mindspore.dataset.VOCDataset
- class mindspore.dataset.VOCDataset(dataset_dir, task='Segmentation', usage='train', class_indexing=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None, extra_metadata=False, decrypt=None)[source]
VOC(Visual Object Classes) dataset.
The generated dataset with different task setting has different output columns:
task =
Detection
, output columns:[image, dtype=uint8]
,[bbox, dtype=float32]
,[label, dtype=uint32]
,[difficult, dtype=uint32]
,[truncate, dtype=uint32]
.task =
Segmentation
, output columns:[image, dtype=uint8]
,[target,dtype=uint8]
.
- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
task (str, optional) – Set the task type of reading voc data, now only support
'Segmentation'
or'Detection'
. Default:'Segmentation'
.usage (str, optional) – Set the task type of ImageSets. Default:
'train'
. If task is'Segmentation'
, image and annotation list will be loaded in ./ImageSets/Segmentation/usage + ".txt"; If task is 'Detection', image and annotation list will be loaded in ./ImageSets/Main/usage + ".txt"; if task and usage are not set, image and annotation list will be loaded in ./ImageSets/Segmentation/train.txt as default.class_indexing (dict, optional) – A str-to-int mapping from label name to index, only valid in 'Detection' task. Default:
None
, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0.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 to perform shuffle on the dataset. Default:
None
, expected order behavior shown in the table below.decode (bool, optional) – Decode the images after reading. Default:
False
.sampler (Sampler, optional) – Object used to choose samples from the dataset. Default:
None
, expected order behavior shown in the table below.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.extra_metadata (bool, optional) – Flag to add extra meta-data to row. If True, an additional column named
[_meta-filename, dtype=string]
will be output at the end. Default:False
.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.
RuntimeError – If xml of Annotations is an invalid format.
RuntimeError – If xml of Annotations loss attribution of object .
RuntimeError – If xml of Annotations loss attribution of bndbox .
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.
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If task is not equal
'Segmentation'
or'Detection'
.ValueError – If task is
'Segmentation'
but class_indexing is notNone
.ValueError – If txt related to mode is not exist.
ValueError – If shard_id is not in range of [0, num_shards ).
- Tutorial Examples:
Note
Column '[_meta-filename, dtype=string]' won't be output unless an explicit rename dataset op is added to remove the prefix('_meta-').
The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.
Parameter sampler
Parameter num_shards / shard_id
Parameter shuffle
Parameter num_samples
Sampler Used
mindspore.dataset.Sampler type
None
None
None
sampler
numpy.ndarray,list,tuple,int type
/
/
num_samples
SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type
/
/
num_samples
IterSampler(sampler = sampler , num_samples = num_samples )
None
num_shards / shard_id
None / True
num_samples
DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None
num_shards / shard_id
False
num_samples
DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None
None
None / True
None
RandomSampler(num_samples = num_samples )
None
None
None / True
num_samples
RandomSampler(replacement = True , num_samples = num_samples )
None
None
False
num_samples
SequentialSampler(num_samples = num_samples )
Examples
>>> import mindspore.dataset as ds >>> voc_dataset_dir = "/path/to/voc_dataset_directory" >>> >>> # 1) Read VOC data for segmentation training >>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Segmentation", usage="train") >>> >>> # 2) Read VOC data for detection training >>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train") >>> >>> # 3) Read all VOC dataset samples in voc_dataset_dir with 8 threads in random order >>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train", ... num_parallel_workers=8) >>> >>> # 4) Read then decode all VOC dataset samples in voc_dataset_dir in sequence >>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train", ... decode=True, shuffle=False) >>> >>> # In VOC dataset, if task='Segmentation', each dictionary has keys "image" and "target" >>> # In VOC dataset, if task='Detection', each dictionary has keys "image" and "annotation"
About VOC dataset:
The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures.
You can unzip the original VOC-2012 dataset files into this directory structure and read by MindSpore's API.
. └── voc2012_dataset_dir ├── Annotations │ ├── 2007_000027.xml │ ├── 2007_000032.xml │ ├── ... ├── ImageSets │ ├── Action │ ├── Layout │ ├── Main │ └── Segmentation ├── JPEGImages │ ├── 2007_000027.jpg │ ├── 2007_000032.jpg │ ├── ... ├── SegmentationClass │ ├── 2007_000032.png │ ├── 2007_000033.png │ ├── ... └── SegmentationObject ├── 2007_000032.png ├── 2007_000033.png ├── ...
Citation:
@article{Everingham10, author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.}, title = {The Pascal Visual Object Classes (VOC) Challenge}, journal = {International Journal of Computer Vision}, volume = {88}, year = {2012}, number = {2}, month = {jun}, pages = {303--338}, biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex}, howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html} }
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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Take the first specified number of samples 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 that yields samples of type dict, while the key is the column name and the value is the data. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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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
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. |