| /third_party/mindspore/tests/ut/python/nn/probability/distribution/ |
| D | test_categorical.py | 23 from mindspore import dtype
33 c = msd.Categorical([0.1, 0.9], dtype=dtype.int32)
39 msd.Categorical([0.1], dtype=dtype.bool_)
57 msd.Categorical([-0.1], dtype=dtype.int32)
59 msd.Categorical([1.1], dtype=dtype.int32)
61 msd.Categorical([0.0], dtype=dtype.int32)
63 msd.Categorical([1.0], dtype=dtype.int32)
71 msd.Categorical([[0.1, 0.2], [0.4, 0.6]], dtype=dtype.int32)
73 msd.Categorical([[0.5, 0.7], [0.6, 0.6]], dtype=dtype.int32)
81 msd.Categorical(0.2, dtype=dtype.int32)
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| D | test_bernoulli.py | 22 from mindspore import dtype
32 b = msd.Bernoulli([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
38 msd.Bernoulli([0.1], dtype=dtype.bool_)
56 msd.Bernoulli([-0.1], dtype=dtype.int32)
58 msd.Bernoulli([1.1], dtype=dtype.int32)
60 msd.Bernoulli([0.0], dtype=dtype.int32)
62 msd.Bernoulli([1.0], dtype=dtype.int32)
72 self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
89 value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
101 self.b = msd.Bernoulli(dtype=dtype.int32)
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| D | test_uniform.py | 23 from mindspore import dtype
32 msd.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
37 msd.Uniform(0., 1., dtype=dtype.int32)
56 u = msd.Uniform([3.0], [4.0], dtype=dtype.float32)
65 msd.Uniform(0.0, 0.0, dtype=dtype.float32)
67 msd.Uniform(1.0, 0.0, dtype=dtype.float32)
77 self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
94 value = Tensor([3.1, 3.2, 3.3, 3.4], dtype=dtype.float32)
106 self.u = msd.Uniform(dtype=dtype.float32)
123 value = Tensor([0.1, 0.2, 0.3, 0.9], dtype=dtype.float32)
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| D | test_geometric.py | 22 from mindspore import dtype
32 g = msd.Geometric([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
38 msd.Geometric([0.1], dtype=dtype.bool_)
56 msd.Geometric([-0.1], dtype=dtype.int32)
58 msd.Geometric([1.1], dtype=dtype.int32)
60 msd.Geometric([0.0], dtype=dtype.int32)
62 msd.Geometric([1.0], dtype=dtype.int32)
72 self.g = msd.Geometric(0.5, dtype=dtype.int32)
89 value = Tensor([3, 4, 5, 6, 7], dtype=dtype.float32)
101 self.g = msd.Geometric(dtype=dtype.int32)
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| D | test_utils.py | 23 from mindspore import dtype
33 tensor_fp16 = Tensor(0.1, dtype=dtype.float16)
34 tensor_fp32 = Tensor(0.1, dtype=dtype.float32)
35 tensor_fp64 = Tensor(0.1, dtype=dtype.float64)
36 tensor_int32 = Tensor(0.1, dtype=dtype.int32)
53 ans1 = set_param_type(dict1, dtype.float16)
54 assert ans1 == dtype.float32
57 set_param_type(dict2, dtype.float32)
59 ans3 = set_param_type(dict3, dtype.float16)
60 assert ans3 == dtype.float32
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| D | test_exponential.py | 22 from mindspore import dtype
32 e = msd.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
37 msd.Exponential([0.1], dtype=dtype.int32)
52 msd.Exponential([-0.1], dtype=dtype.float32)
54 msd.Exponential([0.0], dtype=dtype.float32)
62 self.e = msd.Exponential(0.5, dtype=dtype.float32)
78 value = Tensor([0.2, 0.3, 5.0, 2, 3.9], dtype=dtype.float32)
88 self.e = msd.Exponential(dtype=dtype.float32)
104 value = Tensor([0.2, 0.9, 1, 2, 3], dtype=dtype.float32)
105 rate = Tensor([0.5], dtype=dtype.float32)
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| D | test_beta.py | 23 from mindspore import dtype
31 msd.Gamma([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
35 msd.Gamma([0.], [1.], dtype=dtype.int32)
69 g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
79 self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)
91 value = Tensor([0.5, 1.0], dtype=dtype.float32)
114 value = Tensor([0.5, 1.0], dtype=dtype.float32)
115 concentration1 = Tensor([2.0, 3.0], dtype=dtype.float32)
116 concentration0 = Tensor([1.0], dtype=dtype.float32)
