mindspore.dataset.ImageFolderDataset
- class mindspore.dataset.ImageFolderDataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, extensions=None, class_indexing=None, decode=False, num_shards=None, shard_id=None, cache=None, decrypt=None)[source]
A source dataset that reads images from a tree of directories. All images within one folder have the same label.
The generated dataset has two columns:
[image, label]
. The tensor of columnimage
is of the uint8 type. The tensor of columnlabel
is of a scalar of uint32 type.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of images to be included in the dataset. Default:
None
, all images.num_parallel_workers (int, optional) – Number of worker threads to read the data. Default:
None
, will use global default workers(8), it can be set bymindspore.dataset.config.set_num_parallel_workers()
.shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Default:
None
, expected order behavior shown in the table below.sampler (Sampler, optional) – Object used to choose samples from the dataset. Default:
None
, expected order behavior shown in the table below.extensions (list[str], optional) – List of file extensions to be included in the dataset. Default:
None
.class_indexing (dict, optional) – A str-to-int mapping from folder name to index Default:
None
, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0.decode (bool, optional) – Decode the images after reading. Default:
False
.num_shards (int, optional) – Number of shards that the dataset will be divided into. Default:
None
. When this argument is specified, num_samples reflects the maximum sample number of per shard.shard_id (int, optional) – The shard ID within num_shards . Default:
None
. This argument can only be specified when num_shards is also specified.cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default:
None
, which means no cache is used.decrypt (callable, optional) – Image decryption function, which accepts the path of the encrypted image file and returns the decrypted bytes data. Default:
None
, no decryption.
- Raises
RuntimeError – If dataset_dir does not contain data files.
ValueError – If num_parallel_workers exceeds the max thread numbers.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and num_shards/shard_id are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
RuntimeError – If class_indexing is not a dictionary.
ValueError – If shard_id is not in range of [0, num_shards ).
- Tutorial Examples:
Note
The shape of the image column is [image_size] if decode flag is
False
, or [H,W,C] otherwise.This dataset can take in a sampler . sampler and shuffle are mutually exclusive. The table below shows what input arguments are allowed and their expected behavior.
Parameter sampler
Parameter shuffle
Expected Order Behavior
None
None
random order
None
True
random order
None
False
sequential order
Sampler object
None
order defined by sampler
Sampler object
True
not allowed
Sampler object
False
not allowed
Examples
>>> import mindspore.dataset as ds >>> image_folder_dataset_dir = "/path/to/image_folder_dataset_directory" >>> >>> # 1) Read all samples (image files) in image_folder_dataset_dir with 8 threads >>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir, ... num_parallel_workers=8) >>> >>> # 2) Read all samples (image files) from folder cat and folder dog with label 0 and 1 >>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir, ... class_indexing={"cat":0, "dog":1}) >>> >>> # 3) Read all samples (image files) in image_folder_dataset_dir with extensions .JPEG >>> # and .png (case sensitive) >>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir, ... extensions=[".JPEG", ".png"])
About ImageFolderDataset:
You can construct the following directory structure from your dataset files and read by MindSpore’s API.
. └── image_folder_dataset_directory ├── class1 │ ├── 000000000001.jpg │ ├── 000000000002.jpg │ ├── ... ├── class2 │ ├── 000000000001.jpg │ ├── 000000000002.jpg │ ├── ... ├── class3 │ ├── 000000000001.jpg │ ├── 000000000002.jpg │ ├── ... ├── classN ├── ...
Pre-processing Operation
Apply a function in this dataset. |
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Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Takes at most given numbers of elements from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
Iterator
Create an iterator over the dataset. |
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Create an iterator over the dataset. |
Attribute
Return the size of batch. |
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Return the class index. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Return a transferred Dataset that transfers data through a device. |
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Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |