比较与torchvision.datasets.CocoDetection的差异

torchvision.datasets.CocoDetection

class torchvision.datasets.CocoDetection(
    root: str,
    annFile: str,
    transform: Optional[Callable]=None,
    target_transform: Optional[Callable]=None,
    transforms: Optional[Callable]=None
    )

更多内容详见torchvision.datasets.CocoDetection。

mindspore.dataset.CocoDataset

class mindspore.dataset.CocoDataset(
    dataset_dir,
    annotation_file,
    task="Detection",
    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
    )

更多内容详见mindspore.dataset.CocoDataset。

差异对比

PyTorch：输入COCO格式数据集，返回创建出的数据集对象，可通过遍历数据集对象获取数据。

MindSpore：输入COCO格式数据集及指定任务类型（目标检测，全景分割等），返回给定任务类型的数据集对象，可通过创建迭代器获取数据。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	root	dataset_dir	-
	参数2	annFile	annotation_file	-
	参数3	transform	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数4	target_transform	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数5	transforms	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数6	-	task	指定COCO数据的任务类型
	参数7	-	num_samples	指定从数据集中读取的样本数
	参数8	-	num_parallel_workers	指定读取数据的工作线程数
	参数9	-	shuffle	指定是否混洗数据集
	参数10	-	decode	指定是否对图像进行解码
	参数11	-	sampler	指定采样器
	参数12	-	num_shards	指定分布式训练时将数据集进行划分的分片数
	参数13	-	shard_id	指定分布式训练时使用的分片ID号
	参数14	-	cache	指定单节点数据缓存服务
	参数15	-	extra_metadata	用于指定是否额外输出一个数据列用于表示图片元信息
	参数16	-	decrypt	图像解密函数

代码示例

import mindspore.dataset as ds
import torchvision.datasets as datasets
import torchvision.transforms as T

# In MindSpore, CocoDataset supports four kinds of tasks, which are Object Detection, Keypoint Detection, Stuff Segmentation and Panoptic Segmentation of 2017 Train/Val/Test dataset.

coco_dataset_dir = "/path/to/coco_dataset_directory/images"
coco_annotation_file = "/path/to/coco_dataset_directory/annotation_file"

# Read COCO data for Detection task. Output columns: [image, dtype=uint8], [bbox, dtype=float32], [category_id, dtype=uint32], [iscrowd, dtype=uint32]
dataset = ds.CocoDataset(
    dataset_dir=coco_dataset_dir,
    annotation_file=coco_annotation_file,
    task='Detection',
    decode=True,
    shuffle=False,
    extra_metadata=True)

dataset = dataset.rename("_meta-filename", "filename")
file_name = []
bbox = []
category_id = []
iscrowd = []
for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
    file_name.append(data["filename"])
    bbox.append(data["bbox"])
    category_id.append(data["category_id"])
    iscrowd.append(data["iscrowd"])

print(file_name[0])
print(bbox[0])
print(category_id[0])
print(iscrowd[0])
# out:
# 000000391895
# [[10. 10. 10. 10.]
# [70. 70. 70. 70.]]
# [[1]
# [7]]
# [[0]
# [0]]

# In torch, the output will be result of transform, eg. Tensor
root = "/path/to/coco_dataset_directory/images"
annFile = "/path/to/coco_dataset_directory/annotation_file"

# Convert a PIL Image or numpy.ndarray to tensor.
dataset = datasets.CocoDetection(root, annFile, transform=T.ToTensor())
for item in dataset:
    print("item:", item[0])
    break
# out:
# loading annotations into memory...
# Done (t=0.00s)
# creating index...
# index created!
# item: tensor([[[0.8588, 0.8549, 0.8549,  ..., 0.7529, 0.7529, 0.7529,
#        [0.8549, 0.8549, 0.8510,  ..., 0.7529, 0.7529, 0.7529],
#        [0.8549, 0.8510, 0.8510,  ..., 0.7529, 0.7529, 0.7529],
#        ...,
#
#        ...,
#        [0.8471, 0.8510, 0.8549,  ..., 0.7412, 0.7333, 0.7294],
#        [0.8549, 0.8549, 0.8549,  ..., 0.7412, 0.7333, 0.7294],
#        [0.8627, 0.8627, 0.8549,  ..., 0.7412, 0.7333, 0.7294]]])