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!10067 fix bugs of op AvgPool2d, Conv2dBnFoldQuantOneConv, ApplyAdadelta, ApplyAdagradV2, ApplyPowerSign and so on 

From: @lihongkang1
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui
tags/v1.1.0
mindspore-ci-bot Gitee 5 years ago
parent
db1f2b0151 bc4efbc9d3
commit
a350613b72
6 changed files with 143 additions and 163 deletions
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  1. +2
    -0
      mindspore/nn/layer/normalization.py
  2. +4
    -10
      mindspore/nn/layer/pooling.py
  3. +1
    -1
      mindspore/nn/layer/quant.py
  4. +2
    -1
      mindspore/nn/loss/loss.py
  5. +132
    -146
      mindspore/ops/operations/nn_ops.py
  6. +2
    -5
      mindspore/ops/operations/other_ops.py

+ 2
- 0
mindspore/nn/layer/normalization.py View File

@@ -383,8 +383,10 @@ class BatchNorm2d(_BatchNorm):
>>> print(output)
[[[[171.99915 46.999763 ]
[116.99941 191.99904 ]]

[[ 66.999664 250.99875 ]
[194.99902 102.99948 ]]

[[ 8.999955 210.99895 ]
[ 20.999895 241.9988 ]]]]
"""


+ 4
- 10
mindspore/nn/layer/pooling.py View File

@@ -111,11 +111,8 @@ class MaxPool2d(_PoolNd):
>>> pool = nn.MaxPool2d(kernel_size=3, stride=1)
>>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
>>> output = pool(x)
>>> print(output)
[[[[7. 8.]
[9. 9.]]
[[7. 8.]
[8. 8.]]]]
>>> print(output.shape)
(1, 2, 2, 2)
"""

def __init__(self, kernel_size=1, stride=1, pad_mode="valid", data_format="NCHW"):
@@ -256,11 +253,8 @@ class AvgPool2d(_PoolNd):
>>> pool = nn.AvgPool2d(kernel_size=3, stride=1)
>>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
>>> output = pool(x)
>>> print(output)
[[[[4.888889 4.4444447]
[4.111111 3.4444444]]
[[4.2222223 4.5555553]
[3.2222223 4.5555553]]]]
>>> print(output.shape)
(1, 2, 2, 2)
"""

def __init__(self,


+ 1
- 1
mindspore/nn/layer/quant.py View File

@@ -375,7 +375,7 @@ class Conv2dBnFoldQuantOneConv(Cell):
Examples:
>>> qconfig = compression.quant.create_quant_config()
>>> conv2d_bnfold = nn.Conv2dBnFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
>>> quant_config=qconfig)
... quant_config=qconfig)
>>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
>>> result = conv2d_bnfold(input)
>>> output = result.shape


+ 2
- 1
mindspore/nn/loss/loss.py View File

@@ -249,12 +249,13 @@ class SoftmaxCrossEntropyWithLogits(_Loss):

Examples:
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> np.random.seed(0)
>>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32)
>>> labels_np = np.ones([1,]).astype(np.int32)
>>> labels = Tensor(labels_np)
>>> output = loss(logits, labels)
>>> print(output)
[5.6924148]
[7.868383]
"""
def __init__(self,
sparse=False,


+ 132
- 146
mindspore/ops/operations/nn_ops.py View File

@@ -877,8 +877,9 @@ class BNTrainingReduce(PrimitiveWithInfer):
>>> bn_training_reduce = ops.BNTrainingReduce()
>>> output = bn_training_reduce(input_x)
>>> print(output)
([1.22880000e+04, 1.22880000e+04, 1.22880000e+04],
[1.22880000e+04, 1.22880000e+04, 1.22880000e+04])
(Tensor(shape=[3], dtype=Float32, value=
[ 1.22880000e+04, 1.22880000e+04, 1.22880000e+04]), Tensor(shape=[3], dtype=Float32, value=
[ 1.22880000e+04, 1.22880000e+04, 1.22880000e+04]))
"""