126 self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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| D | test_lognormal.py | 23 from mindspore import dtype
31 msd.LogNormal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
35 msd.LogNormal(0., 1., dtype=dtype.int32)
57 n = msd.LogNormal([3.0], [4.0], dtype=dtype.float32)
67 self.lognormal = msd.LogNormal(3.0, 4.0, dtype=dtype.float32)
83 value = Tensor([0.5, 1.0], dtype=dtype.float32)
110 value = Tensor([0.5, 1.0], dtype=dtype.float32)
111 mean = Tensor([0.0], dtype=dtype.float32)
112 sd = Tensor([1.0], dtype=dtype.float32)
122 self.n1 = msd.LogNormal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
[all …]
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| D | test_normal.py | 23 from mindspore import dtype
31 msd.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
35 msd.Normal(0., 1., dtype=dtype.int32)
57 n = msd.Normal([3.0], [4.0], dtype=dtype.float32)
67 self.normal = msd.Normal(3.0, 4.0, dtype=dtype.float32)
83 value = Tensor([0.5, 1.0], dtype=dtype.float32)
110 value = Tensor([0.5, 1.0], dtype=dtype.float32)
111 mean = Tensor([0.0], dtype=dtype.float32)
112 sd = Tensor([1.0], dtype=dtype.float32)
122 self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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| D | test_gamma.py | 23 from mindspore import dtype
31 msd.Gamma([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
35 msd.Gamma([0.], [1.], dtype=dtype.int32)
63 g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
73 self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)
89 value = Tensor([0.5, 1.0], dtype=dtype.float32)
116 value = Tensor([0.5, 1.0], dtype=dtype.float32)
117 concentration = Tensor([2.0, 3.0], dtype=dtype.float32)
118 rate = Tensor([1.0], dtype=dtype.float32)
128 self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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| D | test_poisson.py | 22 from mindspore import dtype
32 p = msd.Poisson([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
37 msd.Poisson([0.1], dtype=dtype.bool_)
52 msd.Poisson([-0.1], dtype=dtype.float32)
54 msd.Poisson([0.0], dtype=dtype.float32)
66 self.p = msd.Poisson([0.5, 0.5, 0.5, 0.5, 0.5], dtype=dtype.float32)
82 value = Tensor([0.2, 0.3, 5.0, 2, 3.9], dtype=dtype.float32)
92 self.p = msd.Poisson(dtype=dtype.float32)
108 value = Tensor([0.2, 0.9, 1, 2, 3], dtype=dtype.float32)
109 rate = Tensor([0.5, 0.5, 0.5, 0.5, 0.5], dtype=dtype.float32)
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| D | test_cauchy.py | 22 from mindspore import dtype
30 msd.Cauchy([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
34 msd.Cauchy(0., 1., dtype=dtype.int32)
56 l = msd.Cauchy([3.0], [4.0], dtype=dtype.float32)
66 self.cauchy = msd.Cauchy(3.0, 4.0, dtype=dtype.float32)
82 value = Tensor([0.5, 1.0], dtype=dtype.float32)
109 value = Tensor([0.5, 1.0], dtype=dtype.float32)
110 mu = Tensor([0.0], dtype=dtype.float32)
111 s = Tensor([1.0], dtype=dtype.float32)
136 mu = Tensor([0.0], dtype=dtype.float32)
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| /third_party/mindspore/tests/st/ops/gpu/ |
| D | test_random_categorical_op.py | 26 def __init__(self, num_sample, seed=0, dtype=ms.int64): argument
28 self.rc = P.RandomCategorical(dtype)
43 dtype = ms.int64
44 …expect = np.array([[4, 3, 2, 4, 4, 4, 3, 4, 1, 3], [4, 3, 2, 4, 4, 4, 3, 4, 1, 3]], dtype=np.int64)
46 random_cateogoric = RCnet(num_sample, seed, dtype)
49 assert expect.dtype == output.asnumpy().dtype
58 dtype = ms.int64
59 …expect = np.array([[4, 3, 2, 4, 4, 4, 3, 4, 1, 3], [4, 3, 2, 4, 4, 4, 3, 4, 1, 3]], dtype=np.int64)
61 random_cateogoric = RCnet(num_sample, seed, dtype)
64 assert expect.dtype == output.asnumpy().dtype
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| D | test_prelu_op.py | 24 from mindspore.common import dtype as mstype
49 assert result.dtype == expect.dtype
80 for dtype in dtypes:
81 x = Tensor(x, dtype)
82 weight = Tensor(weight, dtype)
83 expect_forward = Tensor(expect_forward, dtype)
