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])