@prim_attr_register
@@ -943,20 +944,21 @@ class BNTrainingUpdate(PrimitiveWithInfer):
>>> bn_training_update = ops.BNTrainingUpdate()
>>> output = bn_training_update(input_x, sum, square_sum, scale, offset, mean, variance)
>>> print(output)
([[[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]],
[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]]]],
[[[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]],
[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]]]],
[[[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]],
[[2.73200464e+00, 2.73200464e+00],
[2.73200464e+00, 2.73200464e+00]]]],
[2.50000000e-01, 2.50000000e-01],
[1.87500000e-01, 1.87500000e-01])
(Tensor(shape=[1, 2, 2, 2], dtype=Float32, value=
[[[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]],
[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]]]]), Tensor(shape=[1, 2, 2, 2], dtype=Float32, value=
[[[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]],
[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]]]]), Tensor(shape=[1, 2, 2, 2], dtype=Float32, value=
[[[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]],
[[ 2.73200464e+00, 2.73200464e+00],
[ 2.73200464e+00, 2.73200464e+00]]]]), Tensor(shape=[2], dtype=Float32, value=
[ 2.50000000e-01, 2.50000000e-01]), Tensor(shape=[2], dtype=Float32, value=
[ 1.87500000e-01, 1.87500000e-01]))
"""

@prim_attr_register
@@ -1044,12 +1046,13 @@ class BatchNorm(PrimitiveWithInfer):
>>> batch_norm = ops.BatchNorm()
>>> output = batch_norm(input_x, scale, bias, mean, variance)
>>> print(output)
([[1.0, 1.0],
[1.0, 1.0]],
[1.0, 1.0],
[1.0, 1.0],
[1.0, 1.0],
[1.0, 1.0])
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.00000000e+00, 1.00000000e+00],
[ 1.00000000e+00, 1.00000000e+00]]), Tensor(shape=[2], dtype=Float32, value=
[ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[2], dtype=Float32, value=
[ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[2], dtype=Float32, value=
[ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[2], dtype=Float32, value=
[ 1.00000000e+00, 1.00000000e+00]))
"""

@prim_attr_register
@@ -1822,9 +1825,8 @@ class BiasAdd(PrimitiveWithInfer):
>>> bias = Tensor(np.random.random(3).reshape((3,)), mindspore.float32)
>>> bias_add = ops.BiasAdd()
>>> output = bias_add(input_x, bias)
>>> print(output)
[[0.4662124 1.2493685 2.3611782]
[3.4662123 4.2493687 5.3611784]]
>>> print(output.shape)
(2, 3)
"""

@prim_attr_register
@@ -2241,15 +2243,10 @@ class RNNTLoss(PrimitiveWithInfer):
>>> label_length = np.array([len(l) for l in labels]).astype(np.int32)
>>> rnnt_loss = ops.RNNTLoss(blank_label=0)
>>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
>>> print(costs)
[-3.5036912]
>>> print(grads)
[[[[-0.35275543 -0.64724463 0. 0. 0. ]
[-0.19174816 0. -0.45549652 0. 0. ]
[-0.45549664 0. 0. 0. 0. ]]
[[0. -0.35275543 0. 0. 0. ]
[0. 0. -0.5445037 0. 0. ]
[-1.00000002 0. 0. 0. 0. ]]]]
>>> print(costs.shape)
(1,)
>>> print(grads.shape)
(1, 2, 3, 5)
"""

@prim_attr_register
@@ -2640,13 +2637,8 @@ class L2Normalize(PrimitiveWithInfer):
>>> l2_normalize = ops.L2Normalize()
>>> input_x = Tensor(np.random.randint(-256, 256, (2, 3, 4)), mindspore.float32)
>>> output = l2_normalize(input_x)
>>> print(output)
[[[-0.47247353 -0.30934513 -0.4991462 0.8185567 ]
[-0.08070751 -0.9961299 -0.5741758 0.09262337]
[-0.9916556 -0.3049123 0.5730487 -0.40579924]
[[-0.88134485 0.9509498 -0.86651784 0.57442576]
[ 0.99673784 0.08789381 -0.8187321 0.9957012 ]
[ 0.12891524 -0.9523804 -0.81952125 0.91396334]]]
>>> print(output.shape)
(2, 3, 4)
"""

@prim_attr_register
@@ -2688,8 +2680,8 @@ class DropoutGenMask(Primitive):
>>> shape = (2, 4, 5)
>>> keep_prob = Tensor(0.5, mindspore.float32)
>>> output = dropout_gen_mask(shape, keep_prob)
>>> print(output)
[249 11 134 133 143 246 89 52 169 15 94 63 146 103 7 101]
>>> print(output.shape)
(16,)
"""

