def dygraph_case_dcn(self):
paddle.disable_static()
x = paddle.to_tensor(self.input)
offset = paddle.to_tensor(self.offset)
mask = paddle.to_tensor(self.mask)
bias = None if self.no_bias else paddle.to_tensor(self.bias)
deform_conv2d = paddle.vision.ops.DeformConv2D(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
deformable_groups=self.deformable_groups,
groups=self.groups,
weight_attr=I.Assign(self.weight),
bias_attr=False if self.no_bias else I.Assign(self.bias))
y_v1 = deform_conv2d(x, offset)
y_v2 = deform_conv2d(x, offset, mask)
out_v1 = y_v1.numpy()
out_v2 = y_v2.numpy()
return out_v1, out_v2
def static_graph_case_dcn(self):
main = paddle.static.Program()
start = paddle.static.Program()
paddle.enable_static()
with paddle.static.program_guard(main, start):
x = paddle.static.data("input", (-1, self.in_channels, -1, -1),
dtype=self.dtype)
offset = paddle.static.data(
"offset",
(-1, self.deformable_groups * 2 * self.filter_shape[0] *
self.filter_shape[1], -1, -1),
dtype=self.dtype)
mask = paddle.static.data(
"mask", (-1, self.deformable_groups * self.filter_shape[0] *
self.filter_shape[1], -1, -1),
dtype=self.dtype)
y_v1 = paddle.fluid.layers.deformable_conv(
input=x,
offset=offset,
mask=None,
num_filters=self.out_channels,
filter_size=self.filter_shape,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=self.groups,
deformable_groups=self.deformable_groups,
im2col_step=1,
param_attr=I.Assign(self.weight),
bias_attr=False if self.no_bias else I.Assign(self.bias),
modulated=False)
y_v2 = paddle.fluid.layers.deformable_conv(
input=x,
offset=offset,
mask=mask,
num_filters=self.out_channels,
filter_size=self.filter_shape,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=self.groups,
deformable_groups=self.deformable_groups,
im2col_step=1,
param_attr=I.Assign(self.weight),
bias_attr=False if self.no_bias else I.Assign(self.bias))
exe = paddle.static.Executor(self.place)
exe.run(start)
out_v1, out_v2 = exe.run(main,
feed={
"input": self.input,
"offset": self.offset,
"mask": self.mask
},
fetch_list=[y_v1, y_v2])
return out_v1, out_v2
def test_assign_initializer(self, dtype="float32"):
"""Test the numpy array initializer with supplied arguments
"""
paddle.enable_static()
import numpy
program = framework.Program()
block = program.global_block()
np_array = numpy.random.random((10000)).astype(dtype)
for _ in range(2):
block.create_parameter(
dtype=np_array.dtype,
shape=np_array.shape,
lod_level=0,
name="param",
initializer=initializer.Assign(np_array))
num_ops = 2 if dtype == "float16" else 1
self.assertEqual(len(block.ops), num_ops)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'assign_value')
assert (init_op.attr('fp32_values') == np_array).all()
paddle.disable_static()
return block