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/third_party/mindspore/tests/ut/python/ops/
Dtest_tensor_getitem.py54 input_np = np.arange(60).reshape(3, 4, 5)
55 input_me = Tensor(input_np, dtype=mstype.float32)
63 input_np = np.arange(60).reshape(3, 4, 5)
64 input_me = Tensor(input_np, dtype=mstype.float32)
72 input_np = np.arange(60).reshape(3, 4, 5)
73 input_me = Tensor(input_np, dtype=mstype.float32)
81 input_np = np.arange(3*4*5*6*7*8).reshape(3, 4, 5, 6, 7, 8)
82 input_me = Tensor(input_np, dtype=mstype.float32)
90 input_np = np.arange(3*4*5*6*7*8).reshape(3, 4, 5, 6, 7, 8)
91 input_me = Tensor(input_np, dtype=mstype.float32)
[all …]
/third_party/mindspore/tests/ut/python/exec/
Dtest_train.py32 def me_train_tensor(net, input_np, label_np, epoch_size=2): argument
44 _train_net(Tensor(input_np), Tensor(label_np))
78 input_np = np.ones([1, 3, 3, 3], np.float32)
80 me_train_tensor(Net(3, bias_init=bias_init), input_np, label_np)
110 input_np = np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01
112 me_train_tensor(net, input_np, label_np)
147 input_np = np.ones([32, 3, 224, 224]).astype(np.float32) * 0.01
149 me_train_tensor(net, input_np, label_np)
177 input_np = np.ones([32, 2048, 1, 1]).astype(np.float32) * 0.01
179 me_train_tensor(net, input_np, label_np)
/third_party/mindspore/tests/ut/python/pipeline/parse/
Dtest_dictionary.py68 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
69 input_me = Tensor(input_np)
76 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
77 input_me = Tensor(input_np)
83 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
84 input_me = Tensor(input_np)
Dtest_list.py37 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
38 input_me = Tensor(input_np)
44 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
45 input_me = Tensor(input_np)
Dtest_for_stmt.py59 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
60 input_me = Tensor(input_np)
90 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
91 input_me = Tensor(input_np)
124 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
125 input_me = Tensor(input_np)
Dtest_fix_bug.py59 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
60 input_me = Tensor(input_np)
94 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
95 input_me = Tensor(input_np)
Dtest_cont_break.py28 input_np = np.random.randn(2, 3).astype(np.float32)
29 input_ms = Tensor(input_np)
30 output_np = net.construct(input_np) # run python
Dtest_celllist.py44 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
45 input_me = Tensor(input_np)
/third_party/mindspore/tests/st/fusion/
Dtest_conv_bn1_fusion.py41 def me_train_tensor(net, input_np, label_np, epoch_size=2): argument
52 output = _train_net(Tensor(input_np), Tensor(label_np))
82 input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
84 me_train_tensor(net, input_np, label_np)
109 input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
111 me_train_tensor(net, input_np, label_np)
134 input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
136 me_train_tensor(net, input_np, label_np)
/third_party/mindspore/tests/st/pynative/
Dtest_tensor_getitem.py58 input_np = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32)
59 input_0 = Tensor(input_np)
61 assert np.all(output0.asnumpy() == input_np[3:4:1, 1:5:2, 3:6:1] + np.ones([1, 2, 3]))
62 assert np.all(output1.asnumpy() == input_np[-6:4:1, 0:8:1, ::1] + np.ones([4, 8, 10]))
63 assert np.all(output2.asnumpy() == input_np[::, ::, ::] + np.ones([6, 8, 10]))
64 assert np.all(output3.asnumpy() == input_np[::2] + np.ones([3, 8, 10]))
89 input_np = np.arange(6*7*8*9).reshape(6, 7, 8, 9).astype(np.int32)
90 input_0 = Tensor(input_np)
92 assert np.all(output0.asnumpy() == input_np[0:4:2, ..., 1] + np.ones([2, 7, 8]))
93 assert np.all(output1.asnumpy() == input_np[...] + np.ones([6, 7, 8, 9]))
[all …]
Dtest_pynative_hook_grad.py229 input_np = np.ones([1, 1, 224, 224]).astype(np.float32)
234 input_ms = Tensor(input_np)
241 input_np = np.ones([2, 2]).astype(np.float32)
246 input_ms = Tensor(input_np)
262 input_np = np.ones([2, 2]).astype(np.float32)
269 input_ms = Tensor(input_np)
290 input_np = np.ones([2, 2]).astype(np.float32)
