some operators issues:
FFTWithSize api description SquareSumAll cn & en inconsistent ctc_greedy_decoder, trunc, population_count api description ctc_greedy_decoder runtime_error to value_error trunc dynamic shape fix
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@ -187,12 +187,8 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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:template: classtemplate.rst
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mindspore.ops.ComputeAccidentalHits
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mindspore.ops.GridSampler2D
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mindspore.ops.GridSampler3D
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mindspore.ops.LogUniformCandidateSampler
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mindspore.ops.UniformCandidateSampler
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mindspore.ops.UpsampleNearest3D
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mindspore.ops.UpsampleTrilinear3D
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图像处理
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^^^^^^^^^^
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@ -1,8 +0,0 @@
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mindspore.ops.GridSampler2D
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===========================
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.. py:class:: mindspore.ops.GridSampler2D(interpolation_mode='bilinear', padding_mode='zeros', align_corners=False)
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给定一个输入和一个网格，使用网格中的输入值和像素位置计算输出。
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更多参考详见 :func:`mindspore.ops.grid_sample`。
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@ -1,8 +0,0 @@
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mindspore.ops.GridSampler3D
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===========================
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.. py:class:: mindspore.ops.GridSampler3D(interpolation_mode='bilinear', padding_mode='zeros', align_corners=False)
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给定一个输入和一个网格，使用网格中的输入值和像素位置计算输出。
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更多参考详见 :func:`mindspore.ops.grid_sample`。
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@ -11,7 +11,7 @@
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\end{matrix}\right.
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.. note::
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SquareSumAll只支持float32和float64类型的输入值。
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SquareSumAll只支持float16和float32类型的输入值。
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输入：
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- **x** (Tensor) - SquareSumAll的输入，其数据类型为数值型，shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。
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@ -1,28 +0,0 @@
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mindspore.ops.UpsampleNearest3D
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===============================
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.. py:class:: mindspore.ops.UpsampleNearest3D(output_size=None, scales=None)
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执行最近邻上采样操作。
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使用指定的 `output_size` 或 `scales` 因子采用最近邻算法对输入进行缩放。其中 `output_size` 或 `scales` 必须给出一个，且不能同时指定。
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参数：
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- **output_size** (Union[tuple[int], list[int]]) - 指定输出卷大小的int型列表。默认值为None。
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- **scales** (Union[tuple[float], list[float]]) - 指定上采样因子的浮点数列表。默认值为None。
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输入：
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- **x** (Tensor) - shape: :math:`(N, C, D_{in}, H_{in}, W_{in})`。
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输出：
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Tensor，shape: :math:`(N, C, D_{out}, H_{out}, W_{out})`，数据类型与输入 `x` 相同。
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异常：
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- **TypeError** - `x` 的维度不为5。
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- **TypeError** - `x` 的数据类型不为float16，float32。
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- **TypeError** - `output_size` 的数据类型不为int型列表。
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- **TypeError** - `scales` 的数据类型不为float型列表。
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- **ValueError** - `output_size` 的类型为列表，其长度不为3。
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- **ValueError** - `scales` 的类型为列表，其长度不为3。
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- **ValueError** - `output_size` 和 `scales` 两者都为None。
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- **ValueError** - `output_size` 和 `scales` 两者都为非空列表。
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@ -1,30 +0,0 @@
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mindspore.ops.UpsampleTrilinear3D
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=================================
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.. py:class:: mindspore.ops.UpsampleTrilinear3D(output_size=None, scales=None, align_corners=False)
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对于5D输入，在3D上用三线性插值进行上采样。
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使用指定的 `output_size` 或 `scales` 因子对容量输入进行缩放。其中 `output_size` 或 `scales` 必须给出一个，且不能同时指定。
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参数：
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- **output_size** (Union[tuple[int], list[int]]) - 指定输出卷大小的int型列表。默认值为None。
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- **scales** (Union[tuple[float], list[float]]) - 指定上采样因子的浮点数列表。默认值为None。
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- **align_corners** (bool) - True表示使用 :math:`(new\_height - 1) / (height - 1)` 进行缩放，使其输入图像与被调整之后的图像四角对齐；False表示使用 :math:`new\_height / height` 进行缩放。 默认值为False。
