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未验证 提交 1976cc4b 编写于 作者: Z zqw_1997 提交者： GitHub
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[fluid remove]: remove paddle.fluid.layers.target_assign,... 

[fluid remove]: remove paddle.fluid.layers.target_assign, paddle.fluid.layers.rpn_target_assign, paddle.fluid.layers.retinanet_target_assign and paddle.fluid.layers.ssd_loss (#48669)

* remove paddle.fluid.layers.nn.temporal_shift

* code check

* rm unittest

* remove ssd_loss, target_assigns

* remove target_assign, retinanet_target_assign, rpn_target_assign and ssd_loss
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python/paddle/fluid/layers/detection.py
浏览文件 @ 1976cc4b
...@@ -43,11 +43,7 @@ __all__ = [ ...@@ -43,11 +43,7 @@ __all__ = [
'density_prior_box', 'density_prior_box',
'multi_box_head', 'multi_box_head',
'bipartite_match', 'bipartite_match',
'target_assign',
'detection_output', 'detection_output',
'ssd_loss',
'rpn_target_assign',
'retinanet_target_assign',
'anchor_generator', 'anchor_generator',
'roi_perspective_transform', 'roi_perspective_transform',
'generate_proposal_labels', 'generate_proposal_labels',
...@@ -69,460 +65,6 @@ __all__ = [ ...@@ -69,460 +65,6 @@ __all__ = [
] ]
def retinanet_target_assign(
bbox_pred,
cls_logits,
anchor_box,
anchor_var,
gt_boxes,
gt_labels,
is_crowd,
im_info,
num_classes=1,
positive_overlap=0.5,
negative_overlap=0.4,
):
r"""
**Target Assign Layer for the detector RetinaNet.**
This OP finds out positive and negative samples from all anchors
for training the detector `RetinaNet <https://arxiv.org/abs/1708.02002>`_ ,
and assigns target labels for classification along with target locations for
regression to each sample, then takes out the part belonging to positive and
negative samples from category prediction( :attr:`cls_logits`) and location
prediction( :attr:`bbox_pred`) which belong to all anchors.
The searching principles for positive and negative samples are as followed:
1. Anchors are assigned to ground-truth boxes when it has the highest IoU
overlap with a ground-truth box.
2. Anchors are assigned to ground-truth boxes when it has an IoU overlap
higher than :attr:`positive_overlap` with any ground-truth box.
3. Anchors are assigned to background when its IoU overlap is lower than
:attr:`negative_overlap` for all ground-truth boxes.
4. Anchors which do not meet the above conditions do not participate in
the training process.
Retinanet predicts a :math:`C`-vector for classification and a 4-vector for box
regression for each anchor, hence the target label for each positive(or negative)
sample is a :math:`C`-vector and the target locations for each positive sample
is a 4-vector. As for a positive sample, if the category of its assigned
ground-truth box is class :math:`i`, the corresponding entry in its length
:math:`C` label vector is set to 1 and all other entries is set to 0, its box
regression targets are computed as the offset between itself and its assigned
ground-truth box. As for a negative sample, all entries in its length :math:`C`
label vector are set to 0 and box regression targets are omitted because
negative samples do not participate in the training process of location
regression.
After the assignment, the part belonging to positive and negative samples is
taken out from category prediction( :attr:`cls_logits` ), and the part
belonging to positive samples is taken out from location
prediction( :attr:`bbox_pred` ).
Args:
bbox_pred(Variable): A 3-D Tensor with shape :math:`[N, M, 4]` represents
the predicted locations of all anchors. :math:`N` is the batch size( the
number of images in a mini-batch), :math:`M` is the number of all anchors
of one image, and each anchor has 4 coordinate values. The data type of
:attr:`bbox_pred` is float32 or float64.
cls_logits(Variable): A 3-D Tensor with shape :math:`[N, M, C]` represents
the predicted categories of all anchors. :math:`N` is the batch size,
:math:`M` is the number of all anchors of one image, and :math:`C` is
the number of categories (**Notice: excluding background**). The data type
of :attr:`cls_logits` is float32 or float64.
anchor_box(Variable): A 2-D Tensor with shape :math:`[M, 4]` represents
the locations of all anchors. :math:`M` is the number of all anchors of
one image, each anchor is represented as :math:`[xmin, ymin, xmax, ymax]`,
:math:`[xmin, ymin]` is the left top coordinate of the anchor box,
:math:`[xmax, ymax]` is the right bottom coordinate of the anchor box.
The data type of :attr:`anchor_box` is float32 or float64. Please refer
to the OP :ref:`api_fluid_layers_anchor_generator`
for the generation of :attr:`anchor_box`.
anchor_var(Variable): A 2-D Tensor with shape :math:`[M,4]` represents the expanded
factors of anchor locations used in loss function. :math:`M` is number of
all anchors of one image, each anchor possesses a 4-vector expanded factor.
