [M]. The
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dist_matirx[i][j] is the distance between A[i] and B[j]. The bigger
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the distance is, the better macthing the pairs are. Please note,
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This tensor can contain LoD information to represent a batch of
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inputs. One instance of this batch can contain different numbers of
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entities.
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dist_matrix[i][j] is the distance between A[i] and B[j]. The bigger
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the distance is, the better matching the pairs are.
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NOTE: This tensor can contain LoD information to represent a batch
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of inputs. One instance of this batch can contain different numbers
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of entities.
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match_type(string|None): The type of matching method, should be
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'bipartite' or 'per_prediction', 'bipartite' by defalut.
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'bipartite' or 'per_prediction'. [default 'bipartite'].
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dist_threshold(float|None): If `match_type` is 'per_prediction',
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this threshold is to determine the extra matching bboxes based
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on the maximum distance, 0.5 by defalut.
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on the maximum distance, 0.5 by default.
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Returns:
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match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
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tuple: a tuple with two elements is returned. The first is
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matched_indices, the second is matched_distance.
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The matched_indices is a 2-D Tensor with shape [N, M] in int type.
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N is the batch size. If match_indices[i][j] is -1, it
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means B[j] does not match any entity in i-th instance.
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Otherwise, it means B[j] is matched to row
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match_indices[i][j] in i-th instance. The row number of
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i-th instance is saved in match_indices[i][j].
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match_distance(Variable): A 2-D Tensor with shape [N, M] in float type.
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N is batch size. If match_indices[i][j] is -1,
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The matched_distance is a 2-D Tensor with shape [N, M] in float type
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. N is batch size. If match_indices[i][j] is -1,
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match_distance[i][j] is also -1.0. Otherwise, assumed
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match_distance[i][j] = d, and the row offsets of each instance
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are called LoD. Then match_distance[i][j] = dist_matrix[d+LoD[i]][j].
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are called LoD. Then match_distance[i][j] =
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dist_matrix[d+LoD[i]][j].
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Examples:
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>>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
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>>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
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>>> iou = fluid.layers.iou_similarity(x=x, y=y)
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>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
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"""
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helper = LayerHelper('bipartite_match', **locals())
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match_indices = helper.create_tmp_variable(dtype='int32')
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@ -364,7 +379,7 @@ def ssd_loss(location,
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normalize=True,
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sample_size=None):
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"""
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**Multi-box loss layer for object dection algorithm of SSD**
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**Multi-box loss layer for object detection algorithm of SSD**
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This layer is to compute dection loss for SSD given the location offset
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predictions, confidence predictions, prior boxes and ground-truth boudding
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@ -372,21 +387,35 @@ def ssd_loss(location,
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is a weighted sum of the localization loss (or regression loss) and
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confidence loss (or classification loss) by performing the following steps:
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1. Find matched boundding box by bipartite matching algorithm.
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1. Find matched bounding box by bipartite matching algorithm.
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1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
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1.2 Compute matched boundding box by bipartite matching algorithm.
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2. Compute confidence for mining hard examples
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2.1. Get the target label based on matched indices.
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2.2. Compute confidence loss.
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3. Apply hard example mining to get the negative example indices and update
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the matched indices.
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4. Assign classification and regression targets
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4.1. Encoded bbox according to the prior boxes.
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4.2. Assign regression targets.
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4.3. Assign classification targets.
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5. Compute the overall objective loss.
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5.1 Compute confidence loss.
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5.1 Compute localization loss.
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5.3 Compute the overall weighted loss.
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Args:
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@ -421,39 +450,36 @@ def ssd_loss(location,
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mining_type (str): The hard example mining type, should be 'hard_example'
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or 'max_negative', now only support `max_negative`.
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normalize (bool): Whether to normalize the SSD loss by the total number
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of output locations, True by defalut.
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of output locations, True by default.
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sample_size (int): The max sample size of negative box, used only when
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mining_type is 'hard_example'.
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Returns:
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Variable: The weighted sum of the localization loss and confidence loss,
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with shape [N * Np, 1], N and Np are the same as they are
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in `location`.
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The weighted sum of the localization loss and confidence loss, with \
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shape [N * Np, 1], N and Np are the same as they are in `location`.
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Raises:
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ValueError: If mining_type is 'hard_example', now only support
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mining type of `max_negative`.
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ValueError: If mining_type is 'hard_example', now only support mining \
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type of `max_negative`.
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Examples:
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.. code-block:: python
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pb = layers.data(
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name='prior_box',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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pbv = layers.data(
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name='prior_box_var',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
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scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
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gt_box = layers.data(
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name='gt_box', shape=[4], lod_level=1, dtype='float32')
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gt_label = layers.data(
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name='gt_label', shape=[1], lod_level=1, dtype='float32')
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loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
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>>> pb = fluid.layers.data(
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>>> name='prior_box',
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>>> shape=[10, 4],
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>>> append_batch_size=False,
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>>> dtype='float32')
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>>> pbv = fluid.layers.data(
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>>> name='prior_box_var',
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>>> shape=[10, 4],
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>>> append_batch_size=False,
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>>> dtype='float32')
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>>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
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>>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
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>>> gt_box = fluid.layers.data(
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>>> name='gt_box', shape=[4], lod_level=1, dtype='float32')
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>>> gt_label = fluid.layers.data(
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>>> name='gt_label', shape=[1], lod_level=1, dtype='float32')
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>>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
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"""
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helper = LayerHelper('ssd_loss', **locals())
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Yibing Liu
7 years ago