forked from mindspore-Ecosystem/mindspore
!44882 fix: doc review MD
Merge pull request !44882 from guozhijian/fix_doc_review
This commit is contained in:
Gitee
commit
b412570b7b
|
|
@ -3,14 +3,13 @@ mindspore.dataset.Dataset.zip
|
|||
|
||||
.. py:method:: mindspore.dataset.Dataset.zip(datasets)
|
||||
|
||||
将多个dataset对象按列进行合并压缩。
|
||||
将多个dataset对象按列进行合并压缩，多个dataset对象不能有相同的列名。
|
||||
|
||||
参数：
|
||||
- **datasets** (tuple[Dataset]) - 要合并的多个dataset对象。 `datasets` 参数的长度必须大于1。
|
||||
- **datasets** (tuple[Dataset]) - 要合并的（多个）dataset对象。
|
||||
|
||||
返回：
|
||||
ZipDataset，合并后的dataset对象。
|
||||
|
||||
异常：
|
||||
- **ValueError** - `datasets` 参数的长度为1。
|
||||
- **TypeError** - `datasets` 参数不是tuple。
|
||||
- **TypeError** - `datasets` 参数不是dataset对象/tuple(dataset)。
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ mindspore.dataset.WikiTextDataset
|
|||
|
||||
.. py:class:: mindspore.dataset.WikiTextDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)
|
||||
|
||||
读取和解析WikiText2和WikiText103数据集的源数据集。
|
||||
读取和解析WikiText2和WikiText103数据集。
|
||||
|
||||
生成的数据集有一列 `[text]` ，数据类型为string。
|
||||
|
||||
|
|
@ -22,6 +22,14 @@ mindspore.dataset.WikiTextDataset
|
|||
- **num_shards** (int, 可选) - 指定分布式训练时将数据集进行划分的分片数，默认值：None。指定此参数后， `num_samples` 表示每个分片的最大样本数。
|
||||
- **shard_id** (int, 可选) - 指定分布式训练时使用的分片ID号，默认值：None。只有当指定了 `num_shards` 时才能指定此参数。
|
||||
- **cache** (DatasetCache, 可选) - 单节点数据缓存服务，用于加快数据集处理，详情请阅读 `单节点数据缓存 <https://www.mindspore.cn/tutorials/experts/zh-CN/master/dataset/cache.html>`_ 。默认值：None，不使用缓存。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `dataset_dir` 参数所指向的文件目录不存在或缺少数据集文件。
|
||||
- **ValueError** - `num_samples` 参数值错误（小于0）。
|
||||
- **ValueError** - `num_parallel_workers` 参数超过系统最大线程数。
|
||||
- **RuntimeError** - 指定了 `num_shards` 参数，但是未指定 `shard_id` 参数。
|
||||
- **RuntimeError** - 指定了 `shard_id` 参数，但是未指定 `num_shards` 参数。
|
||||
- **ValueError** - `shard_id` 参数值错误（小于0或者大于等于 `num_shards` ）。
|
||||
|
||||
**关于WikiText数据集：**
|
||||
|
||||
|
|
|
|||
|
|
@ -8,6 +8,8 @@ mindspore.dataset.audio.ComputeDeltas
|
|||
.. math::
|
||||
d_{t}=\frac{{\textstyle\sum_{n=1}^{N}}n(c_{t+n}-c_{t-n})}{2{\textstyle\sum_{n=1}^{N}}n^{2}}
|
||||
|
||||
其中， :math:`d_{t}` 是时间 :math:`t` 的增量， :math:`c_{t}` 是时间 :math:`t` 的频谱图系数， :math:`N` 是 :math:`(\text{win_length}-1)//2` 。
|
||||
|
||||
参数：
|
||||
- **win_length** (int, 可选) - 计算窗口长度，长度必须不小于3，默认值：5。
|
||||
- **pad_mode** (:class:`mindspore.dataset.audio.BorderType`, 可选) - 边界填充模式，可以是
|
||||
|
|
|
|||
|
|
@ -7,6 +7,11 @@ mindspore.dataset.audio.MaskAlongAxis
|
|||
|
||||
参数：
|
||||
- **mask_start** (int) - 掩码的起始位置，必须是非负的。
|
||||
- **mask_width** (int) - 掩码的宽度，必须是非负的。
|
||||
- **mask_width** (int) - 掩码的宽度，必须是大于0。
|
||||
- **mask_value** (float) - 掩码值。
|
||||
- **axis** (int) - 要应用掩码的轴（1表示频率，2表示时间）。
|
||||
|
||||
异常：
|
||||
- **ValueError** - `mask_start` 参数值错误（小于0）。
|
||||
- **ValueError** - `mask_width` 参数值错误（小于1）。
|
||||
- **ValueError** - `axis` 参数类型错误或者值错误，不属于 [1, 2]。
|
||||
|
|
|
|||
|
|
@ -10,3 +10,7 @@ mindspore.dataset.audio.MaskAlongAxisIID
|
|||
- **mask_param** (int) - 要屏蔽的列数，将从[0, mask_param]统一采样，必须是非负数。
|
||||
- **mask_value** (float) - 掩码值。
|
||||
- **axis** (int) - 要应用掩码的轴（1表示频率，2表示时间）。
|
||||
|
||||
异常：
|
||||
- **ValueError** - `mask_param` 参数值错误（小于0）。
|
||||
- **ValueError** - `axis` 参数类型错误或者值错误，不属于 [1, 2]。
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ mindspore.dataset.audio.SlidingWindowCmn
|
|||
|
||||
.. py:class:: mindspore.dataset.audio.SlidingWindowCmn(cmn_window=600, min_cmn_window=100, center=False, norm_vars=False)
|
||||
|
||||
应用滑动窗口倒谱平均值（和可选的方差）标准化每个对话语句。
|
||||
对每个话语应用滑动窗口倒谱均值（和可选方差）归一化。
|
||||
|
||||
参数：
|
||||
- **cmn_window** (int, 可选) - 用于运行平均CMN计算的帧中窗口，默认值：600。
|
||||
|
|
@ -11,4 +11,4 @@ mindspore.dataset.audio.SlidingWindowCmn
|
|||
仅在中心为False时适用，在中心为True时忽略，默认值：100。
|
||||
- **center** (bool, 可选) - 如果为True，则使用以当前帧为中心的窗口。如果为False，则窗口在左侧。默认值：False。
|
||||
- **norm_vars** (bool, 可选) - 如果为True，则将方差规范化为1。默认值：False。
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ mindspore.dataset.audio.WindowType
|
|||
|
||||
.. py:class:: mindspore.dataset.audio.WindowType
|
||||
|
||||
窗口函数类型，
|
||||
窗口函数类型。
|
||||
|
||||
可选的枚举值包括：WindowType.BARTTT、WindowType.BACKMAN、WindowType.HAMMING、WindowType.HANN、WindowType.KAISER。
|
||||
|
||||
|
|
@ -12,4 +12,4 @@ mindspore.dataset.audio.WindowType
|
|||
- **WindowType.HAMMING** - 表示窗口函数的类型为Hamming。
|
||||
- **WindowType.HANN** - 表示窗口函数的类型为Hann。
|
||||
- **WindowType.KAISER** - 表示窗口函数的类型为Kaiser，目前在macOS上不支持。
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,17 +3,22 @@
|
|||
|
||||
.. py:class:: mindspore.dataset.text.CharNGram
|
||||
|
||||
CharNGram对象，用于将tokens映射到预训练的向量中。
|
||||
CharNGram对象，用于将 `tokens` 映射到预训练的向量中。
|
||||
|
||||
.. py:method:: from_file(file_path, max_vectors=None)
|
||||
|
||||
从文件构建CharNGram向量。
|
||||
从文件构建 `CharNGram` 向量。
|
||||
|
||||
参数：
|
||||
- **file_path** (str) - 包含CharNGram向量的文件路径。
|
||||
- **file_path** (str) - 包含 `CharNGram` 向量的文件路径。
|
||||
- **max_vectors** (int，可选) - 用于限制加载的预训练向量的数量。
|
||||
大多数预训练的向量集是按词频降序排序的。因此，在如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
大多数预训练的向量集是按词频降序排序的。因此，如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
可以传递 `max_vectors` 限制加载数量。默认值：None，无限制。
|
||||
|
||||
返回：
|
||||
CharNGram， 根据文件构建的CharNGram向量。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `file_path` 参数所指向的文件非法或者包含的数据异常。
|
||||
- **ValueError** - `max_vectors` 参数值错误。
|
||||
- **TypeError** - `max_vectors` 参数不是整数类型。
|
||||
|
|
|
|||
|
|
@ -3,17 +3,22 @@
