alias yolo_loss & yolo_box to paddle.vision. (#28520)

* alias yolo_loss & decode_yolo_box to paddle.vision. test=develop

python/paddle/fluid/layers/detection.py

@@ -33,6 +33,7 @@ import six
 import numpy as np
 from functools import reduce
 from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
+from paddle.utils import deprecated
 
 __all__ = [
     'prior_box',
@@ -998,6 +999,7 @@ def polygon_box_transform(input, name=None):
     return output
 
 
+@deprecated(since="2.0.0", update_to="paddle.vision.ops.yolo_loss")
 @templatedoc(op_type="yolov3_loss")
 def yolov3_loss(x,
                 gt_box,
@@ -1127,6 +1129,7 @@ def yolov3_loss(x,
     return loss
 
 
+@deprecated(since="2.0.0", update_to="paddle.vision.ops.yolo_box")
 @templatedoc(op_type="yolo_box")
 def yolo_box(x,
              img_size,

python/paddle/fluid/tests/unittests/test_yolo_box_op.py

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
+import paddle
 from paddle.fluid import core
 
 
@@ -151,5 +152,44 @@ class TestYoloBoxOpScaleXY(TestYoloBoxOp):
         self.scale_x_y = 1.2
 
 
+class TestYoloBoxDygraph(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+        x = np.random.random([2, 14, 8, 8]).astype('float32')
+        img_size = np.ones((2, 2)).astype('int32')
+
+        x = paddle.to_tensor(x)
+        img_size = paddle.to_tensor(img_size)
+
+        boxes, scores = paddle.vision.ops.yolo_box(
+            x,
+            img_size=img_size,
+            anchors=[10, 13, 16, 30],
+            class_num=2,
+            conf_thresh=0.01,
+            downsample_ratio=8,
+            clip_bbox=True,
+            scale_x_y=1.)
+        assert boxes is not None and scores is not None
+        paddle.enable_static()
+
+
+class TestYoloBoxStatic(unittest.TestCase):
+    def test_static(self):
+        x = paddle.static.data('x', [2, 14, 8, 8], 'float32')
+        img_size = paddle.static.data('img_size', [2, 2], 'int32')
+
+        boxes, scores = paddle.vision.ops.yolo_box(
+            x,
+            img_size=img_size,
+            anchors=[10, 13, 16, 30],
+            class_num=2,
+            conf_thresh=0.01,
+            downsample_ratio=8,
+            clip_bbox=True,
+            scale_x_y=1.)
+        assert boxes is not None and scores is not None
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py

@@ -20,6 +20,7 @@ from scipy.special import logit
 from scipy.special import expit
 from op_test import OpTest
 
+import paddle
 from paddle.fluid import core
 
 
@@ -281,5 +282,66 @@ class TestYolov3LossWithScaleXY(TestYolov3LossOp):
         self.scale_x_y = 1.2
 
 
+class TestYolov3LossDygraph(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+        x = np.random.random([2, 14, 8, 8]).astype('float32')
+        gt_box = np.random.random([2, 10, 4]).astype('float32')
+        gt_label = np.random.random([2, 10]).astype('int32')
+
+        x = paddle.to_tensor(x)
+        gt_box = paddle.to_tensor(gt_box)
+        gt_label = paddle.to_tensor(gt_label)
+
+        loss = paddle.vision.ops.yolo_loss(
+            x,
+            gt_box=gt_box,
+            gt_label=gt_label,
+            anchors=[10, 13, 16, 30],
+            anchor_mask=[0, 1],
+            class_num=2,
+            ignore_thresh=0.7,
+            downsample_ratio=8,
+            use_label_smooth=True,
+            scale_x_y=1.)
+        assert loss is not None
+        paddle.enable_static()
+
+
+class TestYolov3LossStatic(unittest.TestCase):
+    def test_static(self):
+        x = paddle.static.data('x', [2, 14, 8, 8], 'float32')
+        gt_box = paddle.static.data('gt_box', [2, 10, 4], 'float32')
+        gt_label = paddle.static.data('gt_label', [2, 10], 'int32')
+        gt_score = paddle.static.data('gt_score', [2, 10], 'float32')
+
+        loss = paddle.vision.ops.yolo_loss(
+            x,
+            gt_box=gt_box,
+            gt_label=gt_label,
+            anchors=[10, 13, 16, 30],
+            anchor_mask=[0, 1],
+            class_num=2,
+            ignore_thresh=0.7,
+            downsample_ratio=8,
+            gt_score=gt_score,
+            use_label_smooth=True,
+            scale_x_y=1.)
+        assert loss is not None
+
+        loss = paddle.vision.ops.yolo_loss(
+            x,
+            gt_box=gt_box,
+            gt_label=gt_label,
+            anchors=[10, 13, 16, 30],
+            anchor_mask=[0, 1],
+            class_num=2,
+            ignore_thresh=0.7,
+            downsample_ratio=8,
+            use_label_smooth=True,
+            scale_x_y=1.)
+        assert loss is not None
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/vision/__init__.py