The data type of :attr:`anchor_var` is float32 or float64. Please refer
to the OP :ref:`api_fluid_layers_anchor_generator`
for the generation of :attr:`anchor_var`.
gt_boxes(Variable): A 1-level 2-D LoDTensor with shape :math:`[G, 4]` represents
locations of all ground-truth boxes. :math:`G` is the total number of
all ground-truth boxes in a mini-batch, and each ground-truth box has 4
coordinate values. The data type of :attr:`gt_boxes` is float32 or
float64.
gt_labels(variable): A 1-level 2-D LoDTensor with shape :math:`[G, 1]` represents
categories of all ground-truth boxes, and the values are in the range of
:math:`[1, C]`. :math:`G` is the total number of all ground-truth boxes
in a mini-batch, and each ground-truth box has one category. The data type
of :attr:`gt_labels` is int32.
is_crowd(Variable): A 1-level 1-D LoDTensor with shape :math:`[G]` which
indicates whether a ground-truth box is a crowd. If the value is 1, the
corresponding box is a crowd, it is ignored during training. :math:`G` is
the total number of all ground-truth boxes in a mini-batch. The data type
of :attr:`is_crowd` is int32.
im_info(Variable): A 2-D Tensor with shape [N, 3] represents the size
information of input images. :math:`N` is the batch size, the size
information of each image is a 3-vector which are the height and width
of the network input along with the factor scaling the origin image to
the network input. The data type of :attr:`im_info` is float32.
num_classes(int32): The number of categories for classification, the default
value is 1.
positive_overlap(float32): Minimum overlap required between an anchor
and ground-truth box for the anchor to be a positive sample, the default
value is 0.5.
negative_overlap(float32): Maximum overlap allowed between an anchor
and ground-truth box for the anchor to be a negative sample, the default
value is 0.4. :attr:`negative_overlap` should be less than or equal to
:attr:`positive_overlap`, if not, the actual value of
:attr:`positive_overlap` is :attr:`negative_overlap`.
Returns:
A tuple with 6 Variables:
**predict_scores** (Variable): A 2-D Tensor with shape :math:`[F+B, C]` represents
category prediction belonging to positive and negative samples. :math:`F`
is the number of positive samples in a mini-batch, :math:`B` is the number
of negative samples, and :math:`C` is the number of categories
(**Notice: excluding background**). The data type of :attr:`predict_scores`
is float32 or float64.
**predict_location** (Variable): A 2-D Tensor with shape :math:`[F, 4]` represents
location prediction belonging to positive samples. :math:`F` is the number
of positive samples. :math:`F` is the number of positive samples, and each
sample has 4 coordinate values. The data type of :attr:`predict_location`
is float32 or float64.
**target_label** (Variable): A 2-D Tensor with shape :math:`[F+B, 1]` represents
target labels for classification belonging to positive and negative
samples. :math:`F` is the number of positive samples, :math:`B` is the
number of negative, and each sample has one target category. The data type
of :attr:`target_label` is int32.
**target_bbox** (Variable): A 2-D Tensor with shape :math:`[F, 4]` represents
target locations for box regression belonging to positive samples.
:math:`F` is the number of positive samples, and each sample has 4
coordinate values. The data type of :attr:`target_bbox` is float32 or
float64.
**bbox_inside_weight** (Variable): A 2-D Tensor with shape :math:`[F, 4]`
represents whether a positive sample is fake positive, if a positive
sample is false positive, the corresponding entries in
:attr:`bbox_inside_weight` are set 0, otherwise 1. :math:`F` is the number
of total positive samples in a mini-batch, and each sample has 4
coordinate values. The data type of :attr:`bbox_inside_weight` is float32
or float64.
**fg_num** (Variable): A 2-D Tensor with shape :math:`[N, 1]` represents the number
of positive samples. :math:`N` is the batch size. **Notice: The number
of positive samples is used as the denominator of later loss function,
to avoid the condition that the denominator is zero, this OP has added 1
to the actual number of positive samples of each image.** The data type of
:attr:`fg_num` is int32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
bbox_pred = fluid.data(name='bbox_pred', shape=[1, 100, 4],
dtype='float32')
cls_logits = fluid.data(name='cls_logits', shape=[1, 100, 10],
dtype='float32')
anchor_box = fluid.data(name='anchor_box', shape=[100, 4],
dtype='float32')
anchor_var = fluid.data(name='anchor_var', shape=[100, 4],
dtype='float32')
gt_boxes = fluid.data(name='gt_boxes', shape=[10, 4],
dtype='float32')
gt_labels = fluid.data(name='gt_labels', shape=[10, 1],
dtype='int32')
is_crowd = fluid.data(name='is_crowd', shape=[1],
dtype='int32')
im_info = fluid.data(name='im_info', shape=[1, 3],
dtype='float32')
score_pred, loc_pred, score_target, loc_target, bbox_inside_weight, fg_num = \\
fluid.layers.retinanet_target_assign(bbox_pred, cls_logits, anchor_box,
anchor_var, gt_boxes, gt_labels, is_crowd, im_info, 10)
"""
check_variable_and_dtype(
bbox_pred,
'bbox_pred',
['float32', 'float64'],
'retinanet_target_assign',
)
check_variable_and_dtype(
cls_logits,
'cls_logits',
['float32', 'float64'],
'retinanet_target_assign',
)
check_variable_and_dtype(
anchor_box,
'anchor_box',
['float32', 'float64'],
