mindspore.dataset.Food101Dataset
- class mindspore.dataset.Food101Dataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None)[source]
Food101 dataset.
The generated dataset has two columns
[image, label]
. The tensor of columnimage
is of the uint8 type. The tensor of columnlabel
is of the string type.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Usage of this dataset, can be
'train'
,'test'
, or'all'
.'train'
will read from 75,750 samples,'test'
will read from 25,250 samples, and'all'
will read all'train'
and'test'
samples. Default:None
, will be set to'all'
.num_samples (int, optional) – The number of images to be included in the dataset. Default:
None
, will read 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.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. Used in data parallel training .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.
- Raises
RuntimeError – If dataset_dir does not contain data files.
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 shard_id is not in range of [0, num_shards ).
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If the value of usage is not
'train'
,'test'
, or'all'
.ValueError – If dataset_dir is not exist.
- Tutorial Examples:
Note
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 >>> food101_dataset_dir = "/path/to/food101_dataset_directory" >>> >>> # Read 3 samples from Food101 dataset >>> dataset = ds.Food101Dataset(dataset_dir=food101_dataset_dir, num_samples=3)
About Food101 dataset:
The Food101 is a dataset of 101 food categories, with 101,000 images. There are 250 test imgaes and 750 training images in each class. All images were rescaled to have a maximum side length of 512 pixels.
The following is the original Food101 dataset structure. You can unzip the dataset files into this directory structure and read by MindSpore's API.
. └── food101_dir ├── images │ ├── apple_pie │ │ ├── 1005649.jpg │ │ ├── 1014775.jpg │ │ ├──... │ ├── baby_back_rips │ │ ├── 1005293.jpg │ │ ├── 1007102.jpg │ │ ├──... │ └──... └── meta ├── train.txt ├── test.txt ├── classes.txt ├── train.json ├── test.json └── train.txt
Citation:
@inproceedings{bossard14, title = {Food-101 -- Mining Discriminative Components with Random Forests}, author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc}, booktitle = {European Conference on Computer Vision}, year = {2014} }
Pre-processing Operation
Apply a function in this dataset. |
|
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. |