From bc4efbc9d39e3fc5728e2f8a3846b5c1e8c91399 Mon Sep 17 00:00:00 2001 From: lihongkang <[lihongkang1@huawei.com]> Date: Wed, 16 Dec 2020 20:55:48 +0800 Subject: [PATCH] fix bugs --- mindspore/nn/layer/normalization.py | 2 + mindspore/nn/layer/pooling.py | 14 +- mindspore/nn/layer/quant.py | 2 +- mindspore/nn/loss/loss.py | 3 +- mindspore/ops/operations/nn_ops.py | 278 ++++++++++++-------------- mindspore/ops/operations/other_ops.py | 7 +- 6 files changed, 143 insertions(+), 163 deletions(-) diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 49595f85af..4546ab336b 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -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 ]]]] """ diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py index d5ebb785bf..c9b6746e44 100644 --- a/mindspore/nn/layer/pooling.py +++ b/mindspore/nn/layer/pooling.py @@ -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"): @@ -270,11 +267,8 @@ 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, diff --git a/mindspore/nn/layer/quant.py b/mindspore/nn/layer/quant.py index 870a911e6a..f16482712f 100644 --- a/mindspore/nn/layer/quant.py +++ b/mindspore/nn/layer/quant.py @@ -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 diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py index 13a424f8e1..5c314877d5 100644 --- a/mindspore/nn/loss/loss.py +++ b/mindspore/nn/loss/loss.py @@ -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, diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 756da0dbf9..038c54f709 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -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__ = ( @@ -5201,14 +5189,15 @@ 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__ = ( @@ -5300,15 +5289,16 @@ 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__ = ( @@ -5419,15 +5409,16 @@ 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__ = ( @@ -5517,9 +5508,8 @@ 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__ = ( @@ -5597,9 +5587,8 @@ 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__ = ( @@ -5776,17 +5765,18 @@ 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 @@ -5870,15 +5860,16 @@ 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__ = ( @@ -5976,15 +5967,16 @@ 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__ = ( @@ -6107,14 +6099,10 @@ 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 @@ -6281,6 +6269,7 @@ 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)) @@ -6290,12 +6279,12 @@ 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]])) """ @@ -6402,7 +6391,7 @@ 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) @@ -6696,11 +6685,8 @@ 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 diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py index e445db053b..568e87ce5e 100644 --- a/mindspore/ops/operations/other_ops.py +++ b/mindspore/ops/operations/other_ops.py @@ -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