'retinanet_target_assign',
)
check_variable_and_dtype(
anchor_var,
'anchor_var',
['float32', 'float64'],
'retinanet_target_assign',
)
check_variable_and_dtype(
gt_boxes, 'gt_boxes', ['float32', 'float64'], 'retinanet_target_assign'
)
check_variable_and_dtype(
gt_labels, 'gt_labels', ['int32'], 'retinanet_target_assign'
)
check_variable_and_dtype(
is_crowd, 'is_crowd', ['int32'], 'retinanet_target_assign'
)
check_variable_and_dtype(
im_info, 'im_info', ['float32', 'float64'], 'retinanet_target_assign'
)
helper = LayerHelper('retinanet_target_assign', **locals())
# Assign target label to anchors
loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32')
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype
)
bbox_inside_weight = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype
)
fg_num = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="retinanet_target_assign",
inputs={
'Anchor': anchor_box,
'GtBoxes': gt_boxes,
'GtLabels': gt_labels,
'IsCrowd': is_crowd,
'ImInfo': im_info,
},
outputs={
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label,
'TargetBBox': target_bbox,
'BBoxInsideWeight': bbox_inside_weight,
'ForegroundNumber': fg_num,
},
attrs={
'positive_overlap': positive_overlap,
'negative_overlap': negative_overlap,
},
)
loc_index.stop_gradient = True
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
bbox_inside_weight.stop_gradient = True
fg_num.stop_gradient = True
cls_logits = paddle.reshape(x=cls_logits, shape=(-1, num_classes))
bbox_pred = paddle.reshape(x=bbox_pred, shape=(-1, 4))
predicted_cls_logits = paddle.gather(cls_logits, score_index)
predicted_bbox_pred = paddle.gather(bbox_pred, loc_index)
return (
predicted_cls_logits,
predicted_bbox_pred,
target_label,
target_bbox,
bbox_inside_weight,
fg_num,
)
def rpn_target_assign(
bbox_pred,
cls_logits,
anchor_box,
anchor_var,
gt_boxes,
is_crowd,
im_info,
rpn_batch_size_per_im=256,
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3,
use_random=True,
):
"""
**Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection.**
This layer can be, for given the Intersection-over-Union (IoU) overlap
between anchors and ground truth boxes, to assign classification and
regression targets to each each anchor, these target labels are used for
train RPN. The classification targets is a binary class label (of being
an object or not). Following the paper of Faster-RCNN, the positive labels
are two kinds of anchors: (i) the anchor/anchors with the highest IoU
overlap with a ground-truth box, or (ii) an anchor that has an IoU overlap
higher than rpn_positive_overlap(0.7) with any ground-truth box. Note
that a single ground-truth box may assign positive labels to multiple
anchors. A non-positive anchor is when its IoU ratio is lower than
rpn_negative_overlap (0.3) for all ground-truth boxes. Anchors that are
neither positive nor negative do not contribute to the training objective.
The regression targets are the encoded ground-truth boxes associated with
the positive anchors.
Args:
bbox_pred(Variable): A 3-D Tensor with shape [N, M, 4] represents the
predicted locations of M bounding bboxes. N is the batch size,
and each bounding box has four coordinate values and the layout
is [xmin, ymin, xmax, ymax]. The data type can be float32 or float64.
cls_logits(Variable): A 3-D Tensor with shape [N, M, 1] represents the
predicted confidence predictions. N is the batch size, 1 is the
frontground and background sigmoid, M is number of bounding boxes.
The data type can be float32 or float64.
anchor_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
each box is represented as [xmin, ymin, xmax, ymax],
[xmin, ymin] is the left top coordinate of the anchor box,
if the input is image feature map, they are close to the origin
of the coordinate system. [xmax, ymax] is the right bottom
coordinate of the anchor box. The data type can be float32 or float64.
anchor_var(Variable): A 2-D Tensor with shape [M,4] holds expanded
variances of anchors. The data type can be float32 or float64.
gt_boxes (Variable): The ground-truth bounding boxes (bboxes) are a 2D
LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
bboxes of mini-batch input. The data type can be float32 or float64.
is_crowd (Variable): A 1-D LoDTensor which indicates groud-truth is crowd.
The data type must be int32.
im_info (Variable): A 2-D LoDTensor with shape [N, 3]. N is the batch size,
3 is the height, width and scale.
rpn_batch_size_per_im(int): Total number of RPN examples per image.
The data type must be int32.
rpn_straddle_thresh(float): Remove RPN anchors that go outside the image
by straddle_thresh pixels. The data type must be float32.
rpn_fg_fraction(float): Target fraction of RoI minibatch that is labeled
foreground (i.e. class > 0), 0-th class is background. The data type must be float32.
rpn_positive_overlap(float): Minimum overlap required between an anchor
and ground-truth box for the (anchor, gt box) pair to be a positive
example. The data type must be float32.
rpn_negative_overlap(float): Maximum overlap allowed between an anchor
and ground-truth box for the (anchor, gt box) pair to be a negative
examples. The data type must be float32.
Returns:
tuple:
A tuple(predicted_scores, predicted_location, target_label,
target_bbox, bbox_inside_weight) is returned. The predicted_scores
and predicted_location is the predicted result of the RPN.