|
|||
|
||||
.. py:class:: mindspore.dataset.text.FastText
|
||||
|
||||
用于将tokens映射到矢量的FastText对象。
|
||||
用于将tokens映射到向量的FastText对象。
|
||||
|
||||
.. py:method:: from_file(file_path, max_vectors=None)
|
||||
|
||||
从文件构建FastText向量。
|
||||
|
||||
参数：
|
||||
- **file_path** (str) - 包含向量的文件的路径。预训练向量集的文件后缀必须是 `*.vec` 。
|
||||
- **file_path** (str) - 包含向量的文件路径。预训练向量集的文件后缀必须是 `*.vec` 。
|
||||
- **max_vectors** (int，可选) - 用于限制加载的预训练向量的数量。
|
||||
大多数预训练的向量集是按词频降序排序的。因此，在如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
大多数预训练的向量集是按词频降序排序的。因此，如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
可以传递 `max_vectors` 限制加载数量。默认值：None，无限制。
|
||||
|
||||
返回：
|
||||
FastText， 根据文件构建的FastText向量。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `file_path` 参数所指向的文件非法或者包含的数据异常。
|
||||
- **ValueError** - `max_vectors` 参数值错误。
|
||||
- **TypeError** - `max_vectors` 参数不是整数类型。
|
||||
|
|
|
|||
|
|
@ -3,17 +3,22 @@
|
|||
|
||||
.. py:class:: mindspore.dataset.text.GloVe
|
||||
|
||||
用于将tokens映射到矢量的GloVe对象。
|
||||
用于将tokens映射到向量的GloVe对象。
|
||||
|
||||
.. py:method:: from_file(file_path, max_vectors=None)
|
||||
|
||||
从文件构建CharNGram向量。
|
||||
|
||||
参数：
|
||||
- **file_path** (str) - 包含向量的文件的路径。预训练向量集的格式必须是 `glove.6B.*.txt` 。
|
||||
- **file_path** (str) - 包含向量的文件路径。预训练向量集的格式必须是 `glove.6B.*.txt` 。
|
||||
- **max_vectors** (int，可选) - 用于限制加载的预训练向量的数量。
|
||||
大多数预训练的向量集是按词频降序排序的。因此，在如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
大多数预训练的向量集是按词频降序排序的。因此，如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
可以传递 `max_vectors` 限制加载数量。默认值：None，无限制。
|
||||
|
||||
返回：
|
||||
GloVe， 根据文件构建的GloVe向量。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `file_path` 参数所指向的文件非法或者包含的数据异常。
|
||||
- **ValueError** - `max_vectors` 参数值错误。
|
||||
- **TypeError** - `max_vectors` 参数不是整数类型。
|
||||
|
|
|
|||
|
|
@ -6,10 +6,10 @@ mindspore.dataset.text.ToVectors
|
|||
根据输入向量表查找向量中的tokens。
|
||||
|
||||
参数：
|
||||
- **vectors** (Vectors) - 矢量对象。
|
||||
- **unk_init** (sequence, 可选) - 用于初始化矢量外（OOV）令牌的序列，默认值：None，用零向量初始化。
|
||||
- **vectors** (Vectors) - 向量对象。
|
||||
- **unk_init** (sequence, 可选) - 用于初始化向量外（OOV）令牌的序列，默认值：None，用零向量初始化。
|
||||
- **lower_case_backup** (bool, 可选) - 是否查找小写的token。如果为False，则将查找原始大小写中的每个token。
|
||||
如果为True，则将首先查找原始大小写中的每个token，如果在属性soi的键中找不到，则将查找小写中的token。默认值：False。
|
||||
如果为True，则将首先查找原始大小写中的每个token，如果在属性stoi（字符->索引映射）的键中找不到，则将查找小写中的token。默认值：False。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `unk_init` 不是序列。
|
||||
|
|
|
|||
|
|
@ -3,17 +3,22 @@
|
|||
|
||||
.. py:class:: mindspore.dataset.text.Vectors
|
||||
|
||||
用于将tokens映射到矢量的Vectors对象。
|
||||
用于将tokens映射到向量的Vectors对象。
|
||||
|
||||
.. py:method:: from_file(file_path, max_vectors=None)
|
||||
|
||||
从文件构建Vectors向量。
|
||||
|
||||
参数：
|
||||
- **file_path** (str) - 包含向量的文件的路径。
|
||||
- **file_path** (str) - 包含向量的文件路径。
|
||||
- **max_vectors** (int，可选) - 用于限制加载的预训练向量的数量。
|
||||
大多数预训练的向量集是按词频降序排序的。因此，在如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
大多数预训练的向量集是按词频降序排序的。因此，如果内存不能存放整个向量集，或者由于其他原因不需要，
|
||||
可以传递 `max_vectors` 限制加载数量。默认值：None，无限制。
|
||||
|
||||
返回：
|
||||
Vectors， 根据文件构建的Vectors向量。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `file_path` 参数所指向的文件非法或者包含的数据异常。
|
||||
- **ValueError** - `max_vectors` 参数值错误。
|
||||
- **TypeError** - `max_vectors` 参数不是整数类型。
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ mindspore.dataset.transforms.RandomApply
|
|||
|
||||
参数：
|
||||
- **transforms** (list) - 一个数据增强的列表。
|
||||
- **prob** (float, 可选) - 随机应用某个数据增强的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 随机应用某个数据增强的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 参数 `transforms` 类型不为list。
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ mindspore.dataset.vision.AdjustGamma
|
|||
|
||||
参数：
|
||||
- **gamma** (float) - 输出图像像素值与输入图像像素值呈指数相关。 `gamma` 大于1使阴影更暗，而 `gamma` 小于1使黑暗区域更亮。
|
||||
- **gain** (float, 可选) - 常数乘数，默认值：1。
|
||||
- **gain** (float, 可选) - 常数乘数，默认值：1.0。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `gain` 不是浮点类型。
|
||||
|
|
|
|||
|
|
@ -22,14 +22,14 @@ mindspore.dataset.vision.AutoAugment
|
|||
- **Inter.NEAREST**：表示插值方法是最近邻插值。
|
||||
- **Inter.BILINEAR**：表示插值方法是双线性插值。
|
||||
- **Inter.BICUBIC**：表示插值方法为双三次插值。
|
||||
- **Inter.AREA**：表示插值方法为面积插值。
|
||||
- **Inter.AREA**：表示插值方法为像素区域插值。
|
||||
|
||||
- **fill_value** (Union[int, tuple[int]], 可选) - 填充的像素值。
|
||||
如果是3元素元组，则分别用于填充R、G、B通道。
|
||||
如果是整数，则用于所有 RGB 通道。 `fill_value` 值必须在 [0, 255] 范围内，默认值：0。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `policy` 不是AutoAugmentPolicy类型。
|
||||
- **TypeError** - 如果 `interpolation` 不是Inter类型。
|
||||
- **TypeError** - 如果 `policy` 不是 :class:`mindspore.dataset.vision.AutoAugmentPolicy` 类型。
|
||||
- **TypeError** - 如果 `interpolation` 不是 :class:`mindsore.dataset.vision.Inter` 类型。
|
||||
- **TypeError** - 如果 `fill_value` 不是整数或长度为3的元组。
|
||||
- **RuntimeError** - 如果给定的张量形状不是<H, W, C>。
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ mindspore.dataset.vision.CutMixBatch
|
|||
参数：
|
||||
- **image_batch_format** (ImageBatchFormat) - 图像批处理输出格式。可以是 [ImageBatchFormat.NHWC、ImageBatchFormat.NCHW] 中的任何一个。
|
||||
- **alpha** (float, 可选) - β分布的超参数，必须大于0，默认值：1.0。
|
||||
- **prob** (float, 可选) - 对每个图像应用剪切混合处理的概率，范围：[0.0, 1.0]，默认值：1.0。
|
||||
- **prob** (float, 可选) - 对每个图像应用剪切混合处理的概率，取值范围：[0.0, 1.0]，默认值：1.0。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `image_batch_format` 不是 :class:`mindspore.dataset.vision.ImageBatchFormat` 的类型。
|
||||
|
|
|
|||
|
|
@ -8,11 +8,11 @@ mindspore.dataset.vision.RandomAdjustSharpness
