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未验证 提交 d3e9e73a 编写于 作者: Z zqw_1997 提交者： GitHub
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[fluid remove]: remove paddle.fluid.layers.yolo_box and paddle.fluid.layers.yolov3_loss (#48722) 

* remove paddle.fluid.layers.nn.temporal_shift

* code check

* rm unittest

* remove fluid.yolo_box

* remove fluid.yolov3_loss

* change the comments of yolov3_loss to yolo_loss
上级 a5b3a65a
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Showing with 14 addition and 341 deletion
python/paddle/fluid/layers/detection.py
浏览文件 @ d3e9e73a
......@@ -52,8 +52,6 @@ __all__ = [
'iou_similarity',
'box_coder',
'polygon_box_transform',
'yolov3_loss',
'yolo_box',
'box_clip',
'multiclass_nms',
'locality_aware_nms',
......@@ -435,262 +433,6 @@ 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,
gt_label,
anchors,
anchor_mask,
class_num,
ignore_thresh,
downsample_ratio,
gt_score=None,
use_label_smooth=True,
name=None,
scale_x_y=1.0,
):
"""
${comment}
Args:
x (Variable): ${x_comment}The data type is float32 or float64.
gt_box (Variable): 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 (Variable): class id of ground truth boxes, should be in shape
of [N, B].The data type is int32.
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
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 (Variable): mixup score of ground truth boxes, should be in shape
of [N, B]. Default None.
use_label_smooth (bool): ${use_label_smooth_comment}
scale_x_y (float): ${scale_x_y_comment}
Returns:
Variable: A 1-D tensor with shape [N], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable
TypeError: Input gtlabel of yolov3_loss must be Variable
TypeError: Input gtscore of yolov3_loss must be None or Variable
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.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
gt_box = fluid.data(name='gt_box', shape=[None, 6, 4], dtype='float32')
gt_label = fluid.data(name='gt_label', shape=[None, 6], dtype='int32')
gt_score = fluid.data(name='gt_score', shape=[None, 6], dtype='float32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label,
gt_score=gt_score, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gt_box, Variable):
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gt_label, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if gt_score is not None and not isinstance(gt_score, Variable):
raise TypeError("Input gtscore of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
if not isinstance(anchor_mask, list) and not isinstance(anchor_mask, tuple):
raise TypeError("Attr anchor_mask of yolov3_loss must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
raise TypeError(
"Attr ignore_thresh of yolov3_loss must be a float number"
)
if not isinstance(use_label_smooth, bool):
raise TypeError(
"Attr use_label_smooth of yolov3_loss must be a bool value"
)
if _non_static_mode():
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,
)
loss, _, _ = _legacy_C_ops.yolov3_loss(
x, gt_box, gt_label, gt_score, *attrs
)
return loss
helper = LayerHelper('yolov3_loss', **locals())
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
@deprecated(since="2.0.0", update_to="paddle.vision.ops.yolo_box")
@templatedoc(op_type="yolo_box")
def yolo_box(
x,
img_size,
anchors,
class_num,
conf_thresh,
downsample_ratio,
clip_bbox=True,
name=None,
scale_x_y=1.0,
iou_aware=False,
iou_aware_factor=0.5,
):
"""
${comment}
Args:
x (Variable): ${x_comment} The data type is float32 or float64.
img_size (Variable): ${img_size_comment} The data type is int32.
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
conf_thresh (float): ${conf_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
clip_bbox (bool): ${clip_bbox_comment}
scale_x_y (float): ${scale_x_y_comment}
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`
iou_aware (bool): ${iou_aware_comment}
iou_aware_factor (float): ${iou_aware_factor_comment}
Returns:
Variable: 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 Variable
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.fluid as fluid
import paddle
paddle.enable_static()
x = fluid.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
img_size = fluid.data(name='img_size',shape=[None, 2],dtype='int64')
anchors = [10, 13, 16, 30, 33, 23]
boxes,scores = fluid.layers.yolo_box(x=x, img_size=img_size, class_num=80, anchors=anchors,
conf_thresh=0.01, downsample_ratio=32)
"""
helper = LayerHelper('yolo_box', **locals())
if not isinstance(x, Variable):
raise TypeError("Input x of yolo_box must be Variable")
if not isinstance(img_size, Variable):
raise TypeError("Input img_size of yolo_box must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolo_box must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolo_box must be an integer")
if not isinstance(conf_thresh, float):
raise TypeError("Attr ignore_thresh of yolo_box must be a float number")
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,
"iou_aware": iou_aware,
"iou_aware_factor": iou_aware_factor,
}
helper.append_op(
type='yolo_box',
inputs={
"X": x,
"ImgSize": img_size,
},
outputs={
'Boxes': boxes,