The target_label and target_bbox is the ground truth,
respectively. The predicted_location is a 2D Tensor with shape
[F, 4], and the shape of target_bbox is same as the shape of
the predicted_location, F is the number of the foreground
anchors. The predicted_scores is a 2D Tensor with shape
[F + B, 1], and the shape of target_label is same as the shape
of the predicted_scores, B is the number of the background
anchors, the F and B is depends on the input of this operator.
Bbox_inside_weight represents whether the predicted loc is fake_fg
or not and the shape is [F, 4].
Examples:
.. code-block:: python
import paddle.fluid as fluid
bbox_pred = fluid.data(name='bbox_pred', shape=[None, 4], dtype='float32')
cls_logits = fluid.data(name='cls_logits', shape=[None, 1], dtype='float32')
anchor_box = fluid.data(name='anchor_box', shape=[None, 4], dtype='float32')
anchor_var = fluid.data(name='anchor_var', shape=[None, 4], dtype='float32')
gt_boxes = fluid.data(name='gt_boxes', shape=[None, 4], dtype='float32')
is_crowd = fluid.data(name='is_crowd', shape=[None], dtype='float32')
im_info = fluid.data(name='im_infoss', shape=[None, 3], dtype='float32')
loc, score, loc_target, score_target, inside_weight = fluid.layers.rpn_target_assign(
bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, is_crowd, im_info)
"""
helper = LayerHelper('rpn_target_assign', **locals())
check_variable_and_dtype(
bbox_pred, 'bbox_pred', ['float32', 'float64'], 'rpn_target_assign'
)
check_variable_and_dtype(
cls_logits, 'cls_logits', ['float32', 'float64'], 'rpn_target_assign'
)
check_variable_and_dtype(
anchor_box, 'anchor_box', ['float32', 'float64'], 'rpn_target_assign'
)
check_variable_and_dtype(
anchor_var, 'anchor_var', ['float32', 'float64'], 'rpn_target_assign'
)
check_variable_and_dtype(
gt_boxes, 'gt_boxes', ['float32', 'float64'], 'rpn_target_assign'
)
check_variable_and_dtype(
is_crowd, 'is_crowd', ['int32'], 'rpn_target_assign'
)
check_variable_and_dtype(
im_info, 'im_info', ['float32', 'float64'], 'rpn_target_assign'
)
# Assign target label to anchors
loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32')
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype
)
bbox_inside_weight = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype
)
helper.append_op(
type="rpn_target_assign",
inputs={
'Anchor': anchor_box,
'GtBoxes': gt_boxes,
'IsCrowd': is_crowd,
'ImInfo': im_info,
},
outputs={
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label,
'TargetBBox': target_bbox,
'BBoxInsideWeight': bbox_inside_weight,
},
attrs={
'rpn_batch_size_per_im': rpn_batch_size_per_im,
'rpn_straddle_thresh': rpn_straddle_thresh,
'rpn_positive_overlap': rpn_positive_overlap,
'rpn_negative_overlap': rpn_negative_overlap,
'rpn_fg_fraction': rpn_fg_fraction,
'use_random': use_random,
},
)
loc_index.stop_gradient = True
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
bbox_inside_weight.stop_gradient = True
cls_logits = paddle.reshape(x=cls_logits, shape=(-1, 1))
bbox_pred = paddle.reshape(x=bbox_pred, shape=(-1, 4))
predicted_cls_logits = paddle.gather(cls_logits, score_index)
predicted_bbox_pred = paddle.gather(bbox_pred, loc_index)
return (
predicted_cls_logits,
predicted_bbox_pred,
target_label,
target_bbox,
bbox_inside_weight,
)
def detection_output( def detection_output(
loc, loc,
scores, scores,
...@@ -1340,377 +882,6 @@ def bipartite_match( ...@@ -1340,377 +882,6 @@ def bipartite_match(
return match_indices, match_distance return match_indices, match_distance
def target_assign(
input,
matched_indices,
negative_indices=None,
mismatch_value=None,
name=None,
):
"""
This operator can be, for given the target bounding boxes or labels,
to assign classification and regression targets to each prediction as well as
weights to prediction. The weights is used to specify which prediction would
not contribute to training loss.
For each instance, the output `out` and`out_weight` are assigned based on
`match_indices` and `negative_indices`.
Assumed that the row offset for each instance in `input` is called lod,
this operator assigns classification/regression targets by performing the
following steps:
1. Assigning all outputs based on `match_indices`:
.. code-block:: text
If id = match_indices[i][j] > 0,
out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
out_weight[i][j] = 1.
Otherwise,
out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][j] = 0.
2. Assigning outputs based on `neg_indices` if `neg_indices` is provided:
Assumed that i-th instance in `neg_indices` is called `neg_indice`,
for i-th instance:
.. code-block:: text
for id in neg_indice:
out[i][id][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][id] = 1.0
Args:
input (Variable): This input is a 3D LoDTensor with shape [M, P, K].
