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fix unittest for yolov3_loss. test=develop

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revert-15164-speed/enhance_cudnn
dengkaipeng 7 years ago
parent f0804433b0
commit 0d1a9996ac
4 changed files with 86 additions and 36 deletions
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9
paddle/fluid/operators/detection/yolov3_loss_op.cc
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@ -223,6 +223,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
$$
While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
target will be smoothed when calculating classification loss, target of
positive samples will be smoothed to $$1.0 - 1.0/class_num$$ and target of
negetive samples will be smoothed to $$1.0/class_num$$.
While :attr:`GTScore` is given, which means the mixup score of ground truth
boxes, all looses incured by a ground truth box will be multiplied by its
mixup score.
)DOC");
}
};

39
python/paddle/fluid/layers/detection.py
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@ -515,7 +515,9 @@ def yolov3_loss(x,
class_num,
ignore_thresh,
downsample_ratio,
name=None):
name=None,
gtscore=None,
use_label_smooth=True):
"""
${comment}
@ -534,27 +536,34 @@ def yolov3_loss(x,
ignore_thresh (float): ${ignore_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
gtscore (Variable): mixup score of ground truth boxes, shoud be in shape
of [N, B].
use_label_smooth (bool): ${use_label_smooth_comment}
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
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 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
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
gtscore = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
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, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors,
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel,
gtscore=gtscore, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
@ -566,6 +575,8 @@ def yolov3_loss(x,
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(gtscore, 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):
@ -575,6 +586,9 @@ def yolov3_loss(x,
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 name is None:
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
@ -585,21 +599,26 @@ def yolov3_loss(x,
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": gtbox,
"GTLabel": gtlabel,
}
if gtscore:
inputs["GTScore"] = gtscore
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,
}
helper.append_op(
type='yolov3_loss',
inputs={
"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel,
},
inputs=inputs,
outputs={
'Loss': loss,
'ObjectnessMask': objectness_mask,

12
python/paddle/fluid/tests/test_detection.py
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@ -476,8 +476,16 @@ class TestYoloDetection(unittest.TestCase):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13],
[0, 1], 10, 0.7, 32)
gtscore = layers.data(name='gtscore', shape=[10], dtype='int32')
loss = layers.yolov3_loss(
x,
gtbox,
gtlabel, [10, 13, 30, 13], [0, 1],
10,
0.7,
32,
gtscore=gtscore,
use_label_smooth=False)
self.assertIsNotNone(loss)

