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fix tiny bugs for log_matrix_determinant

This commit is contained in:
z00512249 2022-08-02 16:26:08 +08:00
parent 6b752488fa
commit a4760cb7ed
2 changed files with 14 additions and 6 deletions
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12
mindspore/python/mindspore/ops/function/nn_func.py
View File

@ -827,7 +827,8 @@ def interpolate(x, roi=None, scales=None, sizes=None, coordinate_transformation_
resize_bilinear_inner = _get_cache_prim(IMG.ResizeBilinearV2)(align_corners, half_pixel_centers)
return resize_bilinear_inner(x, output_size)
raise ValueError("Input Error: For interpolate, {} mode is not support now".format(mode))
raise TypeError(
"Input Error: For interpolate, {} mode is not support now".format(mode))
def softsign(x):
@ -1688,8 +1689,8 @@ def mish(x):
>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> output = ops.mish(input_x)
>>> print(output)
[[-0.3034014 3.9974129 -0.00026832]
[ 1.9439590 -0.0033576 9.0000000]]
[[-3.0340147e-01 3.9974129e+00 -2.68311895e-03]
[ 1.9439590e+00 -3.3576239e-02 8.99999990e+00]]
"""
return mish_(x)
@ -1818,7 +1819,7 @@ def grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zero
Examples:
>>> input_x = Tensor(np.arange(16).reshape((2, 2, 2, 2)).astype(np.float32))
>>> grid = Tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(np.float32))
>>> output = ops.grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
>>> output = grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
align_corners=True)
>>> print(output)
[[[[ 1.9 ]
@ -1942,8 +1943,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
>>> inputs = Tensor(np.array([[[0.6, 0.4, 0.2], [0.8, 0.6, 0.3]],
... [[0.0, 0.6, 0.0], [0.5, 0.4, 0.5]]]), mindspore.float32)
>>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
>>> decoded_indices, decoded_values, decoded_shape, log_probability = ops.ctc_greedy_decoder(inputs,
sequence_length)
>>> decoded_indices, decoded_values, decoded_shape, log_probability = ctc_greedy_decode(inputs, sequence_length)
>>> print(decoded_indices)
[[0 0]
[0 1]

8
tests/st/ops/gpu/test_matrix_determinant_op.py
View File

@ -141,6 +141,8 @@ def test_matrix_determinant_dy_shape():
input_shape = (4, 4)
data_type = np.float32
ms_data_type = ms_type.float32
if data_type in (np.float32, np.complex64):
loss = 1e-3
input_x_np = np.random.random(input_shape).astype(data_type)
benchmark_output = matrix_determinant_scipy_benchmark(input_x_np)
@ -169,6 +171,8 @@ def test_log_matrix_determinant_dy_shape():
input_shape = (4, 4)
data_type = np.float32
ms_data_type = ms_type.float32
if data_type in (np.float32, np.complex64):
loss = 1e-3
input_x_np = np.random.random(input_shape).astype(data_type)
benchmark_output = log_matrix_determinant_np_benchmark(input_x_np)
@ -195,6 +199,8 @@ def test_matrix_determinant_vmap():
context.set_context(mode=context.GRAPH_MODE)
loss = 1e-6
data_type = np.float32
if data_type in (np.float32, np.complex64):
loss = 1e-3
# Case : in_axes input_x batch remains 0
input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))
@ -219,6 +225,8 @@ def test_log_matrix_determinant_vmap():
context.set_context(mode=context.GRAPH_MODE)
loss = 1e-6
data_type = np.float32
if data_type in (np.float32, np.complex64):
loss = 1e-3
# Case : in_axes input_x batch remains 0
input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))