Data type should be int32 or float32.
matched_indices (Variable): The input matched indices
is 2D Tenosr<int32> with shape [N, P], If MatchIndices[i][j] is -1,
the j-th entity of column is not matched to any entity of row in
i-th instance.
negative_indices (Variable, optional): The input negative example indices
are an optional input with shape [Neg, 1] and int32 type, where Neg is
the total number of negative example indices.
mismatch_value (float32, optional): Fill this value to the mismatched
location.
name (string): The default value is None. Normally there is no need for
user to set this property. For more information, please refer
to :ref:`api_guide_Name`.
Returns:
tuple: A tuple(out, out_weight) is returned.
out (Variable): a 3D Tensor with shape [N, P, K] and same data type
with `input`, N and P is the same as they are in `matched_indices`,
K is the same as it in input of X.
out_weight (Variable): the weight for output with the shape of [N, P, 1].
Data type is float32.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(
name='x',
shape=[4, 20, 4],
dtype='float',
lod_level=1)
matched_id = fluid.data(
name='indices',
shape=[8, 20],
dtype='int32')
trg, trg_weight = fluid.layers.target_assign(
x,
matched_id,
mismatch_value=0)
"""
helper = LayerHelper('target_assign', **locals())
out = helper.create_variable_for_type_inference(dtype=input.dtype)
out_weight = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type='target_assign',
inputs={
'X': input,
'MatchIndices': matched_indices,
'NegIndices': negative_indices,
},
outputs={'Out': out, 'OutWeight': out_weight},
attrs={'mismatch_value': mismatch_value},
)
return out, out_weight
def ssd_loss(
location,
confidence,
gt_box,
gt_label,
prior_box,
prior_box_var=None,
background_label=0,
overlap_threshold=0.5,
neg_pos_ratio=3.0,
neg_overlap=0.5,
loc_loss_weight=1.0,
conf_loss_weight=1.0,
match_type='per_prediction',
mining_type='max_negative',
normalize=True,
sample_size=None,
):
r"""
:alias_main: paddle.nn.functional.ssd_loss
:alias: paddle.nn.functional.ssd_loss,paddle.nn.functional.loss.ssd_loss
:old_api: paddle.fluid.layers.ssd_loss
**Multi-box loss layer for object detection algorithm of SSD**
This layer is to compute detection loss for SSD given the location offset
predictions, confidence predictions, prior boxes and ground-truth bounding
boxes and labels, and the type of hard example mining. The returned loss
is a weighted sum of the localization loss (or regression loss) and
confidence loss (or classification loss) by performing the following steps:
1. Find matched bounding box by bipartite matching algorithm.
1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
1.2 Compute matched bounding box by bipartite matching algorithm.
2. Compute confidence for mining hard examples
2.1. Get the target label based on matched indices.
2.2. Compute confidence loss.
3. Apply hard example mining to get the negative example indices and update
the matched indices.
4. Assign classification and regression targets
4.1. Encoded bbox according to the prior boxes.
4.2. Assign regression targets.
4.3. Assign classification targets.
5. Compute the overall objective loss.
5.1 Compute confidence loss.
5.2 Compute localization loss.
5.3 Compute the overall weighted loss.
Args:
location (Variable): The location predictions are a 3D Tensor with
shape [N, Np, 4], N is the batch size, Np is total number of
predictions for each instance. 4 is the number of coordinate values,
the layout is [xmin, ymin, xmax, ymax].The data type is float32 or
float64.
confidence (Variable): The confidence predictions are a 3D Tensor
with shape [N, Np, C], N and Np are the same as they are in
`location`, C is the class number.The data type is float32 or
float64.
gt_box (Variable): The ground-truth bounding boxes (bboxes) are a 2D
LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
bboxes of mini-batch input.The data type is float32 or float64.
gt_label (Variable): The ground-truth labels are a 2D LoDTensor
with shape [Ng, 1].Ng is the total number of ground-truth bboxes of
mini-batch input, 1 is the number of class. The data type is float32
or float64.
prior_box (Variable): The prior boxes are a 2D Tensor with shape [Np, 4].
Np and 4 are the same as they are in `location`. The data type is
float32 or float64.
prior_box_var (Variable): The variance of prior boxes are a 2D Tensor
with shape [Np, 4]. Np and 4 are the same as they are in `prior_box`
background_label (int): The index of background label, 0 by default.
overlap_threshold (float): If match_type is 'per_prediction', use
'overlap_threshold' to determine the extra matching bboxes when finding \
matched boxes. 0.5 by default.
neg_pos_ratio (float): The ratio of the negative boxes to the positive
boxes, used only when mining_type is 'max_negative', 3.0 by default.
neg_overlap (float): The negative overlap upper bound for the unmatched
predictions. Use only when mining_type is 'max_negative',
0.5 by default.
loc_loss_weight (float): Weight for localization loss, 1.0 by default.
conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
match_type (str): The type of matching method during training, should
be 'bipartite' or 'per_prediction', 'per_prediction' by default.
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
normalize (bool): Whether to normalize the SSD loss by the total number
of output locations, True by default.
sample_size (int): The max sample size of negative box, used only when
mining_type is 'hard_example'.
Returns:
Variable(Tensor): The weighted sum of the localization loss and confidence loss, \
with shape [N * Np, 1], N and Np are the same as they are in
`location`.The data type is float32 or float64.
