def test_yolov3_loss(self):
program = Program()
with program_guard(program):
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)
self.assertIsNotNone(loss)
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 build(self,
boxNum=64,
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
regularization=None,
lazy_mode=False):
dataInput = pfl.data(name='data_input',
shape=[3, 416, 416],
dtype='float32')
gtbox = pfl.data(name='data_gtbox', shape=[boxNum, 4], dtype='float32')
gtlabel = pfl.data(name='data_gtlabel', shape=[boxNum], dtype='int32')
anchors = [10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319]
layer0_output = _DBL(input=dataInput,
num_filters=16,
filter_size=3,
name='layer0')
layer1_output = pfl.pool2d(input=layer0_output,
pool_size=2,
pool_type='max',
pool_stride=2,
name='layer1_max')
layer2_output = _DBL(input=layer1_output,
num_filters=32,
filter_size=3,
name='layer2')
layer3_output = pfl.pool2d(input=layer2_output,
pool_size=2,
pool_type='max',
pool_stride=2,
name='layer3_max')
layer4_output = _DBL(input=layer3_output,
num_filters=64,
filter_size=3,
name='layer4')
layer5_output = pfl.pool2d(input=layer4_output,
pool_size=2,
pool_type='max',
pool_stride=2,
name='layer5_max')
layer6_output = _DBL(input=layer5_output,
num_filters=128,
filter_size=3,
name='layer6')
layer7_output = pfl.pool2d(input=layer6_output,
pool_size=2,
pool_type='max',
pool_stride=2,
name='layer7_max')
layer8_output = _DBL(input=layer7_output,
num_filters=256,
filter_size=3,
name='layer8')
layer9_output = pfl.pool2d(input=layer8_output,
pool_size=2,
pool_type='max',
pool_stride=2,
name='layer9_max')
layer10_output = _DBL(input=layer9_output,
num_filters=512,
filter_size=3,
name='layer10')
layer11_output = pfl.pool2d(input=pfl.pad(
layer10_output, paddings=[0, 0, 0, 0, 0, 1, 0, 1]),
pool_size=2,
pool_type='max',
pool_stride=1,
name='layer11_max')
layer12_output = _DBL(input=layer11_output,
num_filters=1024,
filter_size=3,
name='layer12')
layer13_output = _DBL(input=layer12_output,
num_filters=256,
filter_size=1,
padding=0,
name='layer13')
layer14_output = _DBL(input=layer13_output,
num_filters=512,
filter_size=3,
name='layer14')
layer15_output = pfl.conv2d(input=layer14_output,
num_filters=18,
filter_size=1,
name='layer15_conv')
# layer16_yolo -> -1 x 18 x 13 x 13
yolo1_loss = pfl.yolov3_loss(name='yolo1_loss',
x=layer15_output,
gtbox=gtbox,
gtlabel=gtlabel,
anchors=anchors,
anchor_mask=[3, 4, 5],
class_num=1,
ignore_thresh=0.5,
downsample_ratio=32)
# layer17_route_13
layer18_output = _DBL(input=layer13_output,
num_filters=128,
filter_size=1,
padding=0,
name='layer18')
layer19_output = pfl.expand(layer18_output,
expand_times=[1, 1, 2, 2],
name='layer19_upsample')
# layer20_route_19_8
layer20_output = pfl.concat([layer19_output, layer8_output],
axis=1,
name='layer20_concat')
layer21_output = _DBL(layer20_output,
num_filters=256,
filter_size=3,
name='layer21')
layer22_output = pfl.conv2d(input=layer21_output,
num_filters=18,
filter_size=1,
name='layer22_conv')
# layer23_yolo -> -1 x 18 x 26 x 26
yolo2_loss = pfl.yolov3_loss(name='yolo2_loss',
x=layer22_output,
gtbox=gtbox,
gtlabel=gtlabel,
anchors=anchors,
anchor_mask=[0, 1, 2],
class_num=1,
ignore_thresh=0.5,
downsample_ratio=16)
loss = pfl.reduce_mean(pfl.elementwise_add(yolo1_loss, yolo2_loss),
name="loss_output")
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=learning_rate,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
regularization=regularization,
lazy_mode=lazy_mode)
optimizer.minimize(loss)
self._netOutput1, self._netOutput2 = layer15_output, layer22_output
self._loss = loss
self._trainExe = fluid.Executor(
fluid.CUDAPlace(0)) if self._USE_CUDA else fluid.Executor(
fluid.CPUPlace())