|
|||
参数：
|
||||
- **degree** (float) - 锐度调整度，必须是非负的。
|
||||
0.0度表示模糊图像，1.0度表示原始图像，2.0度表示清晰度增加2倍。
|
||||
- **prob** (float, 可选) - 图像被锐化的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被锐化的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `degree` 的类型不为float。
|
||||
- **TypeError** - 如果 `prob` 的类型不为bool。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **TypeError** - 如果 `prob` 的类型不为float。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **ValueError** - 如果 `degree` 为负数。
|
||||
- **RuntimeError** -如果给定的张量形状不是<H, W>或<H, W, C>。
|
||||
|
|
|
|||
|
|
@ -8,13 +8,13 @@ mindspore.dataset.vision.RandomAutoContrast
|
|||
参数：
|
||||
- **cutoff** (float, 可选) - 输入图像直方图中最亮和最暗像素的百分比。该值必须在 [0.0, 50.0) 范围内，默认值：0.0。
|
||||
- **ignore** (Union[int, sequence], 可选) - 要忽略的背景像素值，忽略值必须在 [0, 255] 范围内，默认值：None。
|
||||
- **prob** (float, 可选) - 图像被调整对比度的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被调整对比度的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `cutoff` 不是float类型。
|
||||
- **TypeError** - 如果 `ignore` 不是int或sequence类型。
|
||||
- **TypeError** - 如果 `prob` 的类型不为bool。
|
||||
- **TypeError** - 如果 `prob` 的类型不为float。
|
||||
- **ValueError** - 如果 `cutoff` 不在[0, 50.0) 范围内。
|
||||
- **ValueError** - 如果 `ignore` 不在[0, 255] 范围内。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W> 或 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomEqualize
|
|||
以给定的概率随机对输入图像进行直方图均衡化。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被均衡化的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被均衡化的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 的类型不为bool。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **TypeError** - 如果 `prob` 的类型不为float。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W> 或 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ mindspore.dataset.vision.RandomErasing
|
|||
请参阅论文 `Random Erasing Data Augmentation <https://arxiv.org/pdf/1708.04896.pdf>`_。
|
||||
|
||||
参数：
|
||||
- **prob** (float，可选) - 执行随机擦除的概率，默认值：0.5。
|
||||
- **prob** (float，可选) - 执行随机擦除的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
- **scale** (Sequence[float, float]，可选) - 擦除区域面积相对原图比例的随机选取范围，按照(min, max)顺序排列，默认值：(0.02, 0.33)。
|
||||
- **ratio** (Sequence[float, float]，可选) - 擦除区域宽高比的随机选取范围，按照(min, max)顺序排列，默认值：(0.3, 3.3)。
|
||||
- **value** (Union[int, str, Sequence[int, int, int]]) - 擦除区域的像素填充值。若输入int，将以该值填充RGB通道；若输入Sequence[int, int, int]，将分别用于填充R、G、B通道；若输入字符串'random'，将以从标准正态分布获得的随机值擦除各个像素。默认值：0。
|
||||
|
|
@ -22,7 +22,7 @@ mindspore.dataset.vision.RandomErasing
|
|||
- **TypeError** - 当 `value` 的类型不为int、str或Sequence[int, int, int]。
|
||||
- **TypeError** - 当 `inplace` 的类型不为bool。
|
||||
- **TypeError** - 当 `max_attempts` 的类型不为int。
|
||||
- **ValueError** - 当 `prob` 取值不在[0, 1]范围内。
|
||||
- **ValueError** - 当 `prob` 取值不在[0.0, 1.0]范围内。
|
||||
- **ValueError** - 当 `scale` 为负数。
|
||||
- **ValueError** - 当 `ratio` 为负数。
|
||||
- **ValueError** - 当 `value` 取值不在[0, 255]范围内。
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ mindspore.dataset.vision.RandomGrayscale
|
|||
按照指定的概率将输入PIL图像转换为灰度图。
|
||||
|
||||
参数：
|
||||
- **prob** (float，可选) - 执行灰度转换的概率，默认值：0.1。
|
||||
- **prob** (float，可选) - 执行灰度转换的概率，取值范围：[0.0, 1.0]。默认值：0.1。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 当 `prob` 的类型不为float。
|
||||
- **ValueError** - 当 `prob` 取值不在[0, 1]范围内。
|
||||
- **ValueError** - 当 `prob` 取值不在[0.0, 1.0]范围内。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomHorizontalFlip
|
|||
对输入图像按给定的概率进行水平随机翻转。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，必须在 [0, 1] 范围内，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 不是float类型。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围内。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围内。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W> 或 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomHorizontalFlipWithBBox
|
|||
对输入图像按给定的概率进行水平随机翻转并相应地调整边界框。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，必须在 [0, 1] 范围内，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 不是float类型。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围内。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围内。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W> 或 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomInvert
|
|||
以给定的概率随机反转图像的颜色。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被反转颜色的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被反转颜色的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 的类型不为bool。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **TypeError** - 如果 `prob` 的类型不为float。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -9,6 +9,6 @@ mindspore.dataset.vision.RandomLighting
|
|||
- **alpha** (float, 可选) - 图像的强度，必须是非负的。默认值：0.05。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `alpha` 的类型不为bool。
|
||||
- **TypeError** - 如果 `alpha` 的类型不为float。
|
||||
- **ValueError** - 如果 `alpha` 为负数。
|
||||
- **RuntimeError** - 如果输入图像的shape不是 <H, W, C>。
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ mindspore.dataset.vision.RandomPerspective
|
|||
按照指定的概率对输入PIL图像进行透视变换。
|
||||
|
||||
参数：
|
||||
- **distortion_scale** (float，可选) - 失真程度，取值范围为[0, 1]，默认值：0.5。
|
||||
- **prob** (float，可选) - 执行透视变换的概率，默认值：0.5。
|
||||
- **distortion_scale** (float，可选) - 失真程度，取值范围为[0.0, 1.0]，默认值：0.5。
|
||||
- **prob** (float，可选) - 执行透视变换的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
- **interpolation** (Inter，可选) - 插值方式，取值可为 Inter.BILINEAR、Inter.NEAREST 或 Inter.BICUBIC。默认值：Inter.BICUBIC。
|
||||
|
||||
- **Inter.BILINEAR**：双线性插值。
|
||||
|
|