@@ -24,6 +24,8 @@ from .datasets import *
 from . import image
 from .image import *
 
+from . import ops
+
 __all__ = models.__all__ \
         + transforms.__all__ \
         + datasets.__all__ \

python/paddle/vision/ops.py

+#   Copyright (c) 2020 PaddlePaddle 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.
+
+import numpy as np
+from ..fluid.layer_helper import LayerHelper
+from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
+from ..fluid import core, layers
+
+from paddle.common_ops_import import *
+
+__all__ = ['yolo_loss', 'yolo_box']
+
+
+def yolo_loss(x,
+              gt_box,
+              gt_label,
+              anchors,
+              anchor_mask,
+              class_num,
+              ignore_thresh,
+              downsample_ratio,
+              gt_score=None,
+              use_label_smooth=True,
+              name=None,
+              scale_x_y=1.):
+    """
+
+    This operator generates YOLOv3 loss based on given predict result and ground
+    truth boxes.
+    
+    The output of previous network is in shape [N, C, H, W], while H and W
+    should be the same, H and W specify the grid size, each grid point predict 
+    given number bounding boxes, this given number, which following will be represented as S,
+    is specified by the number of anchor clusters in each scale. In the second dimension(the channel
+    dimension), C should be equal to S * (class_num + 5), class_num is the object 
+    category number of source dataset(such as 80 in coco dataset), so in the 
+    second(channel) dimension, apart from 4 box location coordinates x, y, w, h, 
+    also includes confidence score of the box and class one-hot key of each anchor box.
+
+    Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box predictions
+    should be as follows:
+
+    $$
+    b_x = \\sigma(t_x) + c_x
+    $$
+    $$
+    b_y = \\sigma(t_y) + c_y
+    $$
+    $$
+    b_w = p_w e^{t_w}
+    $$
+    $$
+    b_h = p_h e^{t_h}
+    $$
+
+    In the equation above, :math:`c_x, c_y` is the left top corner of current grid
+    and :math:`p_w, p_h` is specified by anchors.
+
+    As for confidence score, it is the logistic regression value of IoU between
+    anchor boxes and ground truth boxes, the score of the anchor box which has 
+    the max IoU should be 1, and if the anchor box has IoU bigger than ignore 
+    thresh, the confidence score loss of this anchor box will be ignored.
+
+    Therefore, the YOLOv3 loss consists of three major parts: box location loss,
+    objectness loss and classification loss. The L1 loss is used for 
+    box coordinates (w, h), sigmoid cross entropy loss is used for box 
+    coordinates (x, y), objectness loss and classification loss.
+
+    Each groud truth box finds a best matching anchor box in all anchors. 
+    Prediction of this anchor box will incur all three parts of losses, and
+    prediction of anchor boxes with no GT box matched will only incur objectness
+    loss.
+
+    In order to trade off box coordinate losses between big boxes and small 
+    boxes, box coordinate losses will be mutiplied by scale weight, which is
+    calculated as follows.
+
+    $$
+    weight_{box} = 2.0 - t_w * t_h
+    $$
+
+    Final loss will be represented as follows.
+
+    $$
+    loss = (loss_{xy} + loss_{wh}) * weight_{box}
+         + loss_{conf} + loss_{class}
+    $$
+
+    While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
+    target will be smoothed when calculating classification loss, target of 
+    positive samples will be smoothed to :math:`1.0 - 1.0 / class\_num` and target of
+    negetive samples will be smoothed to :math:`1.0 / class\_num`.
+
+    While :attr:`gt_score` is given, which means the mixup score of ground truth 
+    boxes, all losses incured by a ground truth box will be multiplied by its 
+    mixup score.
+
+    Args:
+        x (Tensor): The input tensor of YOLOv3 loss operator, This is a 4-D