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输入：
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- **x** (Tensor) - shape: :math:`(N, C, D_{in}, H_{in}, W_{in})`。
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输出：
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Tensor，shape: :math:`(N, C, D_{out}, H_{out}, W_{out})`，数据类型与输入 `x` 相同。
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异常：
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- **TypeError** - `x` 的维度不为5。
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- **TypeError** - `x` 的数据类型不为float16，float32。
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- **TypeError** - `output_size` 的数据类型不为int型列表。
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- **TypeError** - `scales` 的数据类型不为float型列表。
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- **TypeError** - `align_corners` 不是一个布尔值。
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- **ValueError** - `output_size` 的类型为列表，其长度不为3。
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- **ValueError** - `scales` 的类型为列表，其长度不为3。
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- **ValueError** - `output_size` 和 `scales` 两者都为None。
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- **ValueError** - `output_size` 和 `scales` 两者都为非空列表。
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@ -6,11 +6,11 @@ mindspore.ops.population_count
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计算二进制数中1的个数。
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参数：
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- **input_x** (Tensor) - 任意维度的Tensor。Ascend平台支持的数据类型为int16、uint16，CPU平台支持的数据类型为int8、int16、int32、int64、uint8、uint16、uint32、uint64。
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- **input_x** (Tensor) - 任意维度的Tensor。Ascend平台支持的数据类型为int16、uint16，CPU和GPU平台支持的数据类型为int8、int16、int32、int64、uint8、uint16、uint32、uint64。
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返回：
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Tensor，shape与 `input_x` 相同，数据类型为uint8。
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异常：
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- **TypeError** - `input_x` 不是Tensor。
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- **TypeError** - `input_x` 的数据类型不是int16或uint16（Ascend平台）。`input` 的数据类型不是int8、int16、int32、int64、uint8、uint16、uint32、uint64（CPU平台）。
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- **TypeError** - `input_x` 的数据类型不是int16或uint16（Ascend平台）。`input` 的数据类型不是int8、int16、int32、int64、uint8、uint16、uint32、uint64（CPU和GPU平台）。
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@ -186,12 +186,8 @@ Sampling Operator
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:template: classtemplate.rst
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mindspore.ops.ComputeAccidentalHits
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mindspore.ops.GridSampler2D
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mindspore.ops.GridSampler3D
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mindspore.ops.LogUniformCandidateSampler
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mindspore.ops.UniformCandidateSampler
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mindspore.ops.UpsampleNearest3D
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mindspore.ops.UpsampleTrilinear3D
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Image Processing
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^^^^^^^^^^^^^^^^
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@ -45,12 +45,23 @@ bool TruncCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::ve
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const std::vector<KernelTensorPtr> &outputs) {
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MS_EXCEPTION_IF_NULL(base_operator);
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kernel_name_ = base_operator->name();
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auto input_shape = inputs[kZero]->GetShapeVector();
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input_size_ = std::accumulate(input_shape.begin(), input_shape.end(), size_t(1), std::multiplies<size_t>());
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dtype_ = inputs[kZero]->GetDtype();
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return true;
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}
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int TruncCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs,
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const std::map<uint32_t, tensor::TensorPtr> &) {
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if (int ret = KernelMod::Resize(base_operator, inputs, outputs); ret != KRET_OK) {
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return ret;
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}
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auto input_shape = inputs[kZero]->GetShapeVector();
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input_size_ = std::accumulate(input_shape.begin(), input_shape.end(), size_t(1), std::multiplies<size_t>());
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return KRET_OK;
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}
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bool TruncCpuKernelMod::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> &workspace,
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const std::vector<kernel::AddressPtr> &outputs) {
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@ -20,6 +20,7 @@
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#include <vector>
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#include <utility>
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#include <memory>
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#include <map>
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#include "mindspore/core/ops/trunc.h"