84 expect_dx = Tensor(expect_dx, dtype)
85 expect_dw = Tensor(expect_dw, dtype)
121 for dtype in dtypes:
122 x = Tensor(x, dtype)
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| /third_party/mindspore/tests/ut/python/pynative_mode/ |
| D | test_implicit_conversion.py | 29 x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
32 ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
33 assert ret_actual.dtype == ret_expect.dtype
38 x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
41 ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
42 assert ret_actual.dtype == ret_expect.dtype
47 x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
50 ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int64))
51 assert ret_actual.dtype == ret_expect.dtype
57 y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
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| /third_party/mindspore/mindspore/numpy/ |
| D | math_ops.py | 20 from numpy import dtype as nptype
26 from ..common import dtype as mstype
69 def absolute(x, dtype=None): argument
100 original_dtype = x.dtype
101 if not _check_is_float(original_dtype) and dtype is None:
103 return _apply_tensor_op(F.absolute, x, dtype=dtype).astype(original_dtype)
104 return _apply_tensor_op(F.absolute, x, dtype=dtype)
145 def clip(x, xmin, xmax, dtype=None): argument
185 x = maximum(x, xmin, dtype=dtype)
187 x = minimum(x, xmax, dtype=dtype)
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| D | array_creations.py | 22 from ..common import dtype as mstype
53 def array(obj, dtype=None, copy=True, ndmin=0): argument
86 if dtype is not None:
87 dtype = _check_dtype(dtype)
88 res = asarray(obj, dtype)
97 elif dtype is not None and dtype != res.dtype:
98 res = res.astype(dtype)
104 def asarray_const(a, dtype=None): argument
108 if dtype is not None:
109 dtype = _check_dtype(dtype)
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| D | logic_ops.py | 19 from ..common import dtype as mstype
29 def not_equal(x1, x2, dtype=None): argument
63 return _apply_tensor_op(F.not_equal, x1, x2, dtype=dtype)
66 def less_equal(x1, x2, dtype=None): argument
96 return _apply_tensor_op(F.tensor_le, x1, x2, dtype=dtype)
99 def less(x1, x2, dtype=None): argument
128 return _apply_tensor_op(F.tensor_lt, x1, x2, dtype=dtype)
131 def greater_equal(x1, x2, dtype=None): argument
160 return _apply_tensor_op(F.tensor_ge, x1, x2, dtype=dtype)
163 def greater(x1, x2, dtype=None): argument
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| /third_party/mindspore/tests/ut/python/mindrecord/ |
| D | test_mindrecord_exception.py | 472 … = [{"filename": "001.jpg", "label": 43, "score": 0.8, "mask": np.array([3, 6, 9], dtype=np.int64),
473 "segments": np.array([[5.0, 1.6], [65.2, 8.3]], dtype=np.float32),
475 … {"filename": "002.jpg", "label": 91, "score": 5.4, "mask": np.array([1, 4, 7], dtype=np.int64),
476 "segments": np.array([[5.1, 9.1], [2.0, 65.4]], dtype=np.float32),
478 … {"filename": "003.jpg", "label": 61, "score": 6.4, "mask": np.array([7, 6, 3], dtype=np.int64),
479 "segments": np.array([[0.0, 5.6], [3.0, 16.3]], dtype=np.float32),
481 … {"filename": "004.jpg", "label": 29, "score": 8.1, "mask": np.array([2, 8, 0], dtype=np.int64),
482 "segments": np.array([[5.9, 7.2], [4.0, 89.0]], dtype=np.float32),
484 … {"filename": "005.jpg", "label": 78, "score": 7.7, "mask": np.array([3, 1, 2], dtype=np.int64),
485 "segments": np.array([[0.6, 8.1], [5.3, 49.3]], dtype=np.float32),
[all …]
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| /third_party/boost/libs/python/src/numpy/ |
| D | dtype.cpp | 14 dtype(python::detail::new_reference(reinterpret_cast<PyObject*>(PyArray_DescrFromType(code))))
19 static dtype get() { return DTYPE_FROM_CODE(NPY_INT ## bits);} \
23 static dtype get() { return DTYPE_FROM_CODE(NPY_UINT ## bits);} \
25 template BOOST_NUMPY_DECL dtype get_int_dtype<bits, false>(); \
26 template BOOST_NUMPY_DECL dtype get_int_dtype<bits, true>()