+                      tensor with shape of [N, C, H, W]. H and W should be same,
+                      and the second dimension(C) stores box locations, confidence
+                      score and classification one-hot keys of each anchor box.
+                      The data type is float32 or float64. 
+        gt_box (Tensor): groud truth boxes, should be in shape of [N, B, 4],
+                          in the third dimension, x, y, w, h should be stored. 
+                          x,y is the center coordinate of boxes, w, h are the
+                          width and height, x, y, w, h should be divided by 
+                          input image height to scale to [0, 1].
+                          N is the batch number and B is the max box number in 
+                          an image.The data type is float32 or float64. 
+        gt_label (Tensor): class id of ground truth boxes, should be in shape
+                            of [N, B].The data type is int32. 
+        anchors (list|tuple): The anchor width and height, it will be parsed
+                              pair by pair.
+        anchor_mask (list|tuple): The mask index of anchors used in current
+                                  YOLOv3 loss calculation.
+        class_num (int): The number of classes.
+        ignore_thresh (float): The ignore threshold to ignore confidence loss.
+        downsample_ratio (int): The downsample ratio from network input to YOLOv3
+                                loss input, so 32, 16, 8 should be set for the
+                                first, second, and thrid YOLOv3 loss operators. 
+        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`
+        gt_score (Tensor): mixup score of ground truth boxes, should be in shape
+                            of [N, B]. Default None.
+        use_label_smooth (bool): Whether to use label smooth. Default True. 
+        scale_x_y (float): Scale the center point of decoded bounding box.
+                           Default 1.0
+
+    Returns:
+        Tensor: A 1-D tensor with shape [N], the value of yolov3 loss
+
+    Raises:
+        TypeError: Input x of yolov3_loss must be Tensor
+        TypeError: Input gtbox of yolov3_loss must be Tensor 
+        TypeError: Input gtlabel of yolov3_loss must be Tensor 
+        TypeError: Input gtscore of yolov3_loss must be None or Tensor 
+        TypeError: Attr anchors of yolov3_loss must be list or tuple
+        TypeError: Attr class_num of yolov3_loss must be an integer
+        TypeError: Attr ignore_thresh of yolov3_loss must be a float number
+        TypeError: Attr use_label_smooth of yolov3_loss must be a bool value
+
+    Examples:
+      .. code-block:: python
+
+          import paddle
+          import numpy as np
+
+          x = np.random.random([2, 14, 8, 8]).astype('float32')
+          gt_box = np.random.random([2, 10, 4]).astype('float32')
+          gt_label = np.random.random([2, 10]).astype('int32')
+
+          x = paddle.to_tensor(x)
+          gt_box = paddle.to_tensor(gt_box)
+          gt_label = paddle.to_tensor(gt_label)
+
+          loss = paddle.vision.ops.yolo_loss(x,
+                                             gt_box=gt_box,
+                                             gt_label=gt_label,
+                                             anchors=[10, 13, 16, 30],
+                                             anchor_mask=[0, 1],
+                                             class_num=2,
+                                             ignore_thresh=0.7,
+                                             downsample_ratio=8,
+                                             use_label_smooth=True,
+                                             scale_x_y=1.)
+    """
+
+    if in_dygraph_mode() and gt_score is None:
+        loss = core.ops.yolov3_loss(
+            x, gt_box, gt_label, 'anchors', anchors, 'anchor_mask', anchor_mask,
+            'class_num', class_num, 'ignore_thresh', ignore_thresh,
