forked from mindspore-Ecosystem/mindspore
Fix api bug for PReLU op.
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@ -349,7 +349,7 @@ class PSNR(Cell):
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Args:
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max_val (Union[int, float]): The dynamic range of the pixel values (255 for 8-bit grayscale images).
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Default: 1.0.
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The value must be greater than 0. Default: 1.0.
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Inputs:
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- **img1** (Tensor) - The first image batch with format 'NCHW'. It must be the same shape and dtype as img2.
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@ -247,12 +247,11 @@ def multinomial(inputs, num_sample, replacement=True, seed=0):
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Args:
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inputs (Tensor): The input tensor containing probabilities, must be 1 or 2 dimensions, with
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float32 data type.
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float32 data type.
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num_sample (int): Number of samples to draw.
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replacement (bool, optional): Whether to draw with replacement or not, default True.
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seed (int, optional): Seed is used as entropy source for the random number engines to generate
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pseudo-random numbers,
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must be non-negative. Default: 0.
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pseudo-random numbers, must be non-negative. Default: 0.
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Outputs:
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Tensor, has the same rows with input. The number of sampled indices of each row is `num_samples`.
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@ -2488,7 +2488,7 @@ class ResizeBilinear(PrimitiveWithInfer):
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Inputs:
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- **input** (Tensor) - Image to be resized. Input images must be a 4-D tensor with shape
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[batch, channels, height, width], with data type of float32 or float16.
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:math:`(batch, channels, height, width)`, with data type of float32 or float16.
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Outputs:
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Tensor, resized image. 4-D with shape [batch, channels, new_height, new_width] in `float32`.
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@ -2701,13 +2701,12 @@ class PReLU(PrimitiveWithInfer):
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>>> weight = Tensor(np.array([0.1, 0.6, -0.3]), mindspore.float32)
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>>> net = Net()
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>>> result = net(input_x, weight)
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[[[-0.1 1. ]
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[ 0. 2. ]
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[0. 0. ]]
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[[-0.2 -0.1 ]
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[2. -1.8000001]
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[0.6 0.6 ]]]
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[[[-0.1, 1.0],
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[0.0, 2.0],
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[0.0, 0.0]],
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[[-0.2, -0.1],
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[2.0, -1.8000001],
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[0.6, 0.6]]]
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"""
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@prim_attr_register
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