diff --git a/mindspore/nn/dynamic_lr.py b/mindspore/nn/dynamic_lr.py index 54f10c805c..e7dfe98d6a 100644 --- a/mindspore/nn/dynamic_lr.py +++ b/mindspore/nn/dynamic_lr.py @@ -40,7 +40,8 @@ def piecewise_constant_lr(milestone, learning_rates): Examples: >>> milestone = [2, 5, 10] >>> learning_rates = [0.1, 0.05, 0.01] - >>> piecewise_constant_lr(milestone, learning_rates) + >>> output = piecewise_constant_lr(milestone, learning_rates) + >>> print(output) [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01] """ validator.check_value_type('milestone', milestone, (tuple, list)) @@ -100,7 +101,8 @@ def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 1 - >>> exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) + >>> output = exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch) + >>> print(output) [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002] """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) @@ -142,7 +144,8 @@ def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 - >>> natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) + >>> output = natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) + >>> print(output) [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657] """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) @@ -185,7 +188,8 @@ def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, deca >>> total_step = 6 >>> step_per_epoch = 1 >>> decay_epoch = 1 - >>> inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) + >>> output = inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True) + >>> print(output) [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574] """ _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair) @@ -227,7 +231,8 @@ def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch): >>> total_step = 6 >>> step_per_epoch = 2 >>> decay_epoch = 2 - >>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) + >>> output = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch) + >>> print(output) [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01] """ if not isinstance(min_lr, float): @@ -295,7 +300,8 @@ def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_e >>> step_per_epoch = 2 >>> decay_epoch = 2 >>> power = 0.5 - >>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) + >>> r = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) + >>> print(r) [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01] """ validator.check_positive_float(learning_rate, 'learning_rate') @@ -350,7 +356,8 @@ def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch): >>> total_step = 6 >>> step_per_epoch = 2 >>> warmup_epoch = 2 - >>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch) + >>> output = warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch) + >>> print(output) [0.0, 0.0, 0.05, 0.05, 0.1, 0.1] """ if not isinstance(learning_rate, float): diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py index 2ff36e3771..ae320c03b1 100644 --- a/mindspore/nn/layer/activation.py +++ b/mindspore/nn/layer/activation.py @@ -70,7 +70,8 @@ class Softmax(Cell): Examples: >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> softmax = nn.Softmax() - >>> softmax(input_x) + >>> output = softmax(input_x) + >>> print(output) [0.03168 0.01166 0.0861 0.636 0.2341] """ @@ -106,7 +107,8 @@ class LogSoftmax(Cell): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> log_softmax = nn.LogSoftmax() - >>> log_softmax(input_x) + >>> output = log_softmax(input_x) + >>> print(output) [[-5.00672150e+00 -6.72150636e-03 -1.20067215e+01] [-7.00091219e+00 -1.40009127e+01 -9.12250078e-04]] """ @@ -174,7 +176,8 @@ class ReLU(Cell): Examples: >>> input_x = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16) >>> relu = nn.ReLU() - >>> relu(input_x) + >>> output = relu(input_x) + >>> print(output) [0. 2. 0. 2. 