def multi_box_head_output(self, data_shape):
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
mbox_locs, mbox_confs, box, var = layers.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
num_classes=21,
min_ratio=20,
max_ratio=90,
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300,
offset=0.5,
flip=True,
clip=True)
return mbox_locs, mbox_confs, box, var
def multi_box_head_output(self, data_shape):
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
mbox_locs, mbox_confs, box, var = layers.multi_box_head(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
num_classes=21,
min_ratio=20,
max_ratio=90,
aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
base_size=300,
offset=0.5,
flip=True,
clip=True)
return mbox_locs, mbox_confs, box, var