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modify tensor

This commit is contained in:
liuyang_655 2022-04-16 05:25:11 -04:00
parent ca2e44a4b0
commit df37a71d44
4 changed files with 41 additions and 40 deletions
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4
docs/api/api_python/mindspore/mindspore.Tensor.rst
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@ -759,7 +759,7 @@ mindspore.Tensor
.. py:method:: view(*shape)
根据输入shape重新创建一个Tensor，与原Tensor数据相同。
根据输入shape重新创建一个Tensor，与原Tensor数据相同。该方法与reshape方法相同，都是依靠底层reshape算子实现的。
**参数：**
@ -767,4 +767,4 @@ mindspore.Tensor
**返回：**
Tensor，具有与输入shape相同的维度。
Tensor，具有与入参 `shape` 相同的维度。

5
mindspore/python/mindspore/common/tensor.py
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@ -608,13 +608,14 @@ class Tensor(Tensor_):
def view(self, *shape):
"""
Reshape the tensor according to the input shape.
Reshape the tensor according to the input shape. It's the same as :func:`mindspore.Tensor.reshape`,
implemented by the underlying reshape operator.
Args:
shape (Union[tuple(int), int]): Dimension of the output tensor.
Returns:
Tensor, has the same dimension as the input shape.
Tensor, which dimension is the input shape's value.
Examples:
>>> from mindspore import Tensor

6
tests/st/pynative/test_tensor_getitem.py
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@ -254,7 +254,7 @@ class TensorItemByNone(Cell):
@pytest.mark.env_onecard
def test_item_by_none():
net = TensorItemByNone()
input_1d_np = np.ndarray([1]).astype(np.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
input_3d_ms = Tensor(input_3d_np, mstype.float32)
@ -279,7 +279,7 @@ class TensorItemByItem(Cell):
@pytest.mark.env_onecard
def test_item_by_int():
net = TensorItemByItem()
input_1d_np = np.ndarray([1]).astype(np.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
@ -315,7 +315,7 @@ def test_item_by_int():
@pytest.mark.env_onecard
def test_item_by_tuple():
net = TensorItemByItem()
input_1d_np = np.ndarray([1]).astype(np.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
input_3d_ms = Tensor(input_3d_np, mstype.float32)