0.] """ @@ -203,7 +206,8 @@ class ReLU6(Cell): Examples: >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> relu6 = nn.ReLU6() - >>> relu6(input_x) + >>> output = relu6(input_x) + >>> print(output) [0. 0. 0. 2. 1.] """ @@ -240,7 +244,8 @@ class LeakyReLU(Cell): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> leaky_relu = nn.LeakyReLU() - >>> leaky_relu(input_x) + >>> output = leaky_relu(input_x) + >>> print(output) [[-0.2 4. -1.6] [ 2 -1. 9.]] """ @@ -284,7 +289,8 @@ class Tanh(Cell): Examples: >>> input_x = Tensor(np.array([1, 2, 3, 2, 1]), mindspore.float16) >>> tanh = nn.Tanh() - >>> tanh(input_x) + >>> output = tanh(input_x) + >>> print(output) [0.7617 0.964 0.995 0.964 0.7617] """ @@ -315,7 +321,8 @@ class GELU(Cell): Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) >>> gelu = nn.GELU() - >>> gelu(input_x) + >>> output = gelu(input_x) + >>> print(output) [[-1.5880802e-01 3.9999299e+00 -3.1077917e-21] [ 1.9545976e+00 -2.2918017e-07 9.0000000e+00]] """ @@ -346,7 +353,8 @@ class Sigmoid(Cell): Examples: >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16) >>> sigmoid = nn.Sigmoid() - >>> sigmoid(input_x) + >>> output = sigmoid(input_x) + >>> print(output) [0.2688 0.11914 0.5 0.881 0.7305] """ @@ -384,7 +392,8 @@ class PReLU(Cell): Examples: >>> input_x = Tensor(np.array([[[[0.1, 0.6], [0.9, 0.9]]]]), mindspore.float32) >>> prelu = nn.PReLU() - >>> prelu(input_x) + >>> output = prelu(input_x) + >>> print(output) [[[[0.1 0.6] [0.9 0.9]]]] @@ -506,6 +515,7 @@ class LogSigmoid(Cell): >>> net = nn.LogSigmoid() >>> input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) >>> logsigmoid = net(input_x) + >>> print(logsigmoid) [-3.1326166e-01, -1.2692806e-01, -4.8587345e-02] """ diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py index b5bc2cbb99..f9f54683e5 100644 --- a/mindspore/nn/layer/basic.py +++ b/mindspore/nn/layer/basic.py @@ -76,7 +76,8 @@ class Dropout(Cell): >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32) >>> net = nn.Dropout(keep_prob=0.8) >>> net.set_train() - >>> net(x) + >>> output = net(x) + >>> print(output) [[[0., 1.25, 0.], [1.25, 1.25, 1.25]], [[1.25, 1.25, 1.25], @@ -141,7 +142,8 @@ class Flatten(Cell): Examples: >>> input = Tensor(np.array([[[1.2, 1.2], [2.1, 2.1]], [[2.2, 2.2], [3.2, 3.2]]]), mindspore.float32) >>> net = nn.Flatten() - >>> net(input) + >>> output = net(input) + >>> print(output) [[1.2 1.2 2.1 2.1] [2.2 2.2 3.2 3.2]] """ @@ -196,7 +198,8 @@ class Dense(Cell): Examples: >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32) >>> net = nn.Dense(3, 4) - >>> net(input) + >>> output = net(input) + >>> print(output) [[ 2.5246444 2.2738023 0.5711005 -3.9399147 ] [ 1.0739875 4.0155234 0.94188046 -5.459526 ]] """ @@ -317,7 +320,8 @@ class ClipByNorm(Cell): >>> net = nn.ClipByNorm() >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32) >>> clip_norm = Tensor(np.array([100]).astype(np.float32)) - >>> net(input, clip_norm).shape + >>> result = net(input, clip_norm).shape + >>> print(result) (4, 16) """ @@ -386,7 +390,8 @@ class Norm(Cell): Examples: >>> net = nn.Norm(axis=0) >>> input = Tensor(np.random.randint(0, 10, [2, 4]), mindspore.float32) - >>> net(input) + >>> output = net(input) + >>> print(output) [2.236068 9.848858 4. 5.656854] """ @@ -442,7 +447,8 @@ class OneHot(Cell): Examples: >>> net = nn.OneHot(depth=4, axis=1) >>> indices = Tensor([[1, 3], [0, 2]], dtype=mindspore.int32) - >>> net(indices) + >>> output = net(indices) + >>> print(output) [[[0. 0.] [1. 0.] [0. 0.] @@ -501,11 +507,11 @@ class Pad(Cell): >>> import mindspore.nn as nn >>> import numpy as np >>> class Net(nn.Cell): - >>> def __init__(self): - >>> super(Net, self).__init__() - >>> self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT") - >>> def construct(self, x): - >>> return self.pad(x) + ... def __init__(self): + ... super(Net, self).