Raises:
ValueError: If mining_type is 'hard_example', now only support mining \
type of `max_negative`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
pb = fluid.data(
name='prior_box',
shape=[10, 4],
dtype='float32')
pbv = fluid.data(
name='prior_box_var',
shape=[10, 4],
dtype='float32')
loc = fluid.data(name='target_box', shape=[10, 4], dtype='float32')
scores = fluid.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = fluid.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
gt_label = fluid.data(
name='gt_label', shape=[1], lod_level=1, dtype='float32')
loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
"""
helper = LayerHelper('ssd_loss', **locals())
if mining_type != 'max_negative':
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
conf_shape = paddle.shape(confidence)
def __reshape_to_2d(var):
out = paddle.flatten(var, 2, -1)
out = paddle.flatten(out, 0, 1)
return out
# 1. Find matched bounding box by prior box.
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
iou = iou_similarity(x=gt_box, y=prior_box)
# 1.2 Compute matched bounding box by bipartite matching algorithm.
matched_indices, matched_dist = bipartite_match(
iou, match_type, overlap_threshold
)
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
gt_label = paddle.reshape(
x=gt_label, shape=(len(gt_label.shape) - 1) * (0,) + (-1, 1)
)
gt_label.stop_gradient = True
target_label, _ = target_assign(
gt_label, matched_indices, mismatch_value=background_label
)
# 2.2. Compute confidence loss.
# Reshape confidence to 2D tensor.
confidence = __reshape_to_2d(confidence)
target_label = tensor.cast(x=target_label, dtype='int64')
target_label = __reshape_to_2d(target_label)
target_label.stop_gradient = True
conf_loss = softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
actual_shape = paddle.slice(conf_shape, axes=[0], starts=[0], ends=[2])
actual_shape.stop_gradient = True
# shape=(-1, 0) is set for compile-time, the correct shape is set by
# actual_shape in runtime.
conf_loss = paddle.reshape(x=conf_loss, shape=actual_shape)
conf_loss.stop_gradient = True
neg_indices = helper.create_variable_for_type_inference(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_variable_for_type_inference(
dtype=dtype
)
helper.append_op(
type='mine_hard_examples',
inputs={
'ClsLoss': conf_loss,
'LocLoss': None,
'MatchIndices': matched_indices,
'MatchDist': matched_dist,
},
outputs={
'NegIndices': neg_indices,
'UpdatedMatchIndices': updated_matched_indices,
},
attrs={
'neg_pos_ratio': neg_pos_ratio,
'neg_dist_threshold': neg_overlap,
'mining_type': mining_type,
'sample_size': sample_size,
},
)
# 4. Assign classification and regression targets
# 4.1. Encoded bbox according to the prior boxes.
encoded_bbox = box_coder(
prior_box=prior_box,
prior_box_var=prior_box_var,
target_box=gt_box,
code_type='encode_center_size',
)
# 4.2. Assign regression targets
target_bbox, target_loc_weight = target_assign(
encoded_bbox, updated_matched_indices, mismatch_value=background_label
)
# 4.3. Assign classification targets
target_label, target_conf_weight = target_assign(
gt_label,
updated_matched_indices,
negative_indices=neg_indices,
mismatch_value=background_label,
)
# 5. Compute loss.
# 5.1 Compute confidence loss.
target_label = __reshape_to_2d(target_label)
target_label = tensor.cast(x=target_label, dtype='int64')
conf_loss = softmax_with_cross_entropy(confidence, target_label)
target_conf_weight = __reshape_to_2d(target_conf_weight)
conf_loss = conf_loss * target_conf_weight
# the target_label and target_conf_weight do not have gradient.
target_label.stop_gradient = True
target_conf_weight.stop_gradient = True
# 5.2 Compute regression loss.
location = __reshape_to_2d(location)
target_bbox = __reshape_to_2d(target_bbox)
smooth_l1_loss = paddle.nn.loss.SmoothL1Loss()
loc_loss = smooth_l1_loss(location, target_bbox)
target_loc_weight = __reshape_to_2d(target_loc_weight)