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#include "plugin/device/cpu/kernel/cpu_kernel.h"
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#include "plugin/factory/ms_factory.h"
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@ -34,6 +35,9 @@ class TruncCpuKernelMod : public NativeCpuKernelMod {
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bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) override;
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int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) override;
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@ -84,19 +84,20 @@ AbstractBasePtr CTCGreedyDecoderInfer(const abstract::AnalysisEnginePtr &, const
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}
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if (inputs_x_shape.size() != kInputsRank) {
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MS_LOG(EXCEPTION) << "For '" << prim_name << "', inputs's dim must be 3, but got: " << inputs_x_shape.size() << ".";
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MS_EXCEPTION(ValueError) << "For '" << prim_name << "', inputs's dim must be 3, but got: " << inputs_x_shape.size()
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<< ".";
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}
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if (sequence_length_shape.size() != kSeqLenRank) {
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MS_LOG(EXCEPTION) << "For '" << prim_name
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<< "', sequence_length's dims must be 1, but got: " << sequence_length_shape.size() << ".";
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MS_EXCEPTION(ValueError) << "For '" << prim_name
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<< "', sequence_length's dims must be 1, but got: " << sequence_length_shape.size() << ".";
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}
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if (inputs_x_shape[1] != sequence_length_shape[0]) {
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MS_LOG(EXCEPTION) << "For '" << prim_name
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<< "', inputs batch_size must be the same with sequence_length batch_size, "
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<< "but now inputs batch_size: " << inputs_x_shape[1]
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<< " and sequence_length batch_size: " << sequence_length_shape[0] << ".";
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MS_EXCEPTION(ValueError) << "For '" << prim_name
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<< "', inputs batch_size must be the same with sequence_length batch_size, "
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<< "but now inputs batch_size: " << inputs_x_shape[1]
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<< " and sequence_length batch_size: " << sequence_length_shape[0] << ".";
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}
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return std::make_shared<abstract::AbstractTuple>(ret);
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@ -32,7 +32,7 @@ abstract::ShapePtr UpsampleNearest3DInferShape(const PrimitivePtr &primitive,
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const std::vector<AbstractBasePtr> &input_args) {
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auto prim_name = primitive->name();
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auto x_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[kInputIndex0]->BuildShape())[kShape];
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auto x_shape_ptr = input_args[kInputIndex0]->BuildShape();
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(void)CheckAndConvertUtils::CheckInteger("dimension of x", SizeToLong(x_shape.size()), kEqual, SizeToLong(kDim5),
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prim_name);
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@ -68,6 +68,9 @@ abstract::ShapePtr UpsampleNearest3DInferShape(const PrimitivePtr &primitive,
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<< " But get both.";
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}
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if (x_shape_ptr->IsDynamic()) {
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return std::make_shared<abstract::Shape>(y_shape);
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}
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for (size_t i = 0; i < y_shape.size(); i++) {
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(void)CheckAndConvertUtils::CheckInteger("output shape", y_shape[i], kGreaterThan, 0, prim_name);
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}
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@ -255,7 +255,7 @@ from .math_func import (
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approximate_equal,
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frac,
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kron,
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rot90
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rot90,
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)
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from .nn_func import (
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adaptive_avg_pool2d,
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@ -3538,15 +3538,14 @@ def population_count(input_x):
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Raises:
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TypeError: If `input_x` is not a Tensor.
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TypeError: If dtype of `input_x` is not int16, uint16 (Ascend).
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If dtype of `input_x` is not int8, int16, int32, int64, uint8, uint16, uint32, uint64 (CPU).
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If dtype of `input_x` is not int8, int16, int32, int64, uint8, uint16, uint32, uint64 (CPU and GPU).
