def test_ModulatedDeformableConvolution():
"""test of the deformable convolution layer with possible combinations of arguments,
currently this layer only supports gpu
"""
from mxnet.gluon.contrib.cnn import DeformableConvolution
net = nn.HybridSequential()
net.add(
DeformableConvolution(10, kernel_size=(3, 3), strides=1, padding=0),
DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
offset_use_bias=False, use_bias=False),
DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
offset_use_bias=False),
DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, activation='relu',
use_bias=False),
DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False, use_bias=False),
DeformableConvolution(10, kernel_size=(3, 2), strides=1, padding=0, offset_use_bias=False),
DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False),
DeformableConvolution(12, kernel_size=(3, 2), strides=1, padding=0, use_bias=False, num_deformable_group=4),
)
ctx = mx.cpu()
net.initialize(force_reinit=True, ctx=ctx)
net.hybridize()
x = mx.nd.random.uniform(shape=(8, 5, 30, 31), ctx=ctx)
with mx.autograd.record():
y = net(x)
def test_DeformableConvolution():
"""test of the deformable convolution layer with possible combinations of arguments,
currently this layer only supports gpu
"""
net = nn.HybridSequential()
net.add(
DeformableConvolution(10, kernel_size=(3, 3), strides=1, padding=0),
DeformableConvolution(10,
kernel_size=(3, 2),
strides=1,
padding=0,
activation='relu',
offset_use_bias=False,
use_bias=False),
DeformableConvolution(10,
kernel_size=(3, 2),
strides=1,
padding=0,
activation='relu',
offset_use_bias=False),
DeformableConvolution(10,
kernel_size=(3, 2),
strides=1,
padding=0,
activation='relu',
use_bias=False),
DeformableConvolution(10,
kernel_size=(3, 2),
strides=1,
padding=0,
offset_use_bias=False,
use_bias=False),
DeformableConvolution(10,
kernel_size=(3, 2),
strides=1,
padding=0,
offset_use_bias=False),
DeformableConvolution(12,
kernel_size=(3, 2),
strides=1,
padding=0,
use_bias=False),
DeformableConvolution(12,
kernel_size=(3, 2),
strides=1,
padding=0,
use_bias=False,
num_deformable_group=4),
)
try:
ctx = mx.gpu()
_ = mx.nd.array([0], ctx=ctx)
except mx.base.MXNetError:
print("deformable_convolution only supports GPU")
return
net.initialize(force_reinit=True, ctx=ctx)
net.hybridize()
x = mx.nd.random.uniform(shape=(8, 5, 30, 31), ctx=ctx)
with mx.autograd.record():
y = net(x)
y.backward()