loc_loss = loc_loss * target_loc_weight
# the target_bbox and target_loc_weight do not have gradient.
target_bbox.stop_gradient = True
target_loc_weight.stop_gradient = True
# 5.3 Compute overall weighted loss.
loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
# reshape to [N, Np], N is the batch size and Np is the prior box number.
# shape=(-1, 0) is set for compile-time, the correct shape is set by
# actual_shape in runtime.
loss = paddle.reshape(x=loss, shape=actual_shape)
loss = paddle.sum(loss, axis=1, keepdim=True)
if normalize:
normalizer = paddle.sum(target_loc_weight)
loss = loss / normalizer
return loss
def prior_box( def prior_box(
input, input,
image, image,
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 1976cc4b
...@@ -163,74 +163,6 @@ class TestDetection(unittest.TestCase): ...@@ -163,74 +163,6 @@ class TestDetection(unittest.TestCase):
code_type='encode_center_size', code_type='encode_center_size',
) )
def test_detection_api(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='y', shape=[4], dtype='float32')
z = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
iou = layers.iou_similarity(x=x, y=y)
bcoder = layers.box_coder(
prior_box=x,
prior_box_var=y,
target_box=z,
code_type='encode_center_size',
)
self.assertIsNotNone(iou)
self.assertIsNotNone(bcoder)
matched_indices, matched_dist = layers.bipartite_match(iou)
self.assertIsNotNone(matched_indices)
self.assertIsNotNone(matched_dist)
gt = layers.data(
name='gt', shape=[1, 1], dtype='int32', lod_level=1
)
trg, trg_weight = layers.target_assign(
gt, matched_indices, mismatch_value=0
)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
gt2 = layers.data(
name='gt2', shape=[10, 4], dtype='float32', lod_level=1
)
trg, trg_weight = layers.target_assign(
gt2, matched_indices, mismatch_value=0
)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
print(str(program))
def test_ssd_loss(self):
program = Program()
with program_guard(program):
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32',
)
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32',
)
loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32'
)
gt_label = layers.data(
name='gt_label', shape=[1], lod_level=1, dtype='int32'
)
loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
self.assertIsNotNone(loss)
self.assertEqual(loss.shape[-1], 1)
print(str(program))
class TestPriorBox(unittest.TestCase): class TestPriorBox(unittest.TestCase):
def test_prior_box(self): def test_prior_box(self):
...@@ -521,87 +453,6 @@ class TestDetectionMAP(unittest.TestCase): ...@@ -521,87 +453,6 @@ class TestDetectionMAP(unittest.TestCase):
print(str(program)) print(str(program))
class TestRpnTargetAssign(unittest.TestCase):
def test_rpn_target_assign(self):
program = Program()
with program_guard(program):
bbox_pred_shape = [10, 50, 4]
cls_logits_shape = [10, 50, 2]
anchor_shape = [50, 4]
bbox_pred = layers.data(
name='bbox_pred',
shape=bbox_pred_shape,
append_batch_size=False,
dtype='float32',
)
cls_logits = layers.data(
name='cls_logits',
shape=cls_logits_shape,
append_batch_size=False,
dtype='float32',
)
anchor_box = layers.data(
name='anchor_box',
shape=anchor_shape,
append_batch_size=False,
dtype='float32',
)
anchor_var = layers.data(
name='anchor_var',
shape=anchor_shape,
append_batch_size=False,
dtype='float32',
)
gt_boxes = layers.data(
name='gt_boxes', shape=[4], lod_level=1, dtype='float32'
)
is_crowd = layers.data(
name='is_crowd',
shape=[1, 10],
dtype='int32',
lod_level=1,
append_batch_size=False,
)
im_info = layers.data(
name='im_info',
shape=[1, 3],
dtype='float32',
lod_level=1,
append_batch_size=False,
)
outs = layers.rpn_target_assign(
bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
anchor_var=anchor_var,
gt_boxes=gt_boxes,
is_crowd=is_crowd,
im_info=im_info,
rpn_batch_size_per_im=256,
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3,
use_random=False,
)
pred_scores = outs[0]
pred_loc = outs[1]
tgt_lbl = outs[2]
tgt_bbox = outs[3]
bbox_inside_weight = outs[4]
self.assertIsNotNone(pred_scores)
self.assertIsNotNone(pred_loc)
self.assertIsNotNone(tgt_lbl)
self.assertIsNotNone(tgt_bbox)
self.assertIsNotNone(bbox_inside_weight)
assert pred_scores.shape[1] == 1
assert pred_loc.shape[1] == 4
assert pred_loc.shape[1] == tgt_bbox.shape[1]
print(str(program))
class TestGenerateProposals(LayerTest): class TestGenerateProposals(LayerTest):
def test_generate_proposals(self): def test_generate_proposals(self):
scores_np = np.random.rand(2, 3, 4, 4).astype('float32') scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 1976cc4b
...@@ -3288,70 +3288,6 @@ class TestBook(LayerTest): ...@@ -3288,70 +3288,6 @@ class TestBook(LayerTest):
) )
return out return out
def test_retinanet_target_assign(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
bbox_pred = layers.data(
name='bbox_pred',
shape=[1, 100, 4],
append_batch_size=False,
dtype='float32',
)
cls_logits = layers.data(
name='cls_logits',
shape=[1, 100, 10],
append_batch_size=False,
dtype='float32',
)
anchor_box = layers.data(
name='anchor_box',
shape=[100, 4],
append_batch_size=False,
dtype='float32',
)
anchor_var = layers.data(
name='anchor_var',
shape=[100, 4],
append_batch_size=False,
dtype='float32',
)
gt_boxes = layers.data(
name='gt_boxes',
shape=[10, 4],
append_batch_size=False,
dtype='float32',
)
gt_labels = layers.data(
name='gt_labels',
shape=[10, 1],
append_batch_size=False,
dtype='int32',
)
is_crowd = layers.data(
name='is_crowd',
shape=[1],
append_batch_size=False,
dtype='int32',
)
im_info = layers.data(
name='im_info',
shape=[1, 3],
append_batch_size=False,
dtype='float32',
)
return layers.retinanet_target_assign(
bbox_pred,
cls_logits,
anchor_box,
anchor_var,
gt_boxes,
gt_labels,
is_crowd,
im_info,
10,
)
def test_addmm(self): def test_addmm(self):
with program_guard( with program_guard(
fluid.default_main_program(), fluid.default_startup_program() fluid.default_main_program(), fluid.default_startup_program()
......