@ -18,5 +18,5 @@ mindspore.dataset.vision.RandomPerspective
|
|||
- **TypeError** - 当 `distortion_scale` 的类型不为float。
|
||||
- **TypeError** - 当 `prob` 的类型不为float。
|
||||
- **TypeError** - 当 `interpolation` 的类型不为 :class:`mindspore.dataset.vision.Inter` 。
|
||||
- **ValueError** - 当 `distortion_scale` 取值不在[0, 1]范围内。
|
||||
- **ValueError** - 当 `prob` 取值不在[0, 1]范围内。
|
||||
- **ValueError** - 当 `distortion_scale` 取值不在[0.0, 1.0]范围内。
|
||||
- **ValueError** - 当 `prob` 取值不在[0.0, 1.0]范围内。
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ mindspore.dataset.vision.RandomSelectSubpolicy
|
|||
从策略列表中随机选择一个子策略以应用于输入图像。
|
||||
|
||||
参数：
|
||||
- **policy** (list[list[tuple[TensorOperation, float]]]) - 可供选择的子策略列表。子策略是一系列 (operation, prob) 格式的元组组成的列表，其中 `operation` 是数据处理操作， `prob` 是应用此操作的概率， `prob` 值必须在 [0, 1] 范围内。一旦选择了子策略，子策略中的每个操作都将根据其概率依次应用。
|
||||
- **policy** (list[list[tuple[TensorOperation, float]]]) - 可供选择的子策略列表。子策略是一系列 (operation, prob) 格式的元组组成的列表，其中 `operation` 是数据处理操作， `prob` 是应用此操作的概率， `prob` 值必须在 [0.0, 1.0] 范围内。一旦选择了子策略，子策略中的每个操作都将根据其概率依次应用。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 当 `policy` 包含无效数据处理操作。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomVerticalFlip
|
|||
以给定的概率对输入图像在垂直方向进行随机翻转。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 不是float类型。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **RuntimeError** - 如果输入的Tensor不是 <H, W> 或 <H, W, C> 格式。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **RuntimeError** - 如果输入的Tensor不是 <H, W> 或 <H, W, C> 格式。
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@ mindspore.dataset.vision.RandomVerticalFlipWithBBox
|
|||
以给定的概率对输入图像和边界框在垂直方向进行随机翻转。
|
||||
|
||||
参数：
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，默认值：0.5。
|
||||
- **prob** (float, 可选) - 图像被翻转的概率，取值范围：[0.0, 1.0]。默认值：0.5。
|
||||
|
||||
异常：
|
||||
- **TypeError** - 如果 `prob` 不是float类型。
|
||||
- **ValueError** - 如果 `prob` 不在 [0, 1] 范围。
|
||||
- **ValueError** - 如果 `prob` 不在 [0.0, 1.0] 范围。
|
||||
- **RuntimeError** - 如果输入的Tensor不是 <H, W> 或<H, W, C> 格式。
|
||||
|
|
|
|||
|
|
@ -10,3 +10,7 @@
|
|||
|
||||
返回：
|
||||
int，输入图像通道数。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `image` 参数的维度小于2。
|
||||
- **TypeError** - `image` 参数的类型既不是 np.ndarray，也不是 PIL Image。
|
||||
|
|
|
|||
|
|
@ -10,3 +10,7 @@
|
|||
|
||||
返回：
|
||||
list[int, int]，图像大小。
|
||||
|
||||
异常：
|
||||
- **RuntimeError** - `image` 参数的维度小于2。
|
||||
- **TypeError** - `image` 参数的类型既不是 np.ndarray，也不是 PIL Image。
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@
|
|||
#include "mindspore/core/load_mindir/load_model.h"
|
||||
#if !defined(_WIN32) && !defined(_WIN64)
|
||||
#include "cxx_api/dlutils.h"
|
||||
#include "minddata/dataset/engine/serdes.h"
|
||||
#include "minddata/dataset/include/dataset/execute.h"
|
||||
#endif
|
||||
#include "utils/crypto.h"
|
||||
|
|
|
|||
|
|
@ -504,6 +504,9 @@ class ComputeDeltas(AudioTensorOperation):
|
|||
.. math::
|
||||
d_{t}=\frac{{\textstyle\sum_{n=1}^{N}}n(c_{t+n}-c_{t-n})}{2{\textstyle\sum_{n=1}^{N}}n^{2}}
|
||||
|
||||
where :math:`d_{t}` is the deltas at time :math:`t` , :math:`c_{t}` is the spectrogram coefficients
|
||||
at time :math:`t` , :math:`N` is :math:`(\text{win_length}-1)//2` .
|
||||
|
||||
Args:
|
||||
win_length (int, optional): The window length used for computing delta, must be no less than 3 (default=5).
|
||||
pad_mode (BorderType, optional): Mode parameter passed to padding (default=BorderType.EDGE).It can be any of
|
||||
|
|
@ -1262,10 +1265,15 @@ class MaskAlongAxis(AudioTensorOperation):
|
|||
|
||||
Args:
|
||||
mask_start (int): Starting position of the mask, which must be non negative.
|
||||
mask_width (int): The width of the mask, which must be non negative.
|
||||
mask_width (int): The width of the mask, which must be larger than 0.
|
||||
mask_value (float): Value to assign to the masked columns.
|
||||
axis (int): Axis to apply masking on (1 for frequency and 2 for time).
|
||||
|
||||
Raises:
|
||||
ValueError: If `mask_start` is invalid (< 0).
|
||||
ValueError: If `mask_width` is invalid (< 1).
|
||||
ValueError: If `axis` is not type of integer or not within [1, 2].
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>>
|
||||
|
|
@ -1299,6 +1307,10 @@ class MaskAlongAxisIID(AudioTensorOperation):
|
|||
mask_value (float): Value to assign to the masked columns.
|
||||
axis (int): Axis to apply masking on (1 for frequency and 2 for time).
|
||||
|
||||
Raises:
|
||||
ValueError: If `mask_param` is invalid (< 0) or not type of integer.
|
||||
ValueError: If `axis` is not type of integer or not within [1, 2].
|
||||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>>
|
||||
|
|
|
|||
|
|
@ -192,7 +192,7 @@ class ScaleType(str, Enum):
|
|||
|
||||
class WindowType(str, Enum):
|
||||
"""
|
||||
Window Function types,
|
||||
Window Function types.
|
||||
|
||||
Possible enumeration values are: WindowType.BARTLETT, WindowType.BLACKMAN, WindowType.HAMMING, WindowType.HANN,
|
||||
WindowType.KAISER.
|
||||
|
|
|
|||
|
|
@ -1174,6 +1174,9 @@ class Dataset:
|
|||
Returns:
|
||||
Dataset, dataset zipped.
|
||||
|
||||
Raises:
|
||||
TypeError: The parameter is not dataset object or tuple of dataset objects.
|
||||
|
||||
Examples:
|
||||
>>> # Create a dataset which is the combination of dataset and dataset_1
|
||||
>>> dataset = dataset.zip(dataset_1)
|
||||
|
|
|
|||
|
|
@ -1588,7 +1588,7 @@ class WikiTextDataset(SourceDataset, TextBaseDataset):
|
|||
A source dataset that reads and parses WikiText2 and WikiText103 datasets.