@prim_attr_register
@@ -2729,11 +2721,8 @@ class DropoutDoMask(PrimitiveWithInfer):
>>> dropout_do_mask = ops.DropoutDoMask()
>>> mask = dropout_gen_mask(shape, keep_prob)
>>> output = dropout_do_mask(x, mask, keep_prob)
>>> print(output)
[[[2. 0. 0.]
[2. 0. 0.]]
[[0. 2. 2.]
[2. 0. 2.]]]
>>> print(output.shape)
(2, 2, 3)
"""

@prim_attr_register
@@ -3066,24 +3055,19 @@ class PReLU(PrimitiveWithInfer):
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as ops
>>> class Net(nn.Cell):
>>> def __init__(self):
>>> super(Net, self).__init__()
>>> self.prelu = ops.PReLU()
>>> def construct(self, input_x, weight):
>>> result = self.prelu(input_x, weight)
>>> return result
>>>
... def __init__(self):
... super(Net, self).__init__()
... self.prelu = ops.PReLU()
... def construct(self, input_x, weight):
... result = self.prelu(input_x, weight)
... return result
...
>>> input_x = Tensor(np.random.randint(-3, 3, (2, 3, 2)), mindspore.float32)
>>> weight = Tensor(np.array([0.1, 0.6, -0.3]), mindspore.float32)
>>> net = Net()
>>> output = net(input_x, weight)
>>> print(output)
[[[-0.2 -0.1 ]
[-1.8000001 -0.6 ]
[ 0.90000004 1. ]]
[[-0.3 -0.1 ]
[-1.8000001 2. ]
[ 0.90000004 0.90000004]]]
>>> print(output.shape)
(2, 3, 2)
"""

@prim_attr_register
@@ -3373,11 +3357,8 @@ class MirrorPad(PrimitiveWithInfer):
>>> paddings = Tensor([[1,1],[2,2]])
>>> pad = Net()
>>> output = pad(Tensor(x), paddings)
>>> print(output)
[[0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144]
[0.31417271 0.96308136 0.934709 0.96308136 0.31417271 0.96308136 0.934709 ]
[0.5525309 0.49183875 0.99110144 0.49183875 0.5525309 0.49183875 0.99110144]
[0.31417271 0.96308136 0.934709 0.96308136 0.31417271 0.96308136 0.934709 ]]
>>> print(output.shape)
(4, 7)
"""

@prim_attr_register
@@ -3613,19 +3594,19 @@ class Adam(PrimitiveWithInfer):
... out = self.apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2,
... epsilon, grad)
...
>>> return out
... return out
>>> np.random.seed(0)
>>> net = Net()
>>> gradient = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> result = net(0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient)
>>> output = net(0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[[ 9.99458194e-01, 9.99398530e-01],
[ 9.99404728e-01, 9.99371529e-01]]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[[ 8.17151368e-01, 9.41661000e-01],
[ 9.28607702e-01, 9.98143375e-01]]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[[ 9.98003900e-01, 9.98960912e-01],
[ 9.98780012e-01, 9.99961138e-01]]]))
[[ 9.99697924e-01, 9.99692678e-01],
[ 9.99696255e-01, 9.99698043e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 9.54881310e-01, 9.71518934e-01],
[ 9.60276306e-01, 9.54488277e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 9.99301195e-01, 9.99511480e-01],
[ 9.99363303e-01, 9.99296904e-01]]))
"""

@prim_attr_register
@@ -4461,6 +4442,7 @@ class ApplyAdaMax(PrimitiveWithInfer):
... out = self.apply_ada_max(self.var, self.m, self.v, beta1_power, lr, beta1, beta2, epsilon, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> beta1_power =Tensor(0.9, mstype.float32)
>>> lr = Tensor(0.001, mstype.float32)
@@ -4471,12 +4453,12 @@ class ApplyAdaMax(PrimitiveWithInfer):
>>> output = net(beta1_power, lr, beta1, beta2, epsilon, grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.46618605e-01, 6.48276925e-01],
[ 7.72792041e-01, 8.58803272e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.23247683e-01, 6.30929232e-01],
[ 9.17923033e-01, 8.98910999e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 3.03175300e-01, 5.75195193e-01],
[ 9.43458021e-01, 8.41971099e-01]]))
[[ 5.44221461e-01, 7.07908988e-01],
[ 5.97648144e-01, 5.29388547e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 4.38093781e-01, 6.73864365e-01],
[ 4.00932074e-01, 8.11308622e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 9.54026103e-01, 9.25596654e-01],
[ 7.83807814e-01, 5.23605943e-01]]))
"""

__mindspore_signature__ = (
@@ -4593,6 +4575,7 @@ class ApplyAdadelta(PrimitiveWithInfer):
... out = self.apply_adadelta(self.var, self.accum, self.accum_update, lr, rho, epsilon, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> lr = Tensor(0.001, mstype.float32)
>>> rho = Tensor(0.0, mstype.float32)
@@ -4601,12 +4584,12 @@ class ApplyAdadelta(PrimitiveWithInfer):
>>> output = net(lr, rho, epsilon, grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 7.60124624e-01, 9.54110503e-01],
[ 7.25456238e-01, 4.98913884e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.00194868e-02, 5.50848258e-01],
[ 9.95293319e-01, 1.97404027e-02]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 4.17240560e-01, 8.39873433e-01],
[ 4.95992631e-01, 9.19294059e-01]]))
[[ 5.47831833e-01, 7.14570105e-01],
[ 6.01873636e-01, 5.44156015e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 3.22674602e-01, 8.56729150e-01],
[ 5.04612131e-03, 7.59151531e-03]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 9.63660717e-01, 3.83442074e-01],
[ 7.91569054e-01, 5.28826237e-01]]))
"""

__mindspore_signature__ = (
@@ -4704,16 +4687,17 @@ class ApplyAdagrad(PrimitiveWithInfer):
... out = self.apply_adagrad(self.var, self.accum, lr, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> lr = Tensor(0.001, mstype.float32)
>>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(lr, grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 7.12832332e-01, 3.10275197e-01],
[ 9.02635300e-01, 3.90718848e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 8.68964046e-02, 3.21274072e-01],
[ 1.19302607e+00, 9.59712446e-01]]))
[[ 5.47984838e-01, 7.14758754e-01],
[ 6.01995945e-01, 5.44394553e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.35230064e+00, 7.92921484e-01],
[ 1.06441569e+00, 1.17150283e+00]]))
"""

__mindspore_signature__ = (
@@ -4797,16 +4781,17 @@ class ApplyAdagradV2(PrimitiveWithInfer):
... out = self.apply_adagrad_v2(self.var, self.accum, lr, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> lr = Tensor(0.001, mstype.float32)
>>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(lr, grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.75180078e-01, 5.12131870e-01],
[ 9.32922423e-01, 6.53732181e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 8.45080376e-01, 4.80091214e-01],
[ 1.68451762e+00, 1.03823669e+00]]))
[[ 5.47984838e-01, 7.14758754e-01],
[ 6.01995945e-01, 5.44394553e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.35230064e+00, 7.92921484e-01],
[ 1.06441569e+00, 1.17150283e+00]]))
"""

__mindspore_signature__ = (
@@ -4891,6 +4876,7 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
... out = self.sparse_apply_adagrad(self.var, self.accum, grad, indices)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32))
>>> indices = Tensor([0], mstype.int32)
@@ -4987,6 +4973,7 @@ class SparseApplyAdagradV2(PrimitiveWithInfer):
... out = self.sparse_apply_adagrad_v2(self.var, self.accum, grad, indices)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(1, 1, 1).astype(np.float32))
>>> indices = Tensor([0], mstype.int32)
@@ -4994,7 +4981,7 @@ class SparseApplyAdagradV2(PrimitiveWithInfer):
>>> print(output)
(Tensor(shape=[1, 1, 1], dtype=Float32, value=
[[[1.00000000e+00]]]), Tensor(shape=[1, 1, 1], dtype=Float32, value=
[[[1.13986731e+00]]]))
[[[1.30119634e+00]]]))
"""

__mindspore_signature__ = (
@@ -5086,15 +5073,16 @@ class ApplyProximalAdagrad(PrimitiveWithInfer):
... out = self.apply_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 3.79054576e-01, 5.28407156e-01],
[ 2.39551291e-01, 7.34573752e-02]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 8.96461844e-01, 1.47237992e+00],
[ 8.52952123e-01, 1.22406030e+00]]))
[[ 5.40526688e-01, 7.10883260e-01],
[ 5.95089436e-01, 5.39996684e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.35230064e+00, 7.92921484e-01],
[ 1.06441569e+00, 1.17150283e+00]]))
"""