31 static dtype get() { return DTYPE_FROM_CODE(NPY_FLOAT ## bits);} \
33 template BOOST_NUMPY_DECL dtype get_float_dtype<bits>()
38 static dtype get() { return DTYPE_FROM_CODE(NPY_COMPLEX ## bits);} \
40 template BOOST_NUMPY_DECL dtype get_complex_dtype<bits>()
43 NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyArrayDescr_Type, numpy::dtype)
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| /third_party/mindspore/tests/ut/python/nn/probability/bijector/ |
| D | test_bijector.py | 22 from mindspore import dtype
31 super(MyBijector, self).__init__(name='MyBijector', dtype=None, param=param)
57 … super(MySecondBijector, self).__init__(name='MySecondBijector', dtype=dtype.float32, param=param)
84 param1_2 = Tensor(1.0, dtype=dtype.float16)
85 param2_2 = Tensor(2.0, dtype=dtype.float32)
92 param1_3 = Tensor(1.0, dtype=dtype.float32)
93 param2_3 = Tensor(2.0, dtype=dtype.float32)
109 MyBijector(Tensor([1, 2], dtype=dtype.int32), Tensor([1, 2], dtype=dtype.int32))
119 param1_2 = Tensor(1.0, dtype=dtype.float16)
120 param2_2 = Tensor(2.0, dtype=dtype.float32)
[all …]
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| /third_party/mindspore/tests/st/probability/distribution/ |
| D | test_uniform.py | 22 from mindspore import dtype
32 self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
44 x_ = Tensor(np.array([-1.0, 0.0, 0.5, 1.0, 1.5, 3.0]).astype(np.float32), dtype=dtype.float32)
55 self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
67 x_ = Tensor(np.array([0.5]).astype(np.float32), dtype=dtype.float32)
78 self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
93 output = kl(Tensor(low_b, dtype=dtype.float32), Tensor(high_b, dtype=dtype.float32))
103 self.u = msd.Uniform([0.0], [3.0], dtype=dtype.float32)
126 self.u = msd.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
138 low = Tensor([1.0], dtype=dtype.float32)
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| /third_party/mindspore/tests/ut/python/nn/ |
| D | test_transformer.py | 19 from mindspore.common import dtype
34 encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
35 encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
51 encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
52 encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
67 encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
68 encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
83 encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
84 encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
98 encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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| D | test_nn_embedding.py | 20 from mindspore.common import dtype
29 input_data = Tensor(np.ones([8, 128]), dtype.int32)
36 input_data = Tensor(np.ones([8, 128]), dtype.int32)
43 input_data = Tensor(np.ones([8, 128]), dtype.int32)
59 dtype.int64, dtype.float32, dtype.int32)
66 dtype.int16, dtype.float32, dtype.int32)
73 dtype.int16, dtype.float16, dtype.int32)
80 dtype.int16, dtype.float32, dtype.int16)
87 dtype.int16, dtype.float32, dtype.int16)
94 dtype.int16, dtype.float32, dtype.int16)
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| /third_party/boost/boost/python/numpy/ |
| D | dtype.hpp | 28 class BOOST_NUMPY_DECL dtype : public object { class
34 explicit dtype(T arg, bool align=false) : object(convert(arg, align)) {} in dtype() function in boost::python::numpy::dtype
47 template <typename T> static dtype get_builtin();
58 friend BOOST_NUMPY_DECL bool equivalent(dtype const & a, dtype const & b);
68 BOOST_PYTHON_FORWARD_OBJECT_CONSTRUCTORS(dtype, object);
72 BOOST_NUMPY_DECL bool equivalent(dtype const & a, dtype const & b);
77 template <int bits, bool isUnsigned> dtype get_int_dtype();
79 template <int bits> dtype get_float_dtype();
81 template <int bits> dtype get_complex_dtype();
88 static dtype get() { return get_int_dtype< 8*sizeof(T), boost::is_unsigned<T>::value >(); } in get()
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