|
||||
|
||||
The generated dataset has one column :py:obj:`[text]`, and
|
||||
the tensor of column :py:obj:`text` is of the string type.
|
||||
the tensor of column `text` is of the string type.
|
||||
|
||||
Args:
|
||||
dataset_dir (str): Path to the root directory that contains the dataset.
|
||||
|
|
@ -1614,9 +1614,13 @@ class WikiTextDataset(SourceDataset, TextBaseDataset):
|
|||
`Single-Node Data Cache <https://www.mindspore.cn/tutorials/experts/en/master/dataset/cache.html>`_
|
||||
(default=None, which means no cache is used).
|
||||
|
||||
Examples:
|
||||
>>> wiki_text_dataset_dir = "/path/to/wiki_text_dataset_directory"
|
||||
>>> dataset = ds.WikiTextDataset(dataset_dir=wiki_text_dataset_dir, usage='all')
|
||||
Raises:
|
||||
RuntimeError: If `dataset_dir` does not contain data files or invalid.
|
||||
ValueError: If `num_samples` is invalid (< 0).
|
||||
ValueError: If `num_parallel_workers` exceeds the max thread numbers.
|
||||
RuntimeError: If `num_shards` is specified but `shard_id` is None.
|
||||
RuntimeError: If `shard_id` is specified but `num_shards` is None.
|
||||
ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
|
||||
|
||||
About WikiTextDataset dataset:
|
||||
|
||||
|
|
@ -1647,6 +1651,10 @@ class WikiTextDataset(SourceDataset, TextBaseDataset):
|
|||
journal={arXiv preprint arXiv:1609.07843},
|
||||
year={2016}
|
||||
}
|
||||
|
||||
Examples:
|
||||
>>> wiki_text_dataset_dir = "/path/to/wiki_text_dataset_directory"
|
||||
>>> dataset = ds.WikiTextDataset(dataset_dir=wiki_text_dataset_dir, usage='all')
|
||||
"""
|
||||
|
||||
@check_wiki_text_dataset
|
||||
|
|
|
|||
|
|
@ -121,6 +121,7 @@ class JiebaTokenizer(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import JiebaMode
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
>>> jieba_hmm_file = "/path/to/jieba/hmm/file"
|
||||
|
|
@ -175,6 +176,7 @@ class JiebaTokenizer(TextTensorOperation):
|
|||
the better chance the word will be tokenized (default=None, use default frequency).
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import JiebaMode
|
||||
>>> jieba_hmm_file = "/path/to/jieba/hmm/file"
|
||||
>>> jieba_mp_file = "/path/to/jieba/mp/file"
|
||||
|
|
@ -292,6 +294,7 @@ class Lookup(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # Load vocabulary from list
|
||||
>>> vocab = text.Vocab.from_list(['深', '圳', '欢', '迎', '您'])
|
||||
>>> # Use Lookup operation to map tokens to ids
|
||||
|
|
@ -343,6 +346,7 @@ class Ngram(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> ngram_op = text.Ngram(3, separator="-")
|
||||
>>> output = ngram_op(["WildRose Country", "Canada's Ocean Playground", "Land of Living Skies"])
|
||||
>>> # output
|
||||
|
|
@ -428,6 +432,7 @@ class SentencePieceTokenizer(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import SentencePieceModel, SPieceTokenizerOutType
|
||||
>>> sentence_piece_vocab_file = "/path/to/sentence/piece/vocab/file"
|
||||
>>> vocab = text.SentencePieceVocab.from_file([sentence_piece_vocab_file], 5000, 0.9995,
|
||||
|
|
@ -465,6 +470,7 @@ class SlidingWindow(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset as ds
|
||||
>>> dataset = ds.NumpySlicesDataset(data=[[1, 2, 3, 4, 5]], column_names="col1")
|
||||
>>> # Data before
|
||||
>>> # | col1 |
|
||||
|
|
@ -511,6 +517,8 @@ class ToNumber(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset as ds
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore import dtype as mstype
|
||||
>>> data = [["1", "2", "3"]]
|
||||
>>> dataset = ds.NumpySlicesDataset(data)
|
||||
|
|
@ -549,6 +557,7 @@ class ToVectors(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # Load vectors from file
|
||||
>>> vectors = text.Vectors.from_file("/path/to/vectors/file")
|
||||
>>> # Use ToVectors operation to map tokens to vectors
|
||||
|
|
@ -583,6 +592,7 @@ class TruncateSequencePair(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> dataset = ds.NumpySlicesDataset(data={"col1": [[1, 2, 3]], "col2": [[4, 5]]})
|
||||
>>> # Data before
|
||||
>>> # | col1 | col2 |
|
||||
|
|
@ -621,6 +631,7 @@ class UnicodeCharTokenizer(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
>>> tokenizer_op = text.UnicodeCharTokenizer(with_offsets=False)
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
|
||||
|
|
@ -666,6 +677,7 @@ class WordpieceTokenizer(TextTensorOperation):
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> vocab_list = ["book", "cholera", "era", "favor", "##ite", "my", "is", "love", "dur", "##ing", "the"]
|
||||
>>> vocab = text.Vocab.from_list(vocab_list)
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
|
|
@ -744,6 +756,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import NormalizeForm
|
||||
>>>
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
|
|
@ -834,6 +847,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import NormalizeForm
|
||||
>>>
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
|
|
@ -849,8 +863,8 @@ if platform.system().lower() != 'windows':
|
|||
... with_offsets=False)
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
|
||||
>>> # If with_offsets=True, then output three columns {["token", dtype=str],
|
||||
>>> # ["offsets_start", dtype=uint32],
|
||||
>>> # ["offsets_limit", dtype=uint32]}
|
||||
>>> # ["offsets_start", dtype=uint32],
|
||||
>>> # ["offsets_limit", dtype=uint32]}
|
||||
>>> tokenizer_op = text.BertTokenizer(vocab=vocab, suffix_indicator='##', max_bytes_per_token=100,
|
||||
... unknown_token='[UNK]', lower_case=False, keep_whitespace=False,
|
||||
... normalization_form=NormalizeForm.NONE, preserve_unused_token=True,
|
||||
|
|
@ -897,6 +911,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> case_op = text.CaseFold()
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=case_op)
|
||||
"""
|
||||
|
|
@ -917,7 +932,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text.transforms as text
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>>
|
||||
>>> replace_op = text.FilterWikipediaXML()
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=replace_op)
|
||||
|
|
@ -953,6 +968,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> from mindspore.dataset.text import NormalizeForm
|
||||
>>> normalize_op = text.NormalizeUTF8(normalize_form=NormalizeForm.NFC)
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=normalize_op)
|
||||
|
|
@ -994,6 +1010,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> pattern = 'Canada'
|
||||
>>> replace = 'China'
|
||||
>>> replace_op = text.RegexReplace(pattern, replace)
|
||||
|
|
@ -1037,6 +1054,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # If with_offsets=False, default output is one column {["text", dtype=str]}
|
||||
>>> delim_pattern = r"[ |,]"
|
||||
>>> tokenizer_op = text.RegexTokenizer(delim_pattern, with_offsets=False)
|
||||
|
|
@ -1080,6 +1098,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
>>> tokenizer_op = text.UnicodeScriptTokenizer(keep_whitespace=True, with_offsets=False)
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
|
||||
|
|
@ -1121,6 +1140,7 @@ if platform.system().lower() != 'windows':
|
|||
``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset.text as text
|
||||
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
|
||||
>>> tokenizer_op = text.WhitespaceTokenizer(with_offsets=False)
|
||||
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
|
||||
|
|
|
|||
|
|
@ -36,19 +36,25 @@ class CharNGram(cde.CharNGram):
|
|||
@check_from_file_vectors
|
||||
def from_file(cls, file_path, max_vectors=None):
|
||||
"""
|
||||
Build a CharNGram vector from a file.