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x_input = Tensor([0, 1, 3], mindspore.int16)
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>>> population_count = ops.PopulationCount()
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>>> output = population_count(x_input)
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>>> input_x = Tensor([0, 1, 3], mindspore.int16)
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>>> output = ops.population_count(input_x)
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>>> print(output)
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[0 1 2]
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"""
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@ -2340,7 +2340,7 @@ def trunc(x):
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Returns a new tensor with the truncated integer values of the elements of input.
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Args:
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- **x** (Tensor) - Input_x is a tensor.
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- **input_x** (Tensor) - input_x is a tensor.
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Returns:
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Tensor, the same shape and data type as the input.
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@ -2352,9 +2352,8 @@ def trunc(x):
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``Ascend`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([3.4742, 0.5466, -0.8008, -3.9079]),mindspore.float32)
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>>> trunc = ops.Trunc()
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>>> output = trunc(x)
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>>> input_x = Tensor(np.array([3.4742, 0.5466, -0.8008, -3.9079]),mindspore.float32)
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>>> output = ops.trunc(input_x)
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>>> print(output)
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[ 3. 0. 0. -3.]
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"""
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@ -5402,6 +5401,6 @@ __all__ = [
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'approximate_equal',
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'frac',
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'kron',
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'rot90'
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'rot90',
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]
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__all__.sort()
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@ -1819,7 +1819,7 @@ def grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zero
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Examples:
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>>> input_x = Tensor(np.arange(16).reshape((2, 2, 2, 2)).astype(np.float32))
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>>> grid = Tensor(np.arange(0.2, 1, 0.1).reshape((2, 2, 1, 2)).astype(np.float32))
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>>> output = grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
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>>> output = ops.grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zeros',
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align_corners=True)
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>>> print(output)
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[[[[ 1.9 ]
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@ -1943,7 +1943,8 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
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>>> inputs = Tensor(np.array([[[0.6, 0.4, 0.2], [0.8, 0.6, 0.3]],
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... [[0.0, 0.6, 0.0], [0.5, 0.4, 0.5]]]), mindspore.float32)
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>>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
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>>> decoded_indices, decoded_values, decoded_shape, log_probability = ctc_greedy_decode(inputs, sequence_length)
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>>> decoded_indices, decoded_values, decoded_shape, log_probability = ops.ctc_greedy_decoder(inputs,
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sequence_length)
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>>> print(decoded_indices)
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[[0 0]
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[0 1]
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@ -5722,23 +5722,7 @@ class Trunc(Primitive):
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"""
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Returns a new tensor with the truncated integer values of the elements of input.
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Inputs:
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- **input_x** (Tensor) - Input_x is a tensor.
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Outputs:
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Tensor, the same shape and data type as the input.
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Raises:
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TypeError: If `input_x` is not a Tensor.
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Supported Platforms:
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``Ascend`` ``CPU`` ``GPU``
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Examples:
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>>> trunc = ops.Trunc()
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>>> output = trunc(Tensor(np.array([3.4742, 0.5466, -0.8008, -3.9079]),mindspore.float32))
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>>> print(output)
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[ 3. 0. 0. -3.]
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Refer to :func:`mindspore.ops.trunc` for more detail.
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"""
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@prim_attr_register
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@ -6685,17 +6669,19 @@ class FFTWithSize(Primitive):
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IRFFT requires complex64 or complex128 inputs, return float32 or float64 outputs.
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Args:
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signal_ndim(int): The number of dimensions in each signal, this controls how many dimensions of the fourier
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transform are realized, can only be 1, 2 or 3.
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inverse(bool): Whether it is the inverse transformation, used to select FFT or IFFT and RFFT or IRFFT.
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real(bool): Whether it is the real transformation, used to select FFT/IFFT or RFFT/IRFFT.
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norm(str): The default normalization ("backward") has the direct (forward) transforms unscaled
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signal_ndim (int): The number of dimensions in each signal, this controls how many dimensions of the fourier
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transform are realized, can only be 1, 2 or 3.
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inverse (bool): Whether it is the inverse transformation, used to select FFT or IFFT and RFFT or IRFFT.
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inverse=False means FFT or RFFT, inverse=True means IFFT or IRFFT.