+            'downsample_ratio', downsample_ratio, 'use_label_smooth',
+            use_label_smooth, 'scale_x_y', scale_x_y)
+        return loss
+
+    helper = LayerHelper('yolov3_loss', **locals())
+
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'yolo_loss')
+    check_variable_and_dtype(gt_box, 'gt_box', ['float32', 'float64'],
+                             'yolo_loss')
+    check_variable_and_dtype(gt_label, 'gt_label', 'int32', 'yolo_loss')
+    check_type(anchors, 'anchors', (list, tuple), 'yolo_loss')
+    check_type(anchor_mask, 'anchor_mask', (list, tuple), 'yolo_loss')
+    check_type(class_num, 'class_num', int, 'yolo_loss')
+    check_type(ignore_thresh, 'ignore_thresh', float, 'yolo_loss')
+    check_type(use_label_smooth, 'use_label_smooth', bool, 'yolo_loss')
+
+    loss = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    objectness_mask = helper.create_variable_for_type_inference(dtype='int32')
+    gt_match_mask = helper.create_variable_for_type_inference(dtype='int32')
+
+    inputs = {
+        "X": x,
+        "GTBox": gt_box,
+        "GTLabel": gt_label,
+    }
+    if gt_score is not None:
+        inputs["GTScore"] = gt_score
+
+    attrs = {
+        "anchors": anchors,
+        "anchor_mask": anchor_mask,
+        "class_num": class_num,
+        "ignore_thresh": ignore_thresh,
+        "downsample_ratio": downsample_ratio,
+        "use_label_smooth": use_label_smooth,
+        "scale_x_y": scale_x_y,
+    }
+
+    helper.append_op(
+        type='yolov3_loss',
+        inputs=inputs,
+        outputs={
+            'Loss': loss,
+            'ObjectnessMask': objectness_mask,
+            'GTMatchMask': gt_match_mask
+        },
+        attrs=attrs)
+    return loss
+
+
+def yolo_box(x,
+             img_size,
+             anchors,
+             class_num,
+             conf_thresh,
+             downsample_ratio,
+             clip_bbox=True,
+             name=None,
+             scale_x_y=1.):
+    """
+
+    This operator generates YOLO detection boxes from output of YOLOv3 network.
+    
+    The output of previous network is in shape [N, C, H, W], while H and W
+    should be the same, H and W specify the grid size, each grid point predict 
+    given number boxes, this given number, which following will be represented as S,
+    is specified by the number of anchors. In the second dimension(the channel
+    dimension), C should be equal to S * (5 + class_num), class_num is the object 
+    category number of source dataset(such as 80 in coco dataset), so the 
+    second(channel) dimension, apart from 4 box location coordinates x, y, w, h, 
+    also includes confidence score of the box and class one-hot key of each anchor 
+    box.
+
+    Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box 
+    predictions should be as follows:
+
+    $$
+    b_x = \\sigma(t_x) + c_x
+    $$
+    $$
+    b_y = \\sigma(t_y) + c_y
+    $$
+    $$
+    b_w = p_w e^{t_w}
+    $$
+    $$
+    b_h = p_h e^{t_h}
+    $$
+
+    in the equation above, :math:`c_x, c_y` is the left top corner of current grid
+    and :math:`p_w, p_h` is specified by anchors.
+
+    The logistic regression value of the 5th channel of each anchor prediction boxes
+    represents the confidence score of each prediction box, and the logistic
+    regression value of the last :attr:`class_num` channels of each anchor prediction 
+    boxes represents the classifcation scores. Boxes with confidence scores less than
+    :attr:`conf_thresh` should be ignored, and box final scores is the product of 
+    confidence scores and classification scores.
+
+    $$
+    score_{pred} = score_{conf} * score_{class}
+    $$
+
+    Args:
+        x (Tensor): The input tensor of YoloBox operator is a 4-D tensor with
+                      shape of [N, C, H, W]. The second dimension(C) stores box
+                      locations, confidence score and classification one-hot keys
+                      of each anchor box. Generally, X should be the output of