295 input_ms = Tensor(input_np)
326 input_np = np.ones([2, 2]).astype(np.float32)
331 input_ms = Tensor(input_np)
[all …]
Dtest_pynative_layernorm_input_and_argmaxwithvalue.py98 self.input_np = np.random.randn(*input_shape).astype(dtype=dtype)
109 input_ms = Tensor(self.input_np)
119 input_nn = Tensor(self.input_np)
165 self.input_np = np.random.rand(*input_shape).astype(dtype)
171 input_forward = Tensor(self.input_np)
177 index = np.argmax(self.input_np, axis=self.axis)
178 value = np.amax(self.input_np, axis=self.axis, keepdims=self.keep_dims)
182 input_back = Tensor(self.input_np)
Dtest_ops.py26 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
27 input_x = Tensor(input_np)
/third_party/mindspore/tests/st/ops/ascend/test_aicpu_ops/
Dtest_tensor_setitem.py29 input_np = np.zeros(input_shape, dtype)
31 input_tensor = Tensor(input_np)
35 input_np[slice_tuple] = update_np
36 assert (output.asnumpy() == input_np).all()
Dtest_tensor_copy_slices.py52 input_np = np.zeros(input_shape, dtype)
54 input_tensor = Tensor(input_np)
59 input_np[slices] = update_np
60 assert (output.asnumpy() == input_np).all()
/third_party/mindspore/tests/st/ops/ascend/test_tbe_ops/
Dtest_gelu.py36 input_np = np.random.randn(*input_shape).astype(data_type)
37 input_me = Tensor(input_np)
43 input_np = np.random.randn()
44 input_me = Tensor(input_np)
Dtest_pow.py46 input_np = np.absolute(np.random.randn())
48 input_np = np.absolute(np.random.randn(*input_shape).astype(np.float32))
49 input_me = Tensor(input_np, dtype=ms.float32)
Dtest_tanh.py36 input_np = np.random.randn(*input_shape).astype(np.float32) variable
37 input_me = Tensor(input_np)
Dtest_tanh_grad.py36 input_np = np.random.randn(*input_shape).astype(np.float32) variable
37 input_me = Tensor(input_np)
/third_party/mindspore/tests/st/tbe_networks/
Dexport_geir.py25 input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224, 224]).astype(np.float32)
27 export(net, Tensor(input_np), file_name="./me_resnet50.pb", file_format="AIR")
/third_party/mindspore/tests/ut/python/parallel/
Dtest_auto_parallel_resnet.py295 input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32)
299 dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
671 input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32)
675 dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
699 input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32)
703 dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
725 input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32)
729 dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
751 input_np = np.ones([batch_size, 3, 224, 224]).astype(np.float32)
755 dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
Dtest_auto_parallel_resnet_predict.py35 input_np = Tensor(np.ones([batch_size, 3, 224, 224]).astype(np.float32))
38 model.predict(input_np)
/third_party/mindspore/tests/st/control/
Dtest_if_by_if.py64 input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
66 out_ms = net(Tensor(input_np))
67 out_np = input_np * 4
Dtest_cont_break.py28 input_np = np.random.randn(2, 3).astype(np.float32)
29 input_ms = Tensor(input_np)
30 output_np = net.construct(input_np) # run python
/third_party/mindspore/tests/st/ops/gpu/
Dtest_lstm_op.py41 …input_np = np.array([[[0.6755, -1.6607, 0.1367, -0.9209, -1.7088, 0.3953, 2.7120, 0.1103, 0.1504, …
57 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x')
173 …input_np = np.array([[[-1.7322, 1.6642, -1.1861, 0.2955, -0.7907, 0.2982, -1.3413, 1.0665, -0.0436…
189 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x')
330 …input_np = np.array([[[-0.1887, -0.4144, -0.0235, 0.7489, 0.7522, 0.5969, 0.3342, 1.2198, 0.6786, …
346 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x')
604 …input_np = np.array([[[-0.5907, 1.0557, 1.7283, 0.6706, -1.2550, -0.5298, -0.2290, -0.6735, 0.8555…
620 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x')
878 …input_np = np.array([[[-2.48789445e-01, -2.18991071e-01, -8.41492534e-01, -5.73351622e-01, 8.20644…
904 … self.x = Parameter(initializer(Tensor(input_np), [seq_len, batch_size, input_size]), name='x')

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