62
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
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@ -23,8 +23,8 @@ from op_test import OpTest
from paddle.fluid import core
def l2loss(x, y):
return 0.5 * (y - x) * (y - x)
def l1loss(x, y):
return abs(x - y)
def sce(x, label):
@ -66,7 +66,7 @@ def batch_xywh_box_iou(box1, box2):
return inter_area / union
def YOLOv3Loss(x, gtbox, gtlabel, attrs):
def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
n, c, h, w = x.shape
b = gtbox.shape[1]
anchors = attrs['anchors']
@ -75,21 +75,21 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
mask_num = len(anchor_mask)
class_num = attrs["class_num"]
ignore_thresh = attrs['ignore_thresh']
downsample = attrs['downsample']
input_size = downsample * h
downsample_ratio = attrs['downsample_ratio']
use_label_smooth = attrs['use_label_smooth']
input_size = downsample_ratio * h
x = x.reshape((n, mask_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
loss = np.zeros((n)).astype('float32')
label_pos = 1.0 - 1.0 / class_num if use_label_smooth else 1.0
label_neg = 1.0 / class_num if use_label_smooth else 0.0
pred_box = x[:, :, :, :, :4].copy()
grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
x[:, :, :, :, 5:] = np.where(x[:, :, :, :, 5:] < -0.5, x[:, :, :, :, 5:],
np.ones_like(x[:, :, :, :, 5:]) * 1.0 /
class_num)
mask_anchors = []
for m in anchor_mask:
mask_anchors.append((anchors[2 * m], anchors[2 * m + 1]))
@ -138,21 +138,22 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
ty = gtbox[i, j, 1] * w - gj
tw = np.log(gtbox[i, j, 2] * input_size / mask_anchors[an_idx][0])
th = np.log(gtbox[i, j, 3] * input_size / mask_anchors[an_idx][1])
scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3])
scale = (2.0 - gtbox[i, j, 2] * gtbox[i, j, 3]) * gtscore[i, j]
loss[i] += sce(x[i, an_idx, gj, gi, 0], tx) * scale
loss[i] += sce(x[i, an_idx, gj, gi, 1], ty) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 2], tw) * scale
loss[i] += l2loss(x[i, an_idx, gj, gi, 3], th) * scale
loss[i] += l1loss(x[i, an_idx, gj, gi, 2], tw) * scale
loss[i] += l1loss(x[i, an_idx, gj, gi, 3], th) * scale
objness[i, an_idx * h * w + gj * w + gi] = 1.0
objness[i, an_idx * h * w + gj * w + gi] = gtscore[i, j]
for label_idx in range(class_num):
loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx],
float(label_idx == gtlabel[i, j]))
loss[i] += sce(x[i, an_idx, gj, gi, 5 + label_idx], label_pos
if label_idx == gtlabel[i, j] else
label_neg) * gtscore[i, j]
for j in range(mask_num * h * w):
if objness[i, j] > 0:
loss[i] += sce(pred_obj[i, j], 1.0)
loss[i] += sce(pred_obj[i, j], 1.0) * objness[i, j]
elif objness[i, j] == 0:
loss[i] += sce(pred_obj[i, j], 0.0)
@ -167,6 +168,7 @@ class TestYolov3LossOp(OpTest):
x = logit(np.random.uniform(0, 1, self.x_shape).astype('float32'))
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num, self.gtbox_shape[:2])
gtscore = np.random.random(self.gtbox_shape[:2]).astype('float32')
gtmask = np.random.randint(0, 2, self.gtbox_shape[:2])
gtbox = gtbox * gtmask[:, :, np.newaxis]
gtlabel = gtlabel * gtmask
@ -176,15 +178,18 @@ class TestYolov3LossOp(OpTest):
"anchor_mask": self.anchor_mask,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"downsample": self.downsample,
"downsample_ratio": self.downsample_ratio,
"use_label_smooth": self.use_label_smooth,
}
self.inputs = {
'X': x,
'GTBox': gtbox.astype('float32'),
'GTLabel': gtlabel.astype('int32'),
'GTScore': gtscore.astype('float32')
}
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, self.attrs)
loss, objness, gt_matches = YOLOv3Loss(x, gtbox, gtlabel, gtscore,
self.attrs)
self.outputs = {
'Loss': loss,
'ObjectnessMask': objness,
@ -193,24 +198,33 @@ class TestYolov3LossOp(OpTest):
def test_check_output(self):
place = core.CPUPlace()
self.check_output_with_place(place, atol=1e-3)
self.check_output_with_place(place, atol=2e-3)
def test_check_grad_ignore_gtbox(self):
place = core.CPUPlace()
self.check_grad_with_place(
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.3)
no_grad_set=set(["GTBox", "GTLabel", "GTScore"]),
max_relative_error=0.2)
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
self.anchor_mask = [1, 2]
self.anchors = [
10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198,
373, 326
]
self.anchor_mask = [0, 1, 2]
self.class_num = 5
self.ignore_thresh = 0.5
self.downsample = 32
self.downsample_ratio = 32
self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
self.gtbox_shape = (3, 5, 4)
self.use_label_smooth = True
class TestYolov3LossWithoutLabelSmooth(TestYolov3LossOp):
def set_label_smooth(self):
self.use_label_smooth = False
if __name__ == "__main__":

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