|
||||
Build a `CharNGram` vector from a file.
|
||||
|
||||
Args:
|
||||
file_path (str): Path of the file that contains the CharNGram vectors.
|
||||
file_path (str): Path of the file that contains the `CharNGram` vectors.
|
||||
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
|
||||
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
|
||||
situations where the entire set doesn't fit in memory, or is not needed for another reason,
|
||||
passing max_vectors can limit the size of the loaded set (default=None, no limit).
|
||||
passing `max_vectors` can limit the size of the loaded set (default=None, no limit).
|
||||
|
||||
Returns:
|
||||
CharNGram, CharNGram vector build from a file.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `file_path` contains invalid data.
|
||||
ValueError: If `max_vectors` is invalid.
|
||||
TypeError: If `max_vectors` is not type of integer.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> char_n_gram = text.CharNGram.from_file("/path/to/char_n_gram/file", max_vectors=None)
|
||||
"""
|
||||
|
||||
|
|
@ -73,12 +79,18 @@ class FastText(cde.FastText):
|
|||
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
|
||||
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
|
||||
situations where the entire set doesn't fit in memory, or is not needed for another reason,
|
||||
passing max_vectors can limit the size of the loaded set (default=None, no limit).
|
||||
passing `max_vectors` can limit the size of the loaded set (default=None, no limit).
|
||||
|
||||
Returns:
|
||||
FastText, FastText vector build from a file.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `file_path` contains invalid data.
|
||||
ValueError: If `max_vectors` is invalid.
|
||||
TypeError: If `max_vectors` is not type of integer.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> fast_text = text.FastText.from_file("/path/to/fast_text/file", max_vectors=None)
|
||||
"""
|
||||
|
||||
|
|
@ -103,12 +115,18 @@ class GloVe(cde.GloVe):
|
|||
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
|
||||
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
|
||||
situations where the entire set doesn't fit in memory, or is not needed for another reason,
|
||||
passing max_vectors can limit the size of the loaded set (default=None, no limit).
|
||||
passing `max_vectors` can limit the size of the loaded set (default=None, no limit).
|
||||
|
||||
Returns:
|
||||
GloVe, GloVe vector build from a file.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `file_path` contains invalid data.
|
||||
ValueError: If `max_vectors` is invalid.
|
||||
TypeError: If `max_vectors` is not type of integer.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> glove = text.GloVe.from_file("/path/to/glove/file", max_vectors=None)
|
||||
"""
|
||||
|
||||
|
|
@ -220,10 +238,11 @@ class SentencePieceVocab:
|
|||
SentencePieceVocab, vocab built from the dataset.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset.text import SentencePieceModel
|
||||
>>> import mindspore.dataset as ds
|
||||
>>> from mindspore.dataset.text import SentencePieceVocab, SentencePieceModel
|
||||
>>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
|
||||
>>> vocab = text.SentencePieceVocab.from_dataset(dataset, ["text"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
>>> vocab = SentencePieceVocab.from_dataset(dataset, ["text"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
"""
|
||||
|
||||
sentence_piece_vocab = cls()
|
||||
|
|
@ -262,9 +281,9 @@ class SentencePieceVocab:
|
|||
SentencePieceVocab, vocab built from the file.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset.text import SentencePieceModel
|
||||
>>> vocab = text.SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
>>> from mindspore.dataset.text import SentencePieceVocab, SentencePieceModel
|
||||
>>> vocab = SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
"""
|
||||
|
||||
sentence_piece_vocab = cls()
|
||||
|
|
@ -284,10 +303,10 @@ class SentencePieceVocab:
|
|||
filename (str): The name of the file.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset.text import SentencePieceModel
|
||||
>>> vocab = text.SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
>>> text.SentencePieceVocab.save_model(vocab, "./", "m.model")
|
||||
>>> from mindspore.dataset.text import SentencePieceVocab, SentencePieceModel
|
||||
>>> vocab = SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
|
||||
... SentencePieceModel.UNIGRAM, {})
|
||||
>>> SentencePieceVocab.save_model(vocab, "./", "m.model")
|
||||
"""
|
||||
|
||||
cde.SentencePieceVocab.save_model(vocab.c_sentence_piece_vocab, path, filename)
|
||||
|
|
@ -337,12 +356,18 @@ class Vectors(cde.Vectors):
|
|||
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
|
||||
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
|
||||
situations where the entire set doesn't fit in memory, or is not needed for another reason,
|
||||
passing max_vectors can limit the size of the loaded set (default=None, no limit).
|
||||
passing `max_vectors` can limit the size of the loaded set (default=None, no limit).
|
||||
|
||||
Returns:
|
||||
Vectors, Vectors build from a file.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `file_path` contains invalid data.
|
||||
ValueError: If `max_vectors` is invalid.
|
||||
TypeError: If `max_vectors` is not type of integer.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vector = text.Vectors.from_file("/path/to/vectors/file", max_vectors=None)
|
||||
"""
|
||||
|
||||
|
|
@ -393,6 +418,8 @@ class Vocab:
|
|||
Vocab, Vocab object built from the dataset.
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.dataset as ds
|
||||
>>> from mindspore.dataset import text
|
||||
>>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
|
||||
>>> vocab = text.Vocab.from_dataset(dataset, "text", freq_range=None, top_k=None,
|
||||
... special_tokens=["<pad>", "<unk>"],
|
||||
|
|
@ -421,6 +448,7 @@ class Vocab:
|
|||
Vocab, Vocab object built from the list.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
|
||||
"""
|
||||
|
||||
|
|
@ -451,6 +479,7 @@ class Vocab:
|
|||
Vocab, Vocab object built from the file.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> # Assume vocab file contains the following content:
|
||||
>>> # --- begin of file ---
|
||||
>>> # apple,apple2
|
||||
|
|
@ -488,6 +517,7 @@ class Vocab:
|
|||
Vocab, Vocab object built from the dict.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vocab = text.Vocab.from_dict({"home": 3, "behind": 2, "the": 4, "world": 5, "<unk>": 6})
|
||||
"""
|
||||
|
||||
|
|
@ -503,6 +533,7 @@ class Vocab:
|
|||
A vocabulary consisting of word and id pairs.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vocab = text.Vocab.from_list(["word_1", "word_2", "word_3", "word_4"])
|
||||
>>> vocabory_dict = vocab.vocab()
|
||||
"""
|
||||
|
|
@ -522,6 +553,7 @@ class Vocab:
|
|||
The token id or list of token ids.
