def test_output_size():
# im_h * im_w must be L
x = paddle.randn(shape=[2, 6, 6], dtype="float32")
out = fold(x,
output_sizes=[6, 6],
kernel_sizes=[2, 2],
strides=[1, 1])
def test_block_h_w():
# test_block_h_w GT 0
x = paddle.randn(shape=[2, 1, 1], dtype="float32")
out = fold(x,
output_sizes=[1, 1],
kernel_sizes=[2, 2],
strides=1)
def test_strides_shape():
# strids_size must be 2
x = paddle.randn(shape=[2, 6, 6], dtype="float32")
out = fold(x,
output_sizes=[2, 3],
kernel_sizes=[2, 2],
strides=[2, 2, 3])
def test_dilations_shape():
# dialtions_size must be 2
x = paddle.randn(shape=[2, 6, 6], dtype="float32")
out = fold(x,
output_sizes=[2, 3],
kernel_sizes=[2, 2],
dilations=[2, 2, 3])
def test_padding_shape():
# padding_size must be 2 or 4
x = paddle.randn(shape=[2, 6, 6], dtype="float32")
out = fold(x,
output_sizes=[2, 3],
kernel_sizes=[2, 2],
paddings=[2, 2, 3])
def test_output_size_2():
# out_size must GT 1
x = paddle.randn(shape=[2, 6, 6], dtype="float32")
out = fold(x,
output_sizes=[0.1, 0.2],
kernel_sizes=[2, 2],
strides=[1, 1])
def test_GT_0():
x = paddle.randn(shape=[2, 1, 1], dtype="float32")
out = fold(x,
output_sizes=[0, 0],
kernel_sizes=[0, 0],
dilations=0,
paddings=[0, 0],
strides=0)
def test_input_shape():
# input_shpae must be 3-D
x = paddle.randn(shape=[2, 3, 6, 7], dtype="float32")
out = fold(x, output_sizes=[2, 3], kernel_sizes=[2, 2])