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real (bool): Whether it is the real transformation, used to select FFT/IFFT or RFFT/IRFFT.
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real=False means FFT or IFFT, real=True means RFFT or IRFFT.
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norm (str): The default normalization ("backward") has the direct (forward) transforms unscaled
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and the inverse (backward) transforms scaled by 1/n.
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"ortho" has both direct and inverse transforms are scaled by 1/sqrt(n).
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"forward" has the direct transforms scaled by 1/n and the inverse transforms unscaled.
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n is the input x's element numbers.
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onesided(bool): Controls whether the input is halved to avoid redundancy. Default: True.
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signal_sizes(list): Size of the original signal (the signal before rfft, no batch dimension),
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onesided (bool): Controls whether the input is halved to avoid redundancy. Default: True.
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signal_sizes (list): Size of the original signal (the signal before rfft, no batch dimension),
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only in irfft mode and set onesided=true requires the parameter. Default: [].
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|
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Inputs:
|
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|
|
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|||
|
|
@ -7228,52 +7228,7 @@ class CTCGreedyDecoder(Primitive):
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r"""
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Performs greedy decoding on the logits given in inputs.
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|
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Args:
|
||||
merge_repeated (bool): If true, merge repeated classes in output. Default: True.
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|
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Inputs:
|
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- **inputs** (Tensor) - The input Tensor must be a 3-D tensor whose shape is
|
||||
:math:`(max\_time, batch\_size, num\_classes)`. `num_classes` must be `num_labels + 1` classes,
|
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`num_labels` indicates the number of actual labels. Blank labels are reserved.
|
||||
Default blank label is `num_classes - 1`. Data type must be float32 or float64.
|
||||
- **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of :math:`(batch\_size, )`.
|
||||
The type must be int32. Each value in the tensor must be equal to or less than `max_time`.
|
||||
|
||||
Outputs:
|
||||
- **decoded_indices** (Tensor) - A tensor with shape of :math:`(total\_decoded\_outputs, 2)`.
|
||||
Data type is int64.
|
||||
- **decoded_values** (Tensor) - A tensor with shape of :math:`(total\_decoded\_outputs, )`,
|
||||
it stores the decoded classes. Data type is int64.
|
||||
- **decoded_shape** (Tensor) - A tensor with shape of :math:`(batch\_size, max\_decoded\_legth)`.
|
||||
Data type is int64.
|
||||
- **log_probability** (Tensor) - A tensor with shape of :math:`(batch\_size, 1)`,
|
||||
containing sequence log-probability, has the same type as `inputs`.
|
||||
|
||||
Raises:
|
||||
TypeError: If `merge_repeated` is not a bool.
|
||||
ValueError: If length of shape of `inputs` is not equal to 3.
|
||||
ValueError: If length of shape of `sequence_length` is not equal to 1.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend``
|
||||
|
||||
Examples:
|
||||
>>> inputs = Tensor(np.array([[[0.6, 0.4, 0.2], [0.8, 0.6, 0.3]],
|
||||
... [[0.0, 0.6, 0.0], [0.5, 0.4, 0.5]]]), mindspore.float32)
|
||||
>>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)
|
||||
>>> ctc_greedyDecoder = ops.CTCGreedyDecoder()
|
||||
>>> decoded_indices, decoded_values, decoded_shape, log_probability = ctc_greedyDecoder(inputs, sequence_length)
|
||||
>>> print(decoded_indices)
|
||||
[[0 0]
|
||||
[0 1]
|
||||
[1 0]]
|
||||
>>> print(decoded_values)
|
||||
[0 1 0]
|
||||
>>> print(decoded_shape)
|
||||
[2 2]
|
||||
>>> print(log_probability)
|
||||
[[-1.2]
|
||||
[-1.3]]
|
||||
Refer to :func:`mindspore.ops.ctc_greedy_decoder` for more detail.
|
||||
"""
|
||||
|
||||
@prim_attr_register
|
||||
|
|
|
|||
Loading…
Reference in New Issue