__mindspore_signature__ = (
@@ -5203,14 +5191,15 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
... self.l2, grad, indices)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(1, 2).astype(np.float32))
>>> indices = Tensor(np.ones((1,), np.int32))
>>> output = net(grad, indices)
>>> print(output)
Tensor(shape=[1, 2], dtype=Float32, value=
[[ 7.74297953e-01, 7.12414503e-01]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 6.14362955e-01, 6.38007671e-02]]))
(Tensor(shape=[1, 2], dtype=Float32, value=
[[ 5.48813522e-01, 7.15189338e-01]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 6.02763355e-01, 5.44883192e-01]]))
"""

__mindspore_signature__ = (
@@ -5302,15 +5291,16 @@ class ApplyAddSign(PrimitiveWithInfer):
... out = self.apply_add_sign(self.var, self.m, self.lr, self.alpha, self.sign_decay, self.beta, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 5.37551343e-01, 3.78310502e-01],
[ 7.81984031e-01, 5.19252002e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 8.28343272e-01, 8.14828694e-01],
[ 3.79919171e-01, 2.55756438e-01]]))
[[ 5.46895862e-01, 7.14426279e-01],
[ 6.01187825e-01, 5.43830693e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 4.77655590e-01, 6.19648814e-01],
[ 4.73001003e-01, 8.55485201e-01]]))
"""

__mindspore_signature__ = (
@@ -5421,15 +5411,16 @@ class ApplyPowerSign(PrimitiveWithInfer):
... self.sign_decay, self.beta, grad)
... return out
...
>>> np.random.seed(0)
>>> net = Net()
>>> grad = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 5.01964271e-01, 8.59248936e-01],
[ 5.14324069e-01, 2.50274092e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 5.16151905e-01, 7.50251293e-01],
[ 4.36047137e-01, 1.26427144e-01]]))
[[ 5.34601569e-01, 7.09534407e-01],
[ 5.91087103e-01, 5.37083089e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 4.77655590e-01, 6.19648814e-01],
[ 4.73001003e-01, 8.55485201e-01]]))
"""

__mindspore_signature__ = (
@@ -5519,9 +5510,8 @@ class ApplyGradientDescent(PrimitiveWithInfer):
>>> net = Net()
>>> delta = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(delta)
>>> print(output)
[[0.54876804 0.38894778]
[0.5847089 0.09858753]]
>>> print(output.shape)
(2, 2)
"""

__mindspore_signature__ = (
@@ -5599,9 +5589,8 @@ class ApplyProximalGradientDescent(PrimitiveWithInfer):
>>> net = Net()
>>> delta = Tensor(np.random.rand(2, 2).astype(np.float32))
>>> output = net(delta)
>>> print(output)
[[0.38671502 0.087947 ]
[0.07595529 0.44336063]]
>>> print(output.shape)
(2, 2)
"""

__mindspore_signature__ = (
@@ -5778,17 +5767,18 @@ class ApplyFtrl(PrimitiveWithInfer):
... self.lr_power)
... return out
...
>>> np.random.seed(0)
>>> net = ApplyFtrlNet()
>>> input_x = Tensor(np.random.randint(-4, 4, (2, 2)), mindspore.float32)
>>> output = net(input_x)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.51306406e-01, 4.06460911e-02],
[ 5.40895802e-01, 1.35308430e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.62730598e+01, 4.53126240e+00],
[ 4.10181570e+01, 4.67408800e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[-6.10368164e+02, -8.65223694e+01],
[-1.09547302e+03, -2.92531921e+02]]))
[[ 4.61418092e-01, 5.30964255e-01],
[ 2.68715084e-01, 3.82065028e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.64236546e+01, 9.64589405e+00],
[ 1.43758726e+00, 9.89177322e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[-1.86994812e+03, -1.64906018e+03],
[-3.22187836e+02, -1.20163989e+03]]))
"""

@prim_attr_register
@@ -5873,15 +5863,16 @@ class SparseApplyFtrl(PrimitiveWithCheck):
... out = self.sparse_apply_ftrl(self.var, self.accum, self.linear, grad, indices)
... return out
...
>>> np.random.seed(0)
>>> net = SparseApplyFtrlNet()
>>> grad = Tensor(np.random.rand(1, 1).astype(np.float32))
>>> indices = Tensor(np.ones([1]), mindspore.int32)
>>> output = net(grad, indices)
>>> print(output)
(Tensor(shape=[1, 1], dtype=Float32, value=
[[1.21931173e-01]]), Tensor(shape=[1, 1], dtype=Float32, value=
[[3.54384869e-01]]), Tensor(shape=[1, 1], dtype=Float32, value=
[[2.99625486e-01]]))
[[5.48813522e-01]]), Tensor(shape=[1, 1], dtype=Float32, value=
[[7.15189338e-01]]), Tensor(shape=[1, 1], dtype=Float32, value=
[[6.02763355e-01]]))
"""