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @ 1976cc4b
...@@ -23,9 +23,6 @@ from test_generate_proposal_labels_op import ( ...@@ -23,9 +23,6 @@ from test_generate_proposal_labels_op import (
_generate_groundtruth, _generate_groundtruth,
) )
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
def rpn_target_assign( def rpn_target_assign(
anchor_by_gt_overlap, anchor_by_gt_overlap,
...@@ -485,424 +482,5 @@ class TestRetinanetTargetAssignOp(OpTest): ...@@ -485,424 +482,5 @@ class TestRetinanetTargetAssignOp(OpTest):
self.check_output() self.check_output()
class TestRetinanetTargetAssignOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
bbox_pred1 = fluid.data(
name='bbox_pred1', shape=[1, 100, 4], dtype='float32'
)
cls_logits1 = fluid.data(
name='cls_logits1', shape=[1, 100, 10], dtype='float32'
)
anchor_box1 = fluid.data(
name='anchor_box1', shape=[100, 4], dtype='float32'
)
anchor_var1 = fluid.data(
name='anchor_var1', shape=[100, 4], dtype='float32'
)
gt_boxes1 = fluid.data(
name='gt_boxes1', shape=[10, 4], dtype='float32'
)
gt_labels1 = fluid.data(
name='gt_labels1', shape=[10, 1], dtype='int32'
)
is_crowd1 = fluid.data(name='is_crowd1', shape=[1], dtype='float32')
im_info1 = fluid.data(
name='im_info1', shape=[1, 3], dtype='float32'
)
# The `bbox_pred` must be Variable and the data type of `bbox_pred` Tensor
# one of float32 and float64.
def test_bbox_pred_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
[1],
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_bbox_pred_type)
def test_bbox_pred_tensor_dtype():
bbox_pred2 = fluid.data(
name='bbox_pred2', shape=[1, 100, 4], dtype='intt32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred2,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_bbox_pred_tensor_dtype)
# The `cls_logits` must be Variable and the data type of `cls_logits` Tensor
# one of float32 and float64.
def test_cls_logits_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
2,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_cls_logits_type)
def test_cls_logits_tensor_dtype():
cls_logits2 = fluid.data(
name='cls_logits2', shape=[1, 100, 10], dtype='int32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits2,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_cls_logits_tensor_dtype)
# The `anchor_box` must be Variable and the data type of `anchor_box` Tensor
# one of float32 and float64.
def test_anchor_box_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
[5],
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_anchor_box_type)
def test_anchor_box_tensor_dtype():
anchor_box2 = fluid.data(
name='anchor_box2', shape=[100, 4], dtype='int32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box2,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_anchor_box_tensor_dtype)
# The `anchor_var` must be Variable and the data type of `anchor_var` Tensor
# one of float32 and float64.
def test_anchor_var_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
5,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_anchor_var_type)
def test_anchor_var_tensor_dtype():
anchor_var2 = fluid.data(
name='anchor_var2', shape=[100, 4], dtype='int32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var2,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_anchor_var_tensor_dtype)
# The `gt_boxes` must be Variable and the data type of `gt_boxes` Tensor
# one of float32 and float64.
def test_gt_boxes_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
[4],
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_gt_boxes_type)
def test_gt_boxes_tensor_dtype():
gt_boxes2 = fluid.data(
name='gt_boxes2', shape=[10, 4], dtype='int32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes2,
gt_labels1,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_gt_boxes_tensor_dtype)
# The `gt_label` must be Variable and the data type of `gt_label` Tensor
# int32.
def test_gt_label_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
9,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_gt_label_type)
def test_gt_label_tensor_dtype():
gt_labels2 = fluid.data(
name='label2', shape=[10, 1], dtype='float32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels2,
is_crowd1,
im_info1,
10,
)
self.assertRaises(TypeError, test_gt_label_tensor_dtype)
# The `is_crowd` must be Variable and the data type of `is_crowd` Tensor
# int32.
def test_is_crowd_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
[10],
im_info1,
10,
)
self.assertRaises(TypeError, test_is_crowd_type)
def test_is_crowd_tensor_dtype():
is_crowd2 = fluid.data(
name='is_crowd2', shape=[10, 1], dtype='float32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd2,
im_info1,
10,
)
self.assertRaises(TypeError, test_is_crowd_tensor_dtype)
# The `im_info` must be Variable and the data type of `im_info` Tensor
# must be one of float32 and float64.
def test_im_info_type():
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
1,
10,
)
self.assertRaises(TypeError, test_im_info_type)
def test_im_info_tensor_dtype():
im_info2 = fluid.data(
name='im_info2', shape=[1, 3], dtype='int32'
)
(
score_pred,
loc_pred,
score_target,
loc_target,
bbox_inside_weight,
fg_num,
) = fluid.layers.retinanet_target_assign(
bbox_pred1,
cls_logits1,
anchor_box1,
anchor_var1,
gt_boxes1,
gt_labels1,
is_crowd1,
im_info2,
10,
)
self.assertRaises(TypeError, test_im_info_tensor_dtype)
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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