'Scores': scores,
},
attrs=attrs,
)
return boxes, scores
@templatedoc()
def detection_map(
detect_res,
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ d3e9e73a
......@@ -528,75 +528,6 @@ class TestGenerateProposals(LayerTest):
np.testing.assert_array_equal(np.array(rois_num_stat), rois_num_dy)
class TestYoloDetection(unittest.TestCase):
def test_yolov3_loss(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
loss = layers.yolov3_loss(
x,
gt_box,
gt_label,
[10, 13, 30, 13],
[0, 1],
10,
0.7,
32,
gt_score=gt_score,
use_label_smooth=False,
)
self.assertIsNotNone(loss)
def test_yolo_box(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
img_size = layers.data(name='img_size', shape=[2], dtype='int32')
boxes, scores = layers.yolo_box(
x, img_size, [10, 13, 30, 13], 10, 0.01, 32
)
self.assertIsNotNone(boxes)
self.assertIsNotNone(scores)
def test_yolov3_loss_with_scale(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
loss = layers.yolov3_loss(
x,
gt_box,
gt_label,
[10, 13, 30, 13],
[0, 1],
10,
0.7,
32,
gt_score=gt_score,
use_label_smooth=False,
scale_x_y=1.2,
)
self.assertIsNotNone(loss)
def test_yolo_box_with_scale(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
img_size = layers.data(name='img_size', shape=[2], dtype='int32')
boxes, scores = layers.yolo_box(
x, img_size, [10, 13, 30, 13], 10, 0.01, 32, scale_x_y=1.2
)
self.assertIsNotNone(boxes)
self.assertIsNotNone(scores)
class TestBoxClip(unittest.TestCase):
def test_box_clip(self):
program = Program()
......
python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py
浏览文件 @ d3e9e73a
......@@ -314,7 +314,7 @@ class YOLOv3(fluid.dygraph.Layer):
for i, out in enumerate(self.outputs):
anchor_mask = cfg.anchor_masks[i]
if self.is_train:
loss = fluid.layers.yolov3_loss(
loss = paddle.vision.ops.yolo_loss(
x=out,
gt_box=self.gtbox,
gt_label=self.gtlabel,
......@@ -333,7 +333,7 @@ class YOLOv3(fluid.dygraph.Layer):
for m in anchor_mask:
mask_anchors.append(cfg.anchors[2 * m])
mask_anchors.append(cfg.anchors[2 * m + 1])
boxes, scores = fluid.layers.yolo_box(
boxes, scores = paddle.vision.ops.yolo_box(
x=out,
img_size=self.im_shape,
anchors=mask_anchors,
......
python/paddle/fluid/tests/unittests/ipu/test_yolo_box_op_ipu.py
浏览文件 @ d3e9e73a
......@@ -65,7 +65,7 @@ class TestBase(IPUOpTest):
'value': 6,
}
img_size = paddle.fluid.layers.fill_constant(**attrs)
out = paddle.fluid.layers.yolo_box(x=x, img_size=img_size, **self.attrs)
out = paddle.vision.ops.yolo_box(x=x, img_size=img_size, **self.attrs)
self.fetch_list = [x.name for x in out]
def run_model(self, exec_mode):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_yolo_box_op.py
浏览文件 @ d3e9e73a
......@@ -17,8 +17,8 @@ import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -56,7 +56,7 @@ class TRTYoloBoxTest(InferencePassTest):
self.downsample_ratio = 32
def append_yolobox(self, image, image_size):
return fluid.layers.yolo_box(
return paddle.vision.ops.yolo_box(
x=image,
img_size=image_size,
class_num=self.class_num,
......@@ -66,7 +66,7 @@ class TRTYoloBoxTest(InferencePassTest):
)
def test_check_output(self):
if core.is_compiled_with_cuda():
if paddle.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
......@@ -106,7 +106,7 @@ class TRTYoloBoxFP16Test(InferencePassTest):
self.downsample_ratio = 32
def append_yolobox(self, image, image_size):
return fluid.layers.yolo_box(
return paddle.vision.ops.yolo_box(
x=image,
img_size=image_size,
class_num=self.class_num,
......@@ -116,7 +116,7 @@ class TRTYoloBoxFP16Test(InferencePassTest):
)
def test_check_output(self):
if core.is_compiled_with_cuda():
if paddle.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True, rtol=1e-1)
self.assertTrue(
......@@ -160,7 +160,7 @@ class TRTYoloBoxIoUAwareTest(InferencePassTest):
self.iou_aware_factor = 0.5
def append_yolobox(self, image, image_size):
return fluid.layers.yolo_box(
return paddle.vision.ops.yolo_box(
x=image,
img_size=image_size,
class_num=self.class_num,
......@@ -172,7 +172,7 @@ class TRTYoloBoxIoUAwareTest(InferencePassTest):
)
def test_check_output(self):
if core.is_compiled_with_cuda():
if paddle.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
......
python/paddle/fluid/tests/unittests/test_device_guard.py
浏览文件 @ d3e9e73a
......@@ -127,8 +127,8 @@ class TestDeviceGuard(unittest.TestCase):
]
anchor_mask = [0, 1, 2]
with paddle.static.device_guard("gpu"):
# yolov3_loss only has cpu kernel, so its cpu kernel will be executed
loss = fluid.layers.yolov3_loss(
# yolo_loss only has cpu kernel, so its cpu kernel will be executed
loss = paddle.vision.ops.yolo_loss(
x=x,
gt_box=gt_box,
gt_label=gt_label,
......
python/paddle/fluid/tests/unittests/xpu/test_device_guard_xpu.py
浏览文件 @ d3e9e73a
......@@ -133,8 +133,8 @@ class TestDeviceGuard(unittest.TestCase):
]
anchor_mask = [0, 1, 2]
with paddle.static.device_guard("xpu"):
# yolov3_loss only has cpu kernel, so its cpu kernel will be executed
loss = fluid.layers.yolov3_loss(
# yolo_loss has cpu kernel, so its cpu kernel will be executed
loss = paddle.vision.ops.yolo_loss(
x=x,
gt_box=gt_box,
gt_label=gt_label,
......
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