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!8118 update wrong comment for 'resnext50/densnet121/vgg16' 

Merge pull request !8118 from caojian05/ms_master_bugfix
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mindspore-ci-bot 2020-11-04 17:40:47 +08:00 committed by Gitee
parent 4b52bad287 02f630286c
commit 250ea7c001
3 changed files with 17 additions and 17 deletions
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10
model_zoo/official/cv/densenet121/src/datasets/classification.py
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@ -69,7 +69,7 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
Args: Args:
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"". data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
Or path of the textfile that contains every image's path of the dataset. Or path of the textfile that contains every image's path of the dataset.
image_size (str): Size of the input images. image_size (Union(int, sequence)): Size of the input images.
per_batch_size (int): the batch size of evey step during training. per_batch_size (int): the batch size of evey step during training.
max_epoch (int): the number of epochs. max_epoch (int): the number of epochs.
rank (int): The shard ID within num_shards (default=None). rank (int): The shard ID within num_shards (default=None).
@ -90,14 +90,14 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
Examples: Examples:
>>> from src.datasets.classification import classification_dataset >>> from src.datasets.classification import classification_dataset
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
>>> dataset_dir = "/path/to/imagefolder_directory" >>> data_dir = "/path/to/imagefolder_directory"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, max_epoch=100, >>> per_batch_size=64, max_epoch=100,
>>> rank=0, group_size=4) >>> rank=0, group_size=4)
>>> # Path of the textfile that contains every image's path of the dataset. >>> # Path of the textfile that contains every image's path of the dataset.
>>> dataset_dir = "/path/to/dataset/images/train.txt" >>> data_dir = "/path/to/dataset/images/train.txt"
>>> images_dir = "/path/to/dataset/images" >>> images_dir = "/path/to/dataset/images"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, max_epoch=100, >>> per_batch_size=64, max_epoch=100,
>>> rank=0, group_size=4, >>> rank=0, group_size=4,
>>> input_mode="txt", root=images_dir) >>> input_mode="txt", root=images_dir)

12
model_zoo/official/cv/resnext50/src/dataset.py
View File

@ -73,7 +73,7 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
Args: Args:
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"". data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
Or path of the textfile that contains every image's path of the dataset. Or path of the textfile that contains every image's path of the dataset.
image_size (str): Size of the input images. image_size (Union(int, sequence)): Size of the input images.
per_batch_size (int): the batch size of evey step during training. per_batch_size (int): the batch size of evey step during training.
max_epoch (int): the number of epochs. max_epoch (int): the number of epochs.
rank (int): The shard ID within num_shards (default=None). rank (int): The shard ID within num_shards (default=None).
@ -92,16 +92,16 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
unique index starting from 0). unique index starting from 0).
Examples: Examples:
>>> from mindvision.common.datasets.classification import classification_dataset >>> from src.dataset import classification_dataset
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
>>> dataset_dir = "/path/to/imagefolder_directory" >>> data_dir = "/path/to/imagefolder_directory"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, max_epoch=100, >>> per_batch_size=64, max_epoch=100,
>>> rank=0, group_size=4) >>> rank=0, group_size=4)
>>> # Path of the textfile that contains every image's path of the dataset. >>> # Path of the textfile that contains every image's path of the dataset.
>>> dataset_dir = "/path/to/dataset/images/train.txt" >>> data_dir = "/path/to/dataset/images/train.txt"
>>> images_dir = "/path/to/dataset/images" >>> images_dir = "/path/to/dataset/images"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, max_epoch=100, >>> per_batch_size=64, max_epoch=100,
>>> rank=0, group_size=4, >>> rank=0, group_size=4,
>>> input_mode="txt", root=images_dir) >>> input_mode="txt", root=images_dir)

12
model_zoo/official/cv/vgg16/src/dataset.py
View File

@ -88,7 +88,7 @@ def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_s
Args: Args:
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"". data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
Or path of the textfile that contains every image's path of the dataset. Or path of the textfile that contains every image's path of the dataset.
image_size (str): Size of the input images. image_size (Union(int, sequence)): Size of the input images.
per_batch_size (int): the batch size of evey step during training. per_batch_size (int): the batch size of evey step during training.
rank (int): The shard ID within num_shards (default=None). rank (int): The shard ID within num_shards (default=None).
group_size (int): Number of shards that the dataset should be divided group_size (int): Number of shards that the dataset should be divided
@ -107,15 +107,15 @@ def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_s
unique index starting from 0). unique index starting from 0).
Examples: Examples:
>>> from mindvision.common.datasets.classification import classification_dataset >>> from src.dataset import classification_dataset
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
>>> dataset_dir = "/path/to/imagefolder_directory" >>> data_dir = "/path/to/imagefolder_directory"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, rank=0, group_size=4) >>> per_batch_size=64, rank=0, group_size=4)
>>> # Path of the textfile that contains every image's path of the dataset. >>> # Path of the textfile that contains every image's path of the dataset.
>>> dataset_dir = "/path/to/dataset/images/train.txt" >>> data_dir = "/path/to/dataset/images/train.txt"
>>> images_dir = "/path/to/dataset/images" >>> images_dir = "/path/to/dataset/images"
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244], >>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
>>> per_batch_size=64, rank=0, group_size=4, >>> per_batch_size=64, rank=0, group_size=4,
>>> input_mode="txt", root=images_dir) >>> input_mode="txt", root=images_dir)
""" """