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
|
||||
>>> ids = vocab.tokens_to_ids(["w1", "w3"])
|
||||
"""
|
||||
|
|
@ -545,6 +577,7 @@ class Vocab:
|
|||
The decoded token(s).
|
||||
|
||||
Examples:
|
||||
>>> from mindspore.dataset import text
|
||||
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
|
||||
>>> token = vocab.ids_to_tokens(0)
|
||||
"""
|
||||
|
|
@ -569,6 +602,7 @@ def to_bytes(array, encoding='utf8'):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore.dataset as ds
|
||||
>>>
|
||||
>>> data = np.array([["1", "2", "3"]], dtype=np.str_)
|
||||
>>> dataset = ds.NumpySlicesDataset(data, column_names=["text"])
|
||||
|
|
@ -595,6 +629,7 @@ def to_str(array, encoding='utf8'):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore.dataset as ds
|
||||
>>>
|
||||
>>> data = np.array([["1", "2", "3"]], dtype=np.bytes_)
|
||||
>>> dataset = ds.NumpySlicesDataset(data, column_names=["text"])
|
||||
|
|
|
|||
|
|
@ -151,7 +151,7 @@ class AdjustGamma(ImageTensorOperation):
|
|||
The output image pixel value is exponentially related to the input image pixel value.
|
||||
gamma larger than 1 make the shadows darker,
|
||||
while gamma smaller than 1 make dark regions lighter.
|
||||
gain (float, optional): The constant multiplier (default=1).
|
||||
gain (float, optional): The constant multiplier (default=1.0).
|
||||
|
||||
Raises:
|
||||
TypeError: If `gain` is not of type float.
|
||||
|
|
@ -205,7 +205,7 @@ class AutoAugment(ImageTensorOperation):
|
|||
|
||||
- Inter.BICUBIC: means the interpolation method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA: means the interpolation method is area interpolation.
|
||||
- Inter.AREA: means the interpolation method is pixel area interpolation.
|
||||
|
||||
fill_value (Union[int, tuple], optional): Pixel fill value for the area outside the transformed image.
|
||||
It can be an int or a 3-tuple. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
|
||||
|
|
@ -1943,7 +1943,7 @@ class RandomRotation(ImageTensorOperation):
|
|||
|
||||
- Inter.BICUBIC, means resample method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA: means the interpolation method is area interpolation.
|
||||
- Inter.AREA: means the interpolation method is pixel area interpolation.
|
||||
|
||||
expand (bool, optional): Optional expansion flag (default=False). If set to True, expand the output
|
||||
image to make it large enough to hold the entire rotated image.
|
||||
|
|
|
|||
|
|
@ -201,7 +201,7 @@ class AdjustGamma(ImageTensorOperation, PyTensorOperation):
|
|||
The output image pixel value is exponentially related to the input image pixel value.
|
||||
gamma larger than 1 make the shadows darker,
|
||||
while gamma smaller than 1 make dark regions lighter.
|
||||
gain (float, optional): The constant multiplier (default=1).
|
||||
gain (float, optional): The constant multiplier (default=1.0).
|
||||
|
||||
Raises:
|
||||
TypeError: If `gain` is not of type float.
|
||||
|
|
@ -461,7 +461,7 @@ class AutoAugment(ImageTensorOperation):
|
|||
|
||||
- Inter.BICUBIC: means the interpolation method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA: means the interpolation method is area interpolation.
|
||||
- Inter.AREA: means the interpolation method is pixel area interpolation.
|
||||
|
||||
fill_value (Union[int, tuple[int]], optional): Pixel fill value for the area outside the transformed image.
|
||||
It can be an int or a 3-tuple. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
|
||||
|
|
@ -469,8 +469,8 @@ class AutoAugment(ImageTensorOperation):
|
|||
(default=0).
|
||||
|
||||
Raises:
|
||||
TypeError: If `policy` is not of type AutoAugmentPolicy.
|
||||
TypeError: If `interpolation` is not of type Inter.
|
||||
TypeError: If `policy` is not of type :class:`mindspore.dataset.vision.AutoAugmentPolicy`.
|
||||
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
|
||||
TypeError: If `fill_value` is not an integer or a tuple of length 3.
|
||||
RuntimeError: If given tensor shape is not <H, W, C>.
|
||||
|
||||
|
|
@ -564,12 +564,12 @@ class BoundingBoxAugment(ImageTensorOperation):
|
|||
transform (TensorOperation): Transformation operation to be applied on random selection
|
||||
of bounding box regions of a given image.
|
||||
ratio (float, optional): Ratio of bounding boxes to apply augmentation on.
|
||||
Range: [0, 1] (default=0.3).
|
||||
Range: [0.0, 1.0] (default=0.3).
|
||||
|
||||
Raises:
|
||||
TypeError: If `transform` is an image processing operation in :class:`mindspore.dataset.vision.transforms`.
|
||||
TypeError: If `ratio` is not of type float.
|
||||
ValueError: If `ratio` is not in range [0, 1].
|
||||
ValueError: If `ratio` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given bounding box is invalid.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -781,14 +781,15 @@ class CutMixBatch(ImageTensorOperation):
|
|||
image_batch_format (ImageBatchFormat): The method of padding. Can be any of
|
||||
[ImageBatchFormat.NHWC, ImageBatchFormat.NCHW].
|
||||
alpha (float, optional): Hyperparameter of beta distribution, must be larger than 0 (default = 1.0).
|
||||
prob (float, optional): The probability by which CutMix is applied to each image, range: [0, 1] (default = 1.0).
|
||||
prob (float, optional): The probability by which CutMix is applied to each image,
|
||||
which must be in range: [0.0, 1.0] (default = 1.0).
|
||||
|
||||
Raises:
|
||||
TypeError: If `image_batch_format` is not of type :class:`mindspore.dataset.vision.ImageBatchFormat`.
|
||||
TypeError: If `alpha` is not of type float.
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `alpha` is less than or equal 0.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -1710,7 +1711,7 @@ class Perspective(ImageTensorOperation, PyTensorOperation):
|
|||
- Inter.CUBIC: means the interpolation method is bicubic interpolation, here is the same as Inter.BICUBIC.
|
||||
- Inter.PILCUBIC, means interpolation method is bicubic interpolation like implemented in pillow, input
|
||||
should be in 3 channels format.(PIL input is not supported)
|
||||
- Inter.AREA, area interpolation.(PIL input is not supported)
|
||||
- Inter.AREA, pixel area interpolation.(PIL input is not supported)
|
||||
|
||||
Raises:
|
||||
TypeError: If `start_points` is not of type Sequence[Sequence[int, int]] of length 4.
|
||||
|
|
@ -1812,7 +1813,7 @@ class RandAugment(ImageTensorOperation):
|
|||
|
||||
- Inter.BICUBIC: means the interpolation method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA: means the interpolation method is area interpolation.
|
||||
- Inter.AREA: means the interpolation method is pixel area interpolation.
|
||||
|
||||
fill_value (Union[int, tuple[int, int, int]], optional): Pixel fill value for the area outside the transformed
|
||||
image. It can be an int or a 3-tuple. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
|
||||
|
|
@ -1861,13 +1862,13 @@ class RandomAdjustSharpness(ImageTensorOperation):
|
|||
Degree of 0.0 gives a blurred image, degree of 1.0 gives the original image,
|
||||
and degree of 2.0 increases the sharpness by a factor of 2.
|
||||
prob (float, optional): Probability of the image being sharpness adjusted, which
|
||||
must be in range of [0, 1] (default=0.5).
|
||||
must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `degree` is not of type float.
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `degree` is negative.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2041,7 +2042,7 @@ class RandomAutoContrast(ImageTensorOperation):
|
|||
ignore (Union[int, sequence], optional): The background pixel values to be ignored, each of
|
||||
which must be in range of [0, 255] (default=None).
|
||||
prob (float, optional): Probability of the image being automatically contrasted, which
|
||||
must be in range of [0, 1] (default=0.5).
|
||||
must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `cutoff` is not of type float.
|
||||
|
|
@ -2049,7 +2050,7 @@ class RandomAutoContrast(ImageTensorOperation):
|
|||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `cutoff` is not in range [0.0, 50.0).
|
||||
ValueError: If `ignore` is not in range [0, 255].