__mindspore_signature__ = (
@@ -5979,15 +5970,16 @@ class SparseApplyFtrlV2(PrimitiveWithInfer):
... out = self.sparse_apply_ftrl_v2(self.var, self.accum, self.linear, grad, indices)
... return out
...
>>> np.random.seed(0)
>>> net = SparseApplyFtrlV2Net()
>>> grad = Tensor(np.random.rand(1, 2).astype(np.float32))
>>> indices = Tensor(np.ones([1]), mindspore.int32)
>>> output = net(grad, indices)
>>> print(output)
Tensor(shape=[1, 2], dtype=Float32, value=
[[ 8.69189978e-01, 7.50899851e-01]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 2.51525849e-01, 2.19218452e-02]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 1.70145389e-02, 7.74444342e-01]]))
(Tensor(shape=[1, 2], dtype=Float32, value=
[[ 5.48813522e-01, 7.15189338e-01]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 6.02763355e-01, 5.44883192e-01]]), Tensor(shape=[1, 2], dtype=Float32, value=
[[ 4.23654795e-01, 6.45894110e-01]]))
"""

__mindspore_signature__ = (
@@ -6110,14 +6102,10 @@ class CTCLoss(PrimitiveWithInfer):
>>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
>>> ctc_loss = ops.CTCLoss()
>>> loss, gradient = ctc_loss(inputs, labels_indices, labels_values, sequence_length)
>>> print(loss)
[0.69121575 0.5381993]
>>> print(gradient)
[[[0.25831494 0.3623634 -0.62067937]
[0.25187883 0.2921483 -0.5440271]]

[[0.43522435 0.24408469 0.07787037]
[0.29642645 0.4232373 0.06138104]]]
>>> print(loss.shape)
(2,)
>>> print(gradient.shape)
(2, 2, 3)
"""

@prim_attr_register
@@ -6284,6 +6272,7 @@ class BasicLSTMCell(PrimitiveWithInfer):
``Ascend``

Examples:
>>> np.random.seed(0)
>>> x = Tensor(np.random.rand(1, 32).astype(np.float16))
>>> h = Tensor(np.random.rand(1, 2).astype(np.float16))
>>> c = Tensor(np.random.rand(1, 2).astype(np.float16))
@@ -6293,12 +6282,12 @@ class BasicLSTMCell(PrimitiveWithInfer):
>>> output = lstm(x, h, c, w, b)
>>> print(output)
(Tensor(shape=[1, 2], dtype=Float16, value=
([[9.5312e-01, 9.5215e-01]]), Tensor(shape=[1, 2], dtype=Float16, value=
([[7.6953e-01, 9.2432e-01]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[9.5312e-01, 9.5215e-01]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[7.6953e-01, 9.2432e-01]]), Tensor(shape=[1, 2], dtype=Float16, value=
[[0.0000e+00, 0.0000e+00]]))
"""

@@ -6405,7 +6394,7 @@ class DynamicRNN(PrimitiveWithInfer):
>>> b = Tensor(np.random.rand(128).astype(np.float16))
>>> init_h = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
>>> init_c = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
>>> dynamic_rnn = ops.DynamicRNNN()
>>> dynamic_rnn = ops.DynamicRNN()
>>> output = dynamic_rnn(x, w, b, None, init_h, init_c)
>>> print(output[0].shape)
(2, 16, 32)
@@ -6699,11 +6688,8 @@ class LRN(PrimitiveWithInfer):
>>> x = Tensor(np.random.rand(1, 2, 2, 2), mindspore.float32)
>>> lrn = ops.LRN()
>>> output = lrn(x)
>>> print(output)
[[[[0.18990143 0.59475636]
[0.6291904 0.1371534 ]]
[[0.6258911 0.4964315 ]
[0.3141494 0.43636137]]]]
>>> print(output.shape)
(1, 2, 2, 2)
"""

@prim_attr_register


+ 2
- 5
mindspore/ops/operations/other_ops.py View File

@@ -318,11 +318,8 @@ class IOU(PrimitiveWithInfer):
>>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> output = iou(anchor_boxes, gt_boxes)
>>> print(output)
[[65500. 65500. 65500.]
[ -0. -0. -0.]
[ -0. -0. -0.]]

>>> print(output.shape)
(3, 3)
"""

@prim_attr_register


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