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PyTorch torchvision

wangzf / 2023-01-17

目录

数据集

内置数据集

示例：

from torch.utils.data import DataLoader
from torchvision import datasets

imagenet_data = datasets.ImageNet("path/to/imagenet_root/")
data_loader = DataLoader(
    imagenet_data,
    batch_size = 4,
    shuffle = True,
    num_workers = args.nThreads
)

类型：

自定义图像数据集的基本类

数据集读写

torchvision.io

视屏

import torchvision
video_path = "path to a test video"
# Constructor allocates memory and a threaded decoder
# instance per video. At the moment it takes two arguments:
# path to the video file, and a wanted stream.
reader = torchvision.io.VideoReader(video_path, "video")

# The information about the video can be retrieved using the
# `get_metadata()` method. It returns a dictionary for every stream, with
# duration and other relevant metadata (often frame rate)
reader_md = reader.get_metadata()

# metadata is structured as a dict of dicts with following structure
# {"stream_type": {"attribute": [attribute per stream]}}
#
# following would print out the list of frame rates for every present video stream
print(reader_md["video"]["fps"])

# we explicitly select the stream we would like to operate on. In
# the constructor we select a default video stream, but
# in practice, we can set whichever stream we would like
video.set_current_stream("video:0")

图像

数据转换与增强

torchvision.transforms

数据转换与增强简介

所有的 torchvision datasets 都有两个接受包含转换逻辑的可调用对象的参数:

大部分 transform 同时接受 PIL 图像和 tensor 图像，但是也有一些 tansform 只接受 PIL 图像，或只接受 tensor 图像

transform 接受 tensor 图像或批量 tensor 图像

一个 tensor 图像像素值的范围由 tensor dtype 严格控制

转换的形式：

常用转换

特征提取

预训练模型和权重

torchvision.models

模型类型

预训练权重的一般信息

初始化预训练模型

from torchvision.models import resnet50, ResNet50_Weights

# Old weights with accuracy 76.130%
resnet50(weights = ResNet50_Weights.IMAGENET1K_V1)
resnet50(weights = "IMAGENET_V1")  # new API

# New weights with accuracy 80.858%
resnet50(weights = ResNet50_Weights.IMAGENET1K_V2)
resnet50(weights = "IMAGENET1K_V2")  # net API

# Best available weights(currently alias for IMAGENET1K_V2)
resnet50(weights = ResNet50_Weights.DEFAULT)

# No Weights - random initialization
resnet50(weights = None)
resnet50()  # new API

使用预训练模型

在使用预训练模型前，必须对图像数据进行预处理，处理方式因模型而异，比如：

每个模型的推断转换信息都在其权重文档中提供：

# 初始化权重转换
weights = ResNet50_Weights.DEFAULT
preprocess = weights.transforms()

# Apply it to the input image
img_transformed = preprocess(img)

一些模型使用具有不同训练和评估行为的模块，例如批量归一化

# Initialize model
weights = ResNet50_Weights.DEFAULT
model = resnet50(weights = weights)

# Set model to eval mode
model.eval()

模型注册机制

从 Hub 中使用模型

PyTorch Hub

import torch

# Option 1: passing weights param as string
model = torch.hub.load(
    "pytorch/vision", 
    "resnet50", 
    weights = "IMAGENET1K_V2"
)

# Option 2: passing weights param as enum
weights = torch.hub.load(
    "pytorch/vision", 
    "get_weight",
    weights = "ResNet50_Weights.IMAGENET1K_V2"
)
model = torch.hub.load(
    "pytorch/vision", 
    "resnet50", 
    weights = weights
)
print(weight for weight in weights)

工具

APIs

TODO

操作

torchvision.ops

任务

classification

detection

segmentation

similarity learning

video classification

其他 APIs