__init__() + ... self.pad = nn.Pad(paddings=((1,1),(2,2)), mode="CONSTANT") + ... def construct(self, x): + ... return self.pad(x) >>> x = np.random.random(size=(2, 3)).astype(np.float32) >>> pad = Net() >>> ms_output = pad(Tensor(x)) @@ -567,9 +573,10 @@ class Unfold(Cell): Examples: >>> net = Unfold(ksizes=[1, 2, 2, 1], strides=[1, 2, 2, 1], rates=[1, 2, 2, 1]) >>> image = Tensor(np.ones([2, 3, 6, 6]), dtype=mstype.float16) - >>> net(image) - Tensor ([[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1] [1, 1]], [[1, 1] [1, 1]], - [[1, 1], [1, 1]]]], shape=(2, 12, 2, 2), dtype=mstype.float16) + >>> output = net(image) + >>> print(output) + [[[[1, 1] [1, 1]] [[1, 1], [1, 1]] [[1, 1] [1, 1]], [[1, 1] [1, 1]], [[1, 1] [1, 1]], + [[1, 1], [1, 1]]]] """ def __init__(self, ksizes, strides, rates, padding="valid"): @@ -621,6 +628,7 @@ class MatrixDiag(Cell): >>> x = Tensor(np.array([1, -1]), mstype.float32) >>> matrix_diag = nn.MatrixDiag() >>> result = matrix_diag(x) + >>> print(result) [[1. 0.] [0. -1.]] """ @@ -652,6 +660,7 @@ class MatrixDiagPart(Cell): >>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32) >>> matrix_diag_part = nn.MatrixDiagPart() >>> result = matrix_diag_part(x) + >>> print(result) [[-1., 1.], [-1., 1.], [-1., 1.]] """ def __init__(self): @@ -684,6 +693,7 @@ class MatrixSetDiag(Cell): >>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32) >>> matrix_set_diag = nn.MatrixSetDiag() >>> result = matrix_set_diag(x, diagonal) + >>> print(result) [[[-1, 0], [0, 2]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]] """ def __init__(self): diff --git a/mindspore/nn/layer/combined.py b/mindspore/nn/layer/combined.py index a5582f6ab2..981b11add4 100644 --- a/mindspore/nn/layer/combined.py +++ b/mindspore/nn/layer/combined.py @@ -80,7 +80,8 @@ class Conv2dBnAct(Cell): >>> net = nn.Conv2dBnAct(120, 240, 4, has_bn=True, activation='relu') >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32) >>> result = net(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (1, 240, 1024, 640) """ @@ -171,7 +172,8 @@ class DenseBnAct(Cell): >>> net = nn.DenseBnAct(3, 4) >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32) >>> result = net(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (2, 4) """ diff --git a/mindspore/nn/layer/container.py b/mindspore/nn/layer/container.py index 3e0cbd29fd..dccbbd94e6 100644 --- a/mindspore/nn/layer/container.py +++ b/mindspore/nn/layer/container.py @@ -87,7 +87,8 @@ class SequentialCell(Cell): >>> seq = nn.SequentialCell([conv, bn, relu]) >>> >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32) - >>> seq(x) + >>> output = seq(x) + >>> print(output) [[[[0.02531557 0. ] [0.04933941 0.04880078]] [[0. 0. ] @@ -155,7 +156,8 @@ class SequentialCell(Cell): >>> seq = nn.SequentialCell([conv, bn]) >>> seq.append(relu) >>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32) - >>> seq(x) + >>> output = seq(x) + >>> print(output) [[[[0.12445523 0.12445523] [0.12445523 0.12445523]] [[0. 0. ] diff --git a/mindspore/nn/layer/conv.py b/mindspore/nn/layer/conv.py index 2f7fc8b3ec..3bd3cf6c54 100644 --- a/mindspore/nn/layer/conv.py +++ b/mindspore/nn/layer/conv.py @@ -199,7 +199,8 @@ class Conv2d(_Conv): Examples: >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal') >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32) - >>> net(input).shape + >>> output = net(input).shape + >>> print(output) (1, 240, 1024, 640) """ @@ -374,7 +375,8 @@ class Conv1d(_Conv): Examples: >>> net = nn.Conv1d(120, 240, 4, has_bias=False, weight_init='normal') >>> input = Tensor(np.ones([1, 120, 640]), mindspore.float32) - >>> net(input).shape + >>> output = net(input).shape + >>> print(output) (1, 240, 640) """ @@ -544,7 +546,8 @@ class Conv2dTranspose(_Conv): Examples: >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad') >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32) - >>> net(input).shape + >>> output = net(input).shape + >>> print(output) (1, 64, 19, 53) """ @@ -719,7 +722,8 @@ class Conv1dTranspose(_Conv): Examples: >>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad') >>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32) - >>> net(input).shape + >>> output = net(input).shape + >>> print(output) (1, 64, 53) """ diff --git a/mindspore/nn/layer/embedding.py b/mindspore/nn/layer/embedding.py index b30afbfdb2..f93321efcb 100755 --- a/mindspore/nn/layer/embedding.py +++ b/mindspore/nn/layer/embedding.py @@ -66,7 +66,8 @@ class Embedding(Cell): >>> >>> # Maps the input word IDs to word embedding. >>> output = net(input_data) - >>> output.shape + >>> result = output.shape + >>> print(result) (8, 128, 768) """ diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py index a8cacfd960..387e419ec6 100644 --- a/mindspore/nn/layer/image.py +++ b/mindspore/nn/layer/image.py @@ -53,7 +53,8 @@ class ImageGradients(Cell): Examples: >>> net = nn.ImageGradients() >>> image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32) - >>> net(image) + >>> output = net(image) + >>> print(output) [[[[2,2] [0,0]]]] [[[[1,0] @@ -214,6 +215,7 @@ class SSIM(Cell): >>> img1 = Tensor(np.random.random((1,3,16,16)), mindspore.float32) >>> img2 = Tensor(np.random.random((1,3,16,16)), mindspore.float32) >>> ssim = net(img1, img2) + >>> print(ssim) [0.12174469] """ def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): @@ -379,6 +381,7 @@ class PSNR(Cell): >>> img1 = Tensor(np.random.random((1,3,16,16))) >>> img2 = Tensor(np.random.random((1,3,16,16))) >>> psnr = net(img1, img2) + >>> print(psnr) [7.8297315] """ def __init__(self, max_val=1.0): @@ -447,7 +450,8 @@ class CentralCrop(Cell): >>> net = nn.CentralCrop(central_fraction=0.5) >>> image = Tensor(np.random.random((4, 3, 4, 4)), mindspore.float32) >>> output = net(image) - >>> output.shape + >>> result = output.shape + >>> print(result) (4, 3, 2, 2) """ diff --git a/mindspore/nn/layer/math.py b/mindspore/nn/layer/math.py index b066d44b0b..3543fa5bab 100644 --- a/mindspore/nn/layer/math.py +++ b/mindspore/nn/layer/math.py @@ -64,7 +64,8 @@ class ReduceLogSumExp(Cell): >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = nn.ReduceLogSumExp(1, keep_dims=True) >>> output = op(input_x) - >>> output.shape + >>> result = output.shape + >>> print(reuslt) (3, 1, 5, 6) """ @@ -101,6 +102,7 @@ class Range(Cell): Examples: >>> net = nn.Range(1, 8, 2) >>> out = net() + >>> print(out) [1, 3, 5, 7] """ @@ -154,6 +156,7 @@ class LinSpace(Cell): Examples: >>> linspace = nn.LinSpace(1, 10, 5) >>> output = linspace() + >>> print(output) [1, 3.25, 5.5, 7.75, 10] """ diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py index 2ba0016f18..d2ac092835 100644 --- a/mindspore/nn/layer/normalization.py +++ b/mindspore/nn/layer/normalization.py @@ -522,7 +522,8 @@ class LayerNorm(Cell): >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32) >>> shape1 = x.shape[1:] >>> m = nn.LayerNorm(shape1, begin_norm_axis=1, begin_params_axis=1) - >>> m(x).shape + >>> output = m(x).shape + >>> print(output) (20, 5, 10, 10) """ @@ -593,7 +594,8 @@ class GroupNorm(Cell): Examples: >>> goup_norm_op = nn.GroupNorm(2, 2) >>> x = Tensor(np.ones([1, 2, 4, 4], np.float32)) - >>> goup_norm_op(x) + >>> output = goup_norm_op(x) + >>> print(output) [[[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py index ce91ed9735..daafe8a8a9 100644 --- a/mindspore/nn/layer/pooling.py +++ b/mindspore/nn/layer/pooling.py @@ -107,6 +107,7 @@ class MaxPool2d(_PoolNd): Examples: >>> pool = nn.MaxPool2d(kernel_size=3, stride=1) >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) + >>> print(x) [[[[1. 5. 5. 1.] [0. 3. 4. 8.] [4. 2. 7. 6.] @@ -116,9 +117,10 @@ class MaxPool2d(_PoolNd): [0. 0. 4. 0.] [1. 8. 7. 0.]]]] >>> output = pool(x) - >>> output.shape + >>> reuslt = output.shape + >>> print(result) (1, 2, 2, 2) - >>> output + >>> print(output) [[[[7. 8.] [9. 9.]] [[7. 8.] @@ -185,7 +187,8 @@ class MaxPool1d(_PoolNd): >>> max_pool = nn.MaxPool1d(kernel_size=3, stride=1) >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4]), mindspore.float32) >>> output = max_pool(x) - >>> output.shape + >>> result = output.shape + >>> printI(result) (1, 2, 2) """ @@ -269,6 +272,7 @@ class AvgPool2d(_PoolNd): Examples: >>> pool = nn.AvgPool2d(kernel_size=3, stride=1) >>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32) + >>> print(x) [[[[5. 5. 9. 9.] [8. 4. 3. 0.] [2. 7. 1. 2.] @@ -278,9 +282,10 @@ class AvgPool2d(_PoolNd): [0. 8. 9. 7.] [2. 1. 4. 9.]]]] >>> output = pool(x) - >>> output.shape + >>> result = output.shape + >>> print(result) (1, 2, 2, 2) - >>> output + >>> print(output) [[[[4.888889 4.4444447] [4.111111 3.4444444]] [[4.2222223 4.5555553] @@ -345,7 +350,8 @@ class AvgPool1d(_PoolNd): >>> pool = nn.AvgPool1d(kernel_size=6, stride=1) >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32) >>> output = pool(x) - >>> output.shape + >>> result = output.shape + >>> print(result) (1, 3, 1) """ diff --git a/mindspore/nn/layer/quant.py b/mindspore/nn/layer/quant.py index 0449e81429..c24bd5b9c4 100644 --- a/mindspore/nn/layer/quant.py +++ b/mindspore/nn/layer/quant.py @@ -116,7 +116,8 @@ def _partial_init(cls_or_self, **kwargs): >>> foo_builder = Foo.partial_init(a=3, b=4).partial_init(answer=42) >>> foo_instance1 = foo_builder() >>> foo_instance2 = foo_builder() - >>> id(foo_instance1) == id(foo_instance2) + >>> result = (id(foo_instance1) == id(foo_instance2)) + >>> print(result) False """ @@ -234,7 +235,7 @@ class FakeQuantWithMinMaxObserver(UniformQuantObserver): >>> fake_quant = nn.FakeQuantWithMinMaxObserver() >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32) >>> result = fake_quant(input) - >>> result + >>> print(result) [[0.9882355, 1.9764705, 0.9882355], [-1.9764705, 0. , -0.9882355]] """ @@ -591,7 +592,8 @@ class Conv2dBnFoldQuant(Cell): >>> quant_config=qconfig) >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32) >>> result = conv2d_bnfold(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (2, 6, 2, 2) """ @@ -776,7 +778,8 @@ class Conv2dBnWithoutFoldQuant(Cell): >>> quant_config=qconfig) >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mstype.float32) >>> result = conv2d_no_bnfold(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (2, 6, 2, 2) """ @@ -897,7 +900,8 @@ class Conv2dQuant(Cell): >>> quant_config=qconfig) >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32) >>> result = conv2d_quant(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (2, 6, 2, 2) """ @@ -997,7 +1001,8 @@ class DenseQuant(Cell): >>> dense_quant = nn.DenseQuant(3, 6, quant_config=qconfig) >>> input = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32) >>> result = dense_quant(input) - >>> result.shape + >>> output = result.shape + >>> print(output) (2, 6) """ @@ -1102,7 +1107,7 @@ class ActQuant(_QuantActivation): >>> act_quant = nn.ActQuant(nn.ReLU(), quant_config=qconfig) >>> input = Tensor(np.array([[1, 2, -1], [-2, 0, -1]]), mindspore.float32) >>> result = act_quant(input) - >>> result + >>> print(result) [[0.9882355, 1.9764705, 0.], [0., 0., 0.]] """ @@ -1164,7 +1169,7 @@ class TensorAddQuant(Cell): >>> input_x1 = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32) >>> input_x2 = Tensor(np.ones((2, 3)), mindspore.float32) >>> result = add_quant(input_x1, input_x2) - >>> result + >>> print(result) [[1.9764705, 3.011765, 1.9764705], [-0.9882355, 0.9882355, 0.]] """ @@ -1211,7 +1216,7 @@ class MulQuant(Cell): >>> input_x1 = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32) >>> input_x2 = Tensor(np.ones((2, 3)) * 2, mindspore.float32) >>> result = mul_quant(input_x1, input_x2) - >>> result + >>> print(result) [[1.9764705, 4.0000005, 1.9764705], [-4., 0., -1.9764705]] """