+                      YOLOv3 network. The data type is float32 or float64. 
+        img_size (Tensor): The image size tensor of YoloBox operator, This is a
+                           2-D tensor with shape of [N, 2]. This tensor holds
+                           height and width of each input image used for resizing
+                           output box in input image scale. The data type is int32. 
+        anchors (list|tuple): The anchor width and height, it will be parsed pair
+                              by pair.
+        class_num (int): The number of classes.
+        conf_thresh (float): The confidence scores threshold of detection boxes.
+                             Boxes with confidence scores under threshold should
+                             be ignored.
+        downsample_ratio (int): The downsample ratio from network input to
+                                :attr:`yolo_box` operator input, so 32, 16, 8
+                                should be set for the first, second, and thrid
+                                :attr:`yolo_box` layer.
+        clip_bbox (bool): Whether clip output bonding box in :attr:`img_size`
+                          boundary. Default true."
+        "
+        scale_x_y (float): Scale the center point of decoded bounding box.
+                           Default 1.0
+        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:
+        Tensor: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
+        and a 3-D tensor with shape [N, M, :attr:`class_num`], the classification 
+        scores of boxes.
+
+    Raises:
+        TypeError: Input x of yolov_box must be Tensor
+        TypeError: Attr anchors of yolo box must be list or tuple
+        TypeError: Attr class_num of yolo box must be an integer
+        TypeError: Attr conf_thresh of yolo box must be a float number
+
+    Examples:
+
+    .. code-block:: python
+
+        import paddle
+        import numpy as np
+
+	x = np.random.random([2, 14, 8, 8]).astype('float32')
+        img_size = np.ones((2, 2)).astype('int32')
+
+        x = paddle.to_tensor(x)
+        img_size = paddle.to_tensor(img_size)
+
+        boxes, scores = paddle.vision.ops.yolo_box(x,
+                                                   img_size=img_size,
+                                                   anchors=[10, 13, 16, 30],
+                                                   class_num=2,
+                                                   conf_thresh=0.01,
+                                                   downsample_ratio=8,
+                                                   clip_bbox=True,
+                                                   scale_x_y=1.)
+    """
+    if in_dygraph_mode():
+        boxes, scores = core.ops.yolo_box(
+            x, img_size, 'anchors', anchors, 'class_num', class_num,
+            'conf_thresh', conf_thresh, 'downsample_ratio', downsample_ratio,
+            'clip_bbox', clip_bbox, 'scale_x_y', scale_x_y)
+        return boxes, scores
+
+    helper = LayerHelper('yolo_box', **locals())
+
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'yolo_box')
+    check_variable_and_dtype(img_size, 'img_size', 'int32', 'yolo_box')
+    check_type(anchors, 'anchors', (list, tuple), 'yolo_box')
+    check_type(conf_thresh, 'conf_thresh', float, 'yolo_box')
+
+    boxes = helper.create_variable_for_type_inference(dtype=x.dtype)
+    scores = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    attrs = {
+        "anchors": anchors,
+        "class_num": class_num,
+        "conf_thresh": conf_thresh,
+        "downsample_ratio": downsample_ratio,
+        "clip_bbox": clip_bbox,
+        "scale_x_y": scale_x_y,
+    }
+
+    helper.append_op(
+        type='yolo_box',
+        inputs={
+            "X": x,
+            "ImgSize": img_size,
+        },
+        outputs={
+            'Boxes': boxes,
+            'Scores': scores,
+        },
+        attrs=attrs)
+    return boxes, scores