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2482,11 +2483,11 @@ class RandomEqualize(ImageTensorOperation):
|
|||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being equalized, which
|
||||
must be in range of [0, 1] (default=0.5).
|
||||
must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2515,7 +2516,8 @@ class RandomErasing(PyTensorOperation):
|
|||
See `Random Erasing Data Augmentation <https://arxiv.org/pdf/1708.04896.pdf>`_.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of performing erasing. Default: 0.5.
|
||||
prob (float, optional): Probability of performing erasing, which
|
||||
must be in range of [0.0, 1.0] (default: 0.5).
|
||||
scale (Sequence[float, float], optional): Range of area scale of the erased area relative
|
||||
to the original image to select from, arranged in order of (min, max).
|
||||
Default: (0.02, 0.33).
|
||||
|
|
@ -2537,7 +2539,7 @@ class RandomErasing(PyTensorOperation):
|
|||
TypeError: If `value` is not of type integer, string, or sequence.
|
||||
TypeError: If `inplace` is not of type boolean.
|
||||
TypeError: If `max_attempts` is not of type integer.
|
||||
ValueError: If `prob` is not in range of [0, 1].
|
||||
ValueError: If `prob` is not in range of [0.0, 1.0].
|
||||
ValueError: If `scale` is negative.
|
||||
ValueError: If `ratio` is negative.
|
||||
ValueError: If `value` is not in range of [0, 255].
|
||||
|
|
@ -2591,11 +2593,12 @@ class RandomGrayscale(PyTensorOperation):
|
|||
Randomly convert the input PIL Image to grayscale.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of performing grayscale conversion. Default: 0.1.
|
||||
prob (float, optional): Probability of performing grayscale conversion,
|
||||
which must be in range of [0.0, 1.0] (default: 0.1).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range of [0, 1].
|
||||
ValueError: If `prob` is not in range of [0.0, 1.0].
|
||||
|
||||
Supported Platforms:
|
||||
``CPU``
|
||||
|
|
@ -2644,11 +2647,12 @@ class RandomHorizontalFlip(ImageTensorOperation, PyTensorOperation):
|
|||
Randomly flip the input image horizontally with a given probability.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being flipped, which must be in range of [0, 1] (default=0.5).
|
||||
prob (float, optional): Probability of the image being flipped,
|
||||
which must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2686,11 +2690,12 @@ class RandomHorizontalFlipWithBBox(ImageTensorOperation):
|
|||
Flip the input image horizontally randomly with a given probability and adjust bounding boxes accordingly.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being flipped, which must be in range of [0, 1] (default=0.5).
|
||||
prob (float, optional): Probability of the image being flipped,
|
||||
which must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2717,11 +2722,12 @@ class RandomInvert(ImageTensorOperation):
|
|||
Randomly invert the colors of image with a given probability.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being inverted, which must be in range of [0, 1] (default=0.5).
|
||||
prob (float, optional): Probability of the image being inverted,
|
||||
which must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -2792,8 +2798,9 @@ class RandomPerspective(PyTensorOperation):
|
|||
Randomly apply perspective transformation to the input PIL Image with a given probability.
|
||||
|
||||
Args:
|
||||
distortion_scale (float, optional): Scale of distortion, in range of [0, 1]. Default: 0.5.
|
||||
prob (float, optional): Probability of performing perspective transformation. Default: 0.5.
|
||||
distortion_scale (float, optional): Scale of distortion, in range of [0.0, 1.0]. Default: 0.5.
|
||||
prob (float, optional): Probability of performing perspective transformation, which
|
||||
must be in range of [0.0, 1.0] (default: 0.5).
|
||||
interpolation (Inter, optional): Method of interpolation. It can be Inter.BILINEAR,
|
||||
Inter.NEAREST or Inter.BICUBIC. Default: Inter.BICUBIC.
|
||||
|
||||
|
|
@ -2805,8 +2812,8 @@ class RandomPerspective(PyTensorOperation):
|
|||
TypeError: If `distortion_scale` is not of type float.
|
||||
TypeError: If `prob` is not of type float.
|
||||
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
|
||||
ValueError: If `distortion_scale` is not in range of [0, 1].
|
||||
ValueError: If `prob` is not in range of [0, 1].
|
||||
ValueError: If `distortion_scale` is not in range of [0.0, 1.0].
|
||||
ValueError: If `prob` is not in range of [0.0, 1.0].
|
||||
|
||||
Supported Platforms:
|
||||
``CPU``
|
||||
|
|
@ -3154,7 +3161,7 @@ class RandomRotation(ImageTensorOperation, PyTensorOperation):
|
|||
|
||||
- Inter.BICUBIC, means resample method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA, means the interpolation method is area interpolation.
|
||||
- Inter.AREA, means the interpolation method is pixel area interpolation.
|
||||
|
||||
expand (bool, optional): Optional expansion flag (default=False). If set to True, expand the output
|
||||
image to make it large enough to hold the entire rotated image.
|
||||
|
|
@ -3246,7 +3253,7 @@ class RandomSelectSubpolicy(ImageTensorOperation):
|
|||
Args:
|
||||
policy (list[list[tuple[TensorOperation, float]]]): List of sub-policies to choose from.
|
||||
A sub-policy is a list of tuple[operation, prob], where operation is a data processing operation and prob
|
||||
is the probability that this operation will be applied, and the prob values must be in range [0, 1].
|
||||
is the probability that this operation will be applied, and the prob values must be in range [0.0, 1.0].
|
||||
Once a sub-policy is selected, each operation within the sub-policy with be applied in sequence according
|
||||
to its probability.
|
||||
|
||||
|
|
@ -3370,11 +3377,12 @@ class RandomVerticalFlip(ImageTensorOperation, PyTensorOperation):
|
|||
Randomly flip the input image vertically with a given probability.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being flipped. Default=0.5.
|
||||
prob (float, optional): Probability of the image being flipped, which
|
||||
must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -3412,11 +3420,12 @@ class RandomVerticalFlipWithBBox(ImageTensorOperation):
|
|||
Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.
|
||||
|
||||
Args:
|
||||
prob (float, optional): Probability of the image being flipped (default=0.5).
|
||||
prob (float, optional): Probability of the image being flipped,
|
||||
which must be in range of [0.0, 1.0] (default=0.5).
|
||||
|
||||
Raises:
|
||||
TypeError: If `prob` is not of type float.
|
||||
ValueError: If `prob` is not in range [0, 1].
|
||||
ValueError: If `prob` is not in range [0.0, 1.0].
|
||||
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
|
||||
|
||||
Supported Platforms:
|
||||
|
|
@ -4098,7 +4107,7 @@ class TrivialAugmentWide(ImageTensorOperation):
|
|||
|
||||
- Inter.BICUBIC: means the interpolation method is bicubic interpolation.
|
||||
|
||||
- Inter.AREA: means the interpolation method is area interpolation.
|
||||
- Inter.AREA: means the interpolation method is pixel area interpolation.
|
||||
|
||||
fill_value (Union[int, tuple[int, int, int]], optional): Pixel fill value for the area outside
|
||||
the transformed image.
|
||||
|
|
|
|||
|
|
@ -393,6 +393,10 @@ def get_image_num_channels(image):
|
|||
Returns:
|
||||
int, the number of input image channels.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `image` has invalid dimensions which should be larger than 1.
|
||||
TypeError: If `image` is not of type <class 'numpy.ndarray'> or <class 'PIL.Image.Image'>.
|
||||
|
||||
Examples:
|
||||
>>> num_channels = vision.get_image_num_channels(image)
|
||||
"""
|
||||
|
|
@ -419,6 +423,10 @@ def get_image_size(image):
|
|||
Returns:
|
||||
list[int, int], the image size.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If `image` has invalid dimensions which should be larger than 1.
|
||||
TypeError: If `image` is not of type <class 'numpy.ndarray'> or <class 'PIL.Image.Image'>.
|
||||
|
||||
Examples:
|
||||
>>> image_size = vision.get_image_size(image)
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in New Issue