66
tests/st/pynative/test_tensor_setitem.py
View File

@ -13,7 +13,7 @@
# limitations under the License.
# ============================================================================
""" test_tensor_setitem """
import numpy as onp
import numpy as np
import pytest
from mindspore import Tensor, context
@ -38,7 +38,7 @@ def setup_testcase(input_np, case_fn):
out_ms = TensorSetItem()(input_ms)
out_np = NumpySetItem()(input_np)
assert onp.all(out_ms.asnumpy() == out_np)
assert np.all(out_ms.asnumpy() == out_np)
class TensorSetItemByList(Cell):
@ -63,7 +63,7 @@ class NumpySetItemByList():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_list():
x = onp.ones((2, 3, 4), dtype=onp.float32)
x = np.ones((2, 3, 4), dtype=np.float32)
def cases(x):
x[[0, 1], [1, 2], [1, 3]] = [3, 4]
@ -79,7 +79,7 @@ def test_setitem_by_list():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_with_sequence():
x = onp.ones((2, 3, 4), dtype=onp.float32)
x = np.ones((2, 3, 4), dtype=np.float32)
def cases(x):
x[...] = [3]
@ -96,7 +96,7 @@ def test_setitem_with_sequence():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_dtype():
x = onp.ones((2, 3, 4), dtype=onp.float32)
x = np.ones((2, 3, 4), dtype=np.float32)
def cases(x):
x[...] = 3
@ -113,7 +113,7 @@ def test_setitem_dtype():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_tuple_with_int():
x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
x = np.arange(24).reshape(2, 3, 4).astype(np.float32)
def cases(x):
x[..., 2, False, 1] = -1
@ -130,7 +130,7 @@ def test_setitem_by_tuple_with_int():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_tuple_with_list():
x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
x = np.arange(24).reshape(2, 3, 4).astype(np.float32)
def cases(x):
x[..., 2, False, 1] = [-1]
@ -148,7 +148,7 @@ def test_setitem_by_tuple_with_list():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_nested_unit_list():
x = onp.arange(24).reshape(2, 3, 4).astype(onp.float32)
x = np.arange(24).reshape(2, 3, 4).astype(np.float32)
def cases(x):
x[[[[0]]], True] = -1
@ -164,9 +164,9 @@ def test_setitem_by_nested_unit_list():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_with_broadcast():
x = onp.arange(2*3*4*5*6).reshape(2, 3, 4, 5, 6).astype(onp.float32)
v1 = onp.full((1, 4, 5), -1).tolist()
v2 = onp.full((4, 1, 6), -2).tolist()
x = np.arange(2*3*4*5*6).reshape(2, 3, 4, 5, 6).astype(np.float32)
v1 = np.full((1, 4, 5), -1).tolist()
v2 = np.full((4, 1, 6), -2).tolist()
def cases(x):
x[..., 4] = v1
@ -183,7 +183,7 @@ def test_setitem_with_broadcast():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_mul_by_scalar():
x = onp.ones((4, 5), dtype=onp.float32)
x = np.ones((4, 5), dtype=np.float32)
def cases(x):
x[1, :] = x[1, :]*2
@ -198,7 +198,7 @@ def test_setitem_mul_by_scalar():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_slice():
x = onp.ones((3, 4, 5), dtype=onp.float32)
x = np.ones((3, 4, 5), dtype=np.float32)
def cases(x):
x[1:2] = 2
@ -218,7 +218,7 @@ def test_setitem_by_slice():
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_by_tuple_of_slices():
x = onp.ones((3, 4, 5), dtype=onp.float32)
x = np.ones((3, 4, 5), dtype=np.float32)
def cases(x):
x[1:2, 2] = 2
@ -243,10 +243,10 @@ class TensorItemSetWithNumber(Cell):
@pytest.mark.env_onecard
def test_itemset_with_number():
net = TensorItemSetWithNumber()
input_1d_np = onp.ndarray([1]).astype(onp.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
input_3d_np = np.arange(60).reshape(3, 4, 5).astype(np.int32)
input_3d_ms = Tensor(input_3d_np, mstype.float32)
value_np_1, value_np_2 = 1, 2.0
@ -255,9 +255,9 @@ def test_itemset_with_number():
output_1d_ms_2 = net(input_1d_ms, value_np_2)
input_1d_np.itemset(value_np_1)
assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_1.asnumpy() == input_1d_np)
input_1d_np.itemset(value_np_2)
assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_2.asnumpy() == input_1d_np)
with pytest.raises(IndexError):
net(input_3d_ms, value_np_1)
@ -277,7 +277,7 @@ class TensorItemSetByItemWithNumber(Cell):
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_setitem_dim_expand():
x = onp.ones((2, 3, 4), dtype=onp.float32)
x = np.ones((2, 3, 4), dtype=np.float32)
def cases(x):
x[None, True, [1, 0], (False, True, True), [2]] = 2
x[([[0]]), ..., [[1]]] = [[[3, 3, 3]]]
@ -294,10 +294,10 @@ def test_setitem_dim_expand():
@pytest.mark.env_onecard
def test_itemset_by_number_with_number():
net = TensorItemSetByItemWithNumber()
input_1d_np = onp.ndarray([1]).astype(onp.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
input_3d_np = np.arange(60).reshape(3, 4, 5).astype(np.int32)
input_3d_ms = Tensor(input_3d_np, mstype.float32)
index_np_1, index_np_2, index_np_3, index_np_4 = 0, 30, 60, 2.0
@ -311,17 +311,17 @@ def test_itemset_by_number_with_number():
output_3d_ms_4 = net(output_3d_ms_3, index_np_2, value_np_2)
input_1d_np.itemset(index_np_1, value_np_1)
assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_1.asnumpy() == input_1d_np)
input_1d_np.itemset(index_np_1, value_np_2)
assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_2.asnumpy() == input_1d_np)
input_3d_np.itemset(index_np_1, value_np_1)
assert onp.all(output_3d_ms_1.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_1.asnumpy() == input_3d_np)
input_3d_np.itemset(index_np_1, value_np_2)
assert onp.all(output_3d_ms_2.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_2.asnumpy() == input_3d_np)
input_3d_np.itemset(index_np_2, value_np_1)
assert onp.all(output_3d_ms_3.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_3.asnumpy() == input_3d_np)
input_3d_np.itemset(index_np_2, value_np_2)
assert onp.all(output_3d_ms_4.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_4.asnumpy() == input_3d_np)
with pytest.raises(IndexError):
net(input_1d_ms, index_np_2, value_np_1)
@ -348,10 +348,10 @@ def test_itemset_by_number_with_number():
@pytest.mark.env_onecard
def test_itemset_by_tuple_with_number():
net = TensorItemSetByItemWithNumber()
input_1d_np = onp.ndarray([1]).astype(onp.float32)
input_1d_np = np.array([1]).astype(np.float32)
input_1d_ms = Tensor(input_1d_np, mstype.float32)
input_3d_np = onp.arange(60).reshape(3, 4, 5).astype(onp.int32)
input_3d_np = np.arange(60).reshape(3, 4, 5).astype(np.int32)
input_3d_ms = Tensor(input_3d_np, mstype.float32)
index_np_1, index_np_2, index_np_3, index_np_4, index_np_5 = (0,), (1, 2), (1, 1, 0), (3, 4, 5), (1, 2, 3, 4)
@ -359,19 +359,19 @@ def test_itemset_by_tuple_with_number():
output_1d_ms_1 = net(input_1d_ms, index_np_1, value_np_1)
input_1d_np.itemset(index_np_1, value_np_1)
assert onp.all(output_1d_ms_1.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_1.asnumpy() == input_1d_np)
output_1d_ms_2 = net(input_1d_ms, index_np_1, value_np_2)
input_1d_np.itemset(index_np_1, value_np_2)
assert onp.all(output_1d_ms_2.asnumpy() == input_1d_np)
assert np.all(output_1d_ms_2.asnumpy() == input_1d_np)
output_3d_ms_1 = net(input_3d_ms, index_np_3, value_np_1)
input_3d_np.itemset(index_np_3, value_np_1)
assert onp.all(output_3d_ms_1.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_1.asnumpy() == input_3d_np)
output_3d_ms_2 = net(input_3d_ms, index_np_3, value_np_2)
input_3d_np.itemset(index_np_3, value_np_2)
assert onp.all(output_3d_ms_2.asnumpy() == input_3d_np)
assert np.all(output_3d_ms_2.asnumpy() == input_3d_np)
with pytest.raises(IndexError):
net(input_1d_ms, index_np_2, value_np_1)