def _test_convtranspose1d_group_bias_false(test_case, device):
np_arr = np.array(
[[[0.38072484, -0.01421228, -0.6512485], [-0.05744093, 2.47079971, 0.17573214]]]
)
weight = np.ones((2, 1, 3))
test_out_data = np.array(
[
[
[0.38072485, 0.36651257, -0.28473592, -0.66546077, -0.6512485],
[-0.05744093, 2.4133587, 2.5890908, 2.6465318, 0.17573214],
]
]
)
test_out_grad = np.array([[[3.0, 3.0, 3.0], [3.0, 3.0, 3.0]]])
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(2, 2, 3, stride=1, groups=2, bias=False)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
def _test_convtranspose1d_bias_true(test_case, device):
np_arr = np.array([[[0.54925832, -0.64144184, 0.15213189]]])
weight = np.ones((1, 2, 3))
bias = np.array([0.16849578, 0.1509564])
test_out_data = np.array(
[
[
[0.71775407, 0.07631224, 0.22844413, -0.32081416, 0.32062766],
[0.7002147, 0.05877288, 0.21090476, -0.3383535, 0.3030883],
]
]
)
test_out_grad = np.array([[[6.0, 6.0, 6.0]]])
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(1, 2, 3, stride=1, bias=True)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f.bias = nn.Parameter(flow.Tensor(bias))
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
def _test_convtranspose1d_bias_false(test_case, device):
np_arr = np.array([[[0.35356437, -0.95761778, 0.19567713]]])
weight = np.ones((1, 2, 3))
test_out_data = np.array(
[
[
[0.35356438, -0.6040534, -0.40837622, -0.7619406, 0.19567713],
[0.35356438, -0.6040534, -0.40837622, -0.7619406, 0.19567713],
]
]
)
test_out_grad = np.array([[[6.0, 6.0, 6.0]]])
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(1, 2, 3, stride=1, bias=False)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
def _test_convtranspose1d_group_large_out_channel(test_case, device):
np_arr = np.array(
[
[
[2.00934643, 1.5782626, -1.59060988],
[-1.70463546, 1.30170714, -1.04025804],
],
[
[0.60327536, 1.26085986, -0.58499662],
[-0.48145872, -1.64391469, -0.09332249],
],
]
)
weight = np.ones((2, 3, 3))
test_out_data = np.array(
[
[
[2.0093465, 3.587609, 1.9969991, -0.01234734, -1.5906099],
[2.0093465, 3.587609, 1.9969991, -0.01234734, -1.5906099],
[2.0093465, 3.587609, 1.9969991, -0.01234734, -1.5906099],
[-1.7046355, -0.40292835, -1.4431864, 0.2614491, -1.040258],
[-1.7046355, -0.40292835, -1.4431864, 0.2614491, -1.040258],
[-1.7046355, -0.40292835, -1.4431864, 0.2614491, -1.040258],
],
[
[0.60327536, 1.8641353, 1.2791386, 0.6758632, -0.58499664],
[0.60327536, 1.8641353, 1.2791386, 0.6758632, -0.58499664],
[0.60327536, 1.8641353, 1.2791386, 0.6758632, -0.58499664],
[-0.48145872, -2.1253734, -2.2186959, -1.7372372, -0.09332249],
[-0.48145872, -2.1253734, -2.2186959, -1.7372372, -0.09332249],
[-0.48145872, -2.1253734, -2.2186959, -1.7372372, -0.09332249],
],
]
)
test_out_grad = np.array(
[[[9.0, 9.0, 9.0], [9.0, 9.0, 9.0]], [[9.0, 9.0, 9.0], [9.0, 9.0, 9.0]]]
)
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(2, 6, 3, stride=1, groups=2, bias=False)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
def _test_convtranspose1d_group_large_in_channel(test_case, device):
np_arr = np.array(
[
[
[-0.3939792, -0.34989742, 0.15775536],
[0.927185, 0.25040535, -1.22738067],
[-0.2187831, -0.24346108, -0.07109655],
[-1.55353756, -0.37241986, 0.59579139],
],
[
[-0.01818884, -1.34408642, 1.31260516],
[0.52124192, 0.52142919, 1.40499944],
[0.7410308, 1.93069512, 0.25694943],
[-0.30531658, 0.24990326, -0.9493729],
],
]
)
weight = np.ones((4, 1, 3))
test_out_data = np.array(
[
[
[0.5332058, 0.43371373, -0.6359115, -1.1691173, -1.0696253],
[-1.7723207, -2.3882017, -1.8635068, -0.09118611, 0.52469486],
],
[
[0.50305307, -0.31960416, 2.3980005, 1.8949474, 2.7176046],
[0.43571424, 2.6163127, 1.9238893, 1.488175, -0.69242346],
],
]
)
test_out_grad = np.array(
[
[[3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [3.0, 3.0, 3.0]],
[[3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [3.0, 3.0, 3.0], [3.0, 3.0, 3.0]],
]
)
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(4, 2, 3, stride=1, groups=2, bias=False)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
def _test_convtranspose1d_group_bias_true(test_case, device):
np_arr = np.array(
[
[
[-0.77808793, 0.99824008, 0.57340066],
[1.46278707, -0.65234252, -1.13087643],
],
[
[0.76053973, 0.62332447, -1.17157106],
[0.60291466, -0.0472167, 0.89986403],
],
]
)
weight = np.ones((2, 1, 3))
bias = np.array([0.32546719, 0.14995032])
test_out_data = np.array(
[
[
[-0.45262071, 0.54561937, 1.11902, 1.897108, 0.89886785],
[1.6127374, 0.96039486, -0.1704815, -1.6332686, -0.9809261],
],
[
[1.0860069, 1.7093314, 0.5377604, -0.22277936, -0.8461038],
[0.75286496, 0.70564824, 1.6055121, 1.0025976, 1.0498143],
],
]
)
test_out_grad = np.array(
[[[3.0, 3.0, 3.0], [3.0, 3.0, 3.0]], [[3.0, 3.0, 3.0], [3.0, 3.0, 3.0]]]
)
input_flow = flow.tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
m_f = nn.ConvTranspose1d(2, 2, 3, stride=1, groups=2, bias=True)
m_f.weight.data = flow.tensor(weight, dtype=flow.float32)
m_f.bias = nn.Parameter(flow.Tensor(bias))
m_f = m_f.to(device)
out_flow = m_f(input_flow)
test_case.assertTrue(np.allclose(out_flow.numpy(), test_out_data, 1e-06, 1e-06))
out_flow = out_flow.sum()
out_flow.backward()
test_case.assertTrue(
np.allclose(input_flow.grad.numpy(), test_out_grad, 1e-06, 1e-06)
)
