注意

点击 here 下载完整的示例代码

TorchVision 目标检测微调教程

创建于：2023年12月14日 | 最后更新：2024年6月11日 | 最后验证：2024年11月5日

在本教程中，我们将在Penn-Fudan行人检测和分割数据库上微调一个预训练的Mask R-CNN模型。该数据库包含170张图像，其中有345个行人实例，我们将使用它来演示如何利用torchvision中的新功能在自定义数据集上训练目标检测和实例分割模型。

注意

本教程仅适用于torchvision版本>=0.16或nightly。如果您使用的是torchvision<=0.15，请遵循此教程。

定义数据集

用于训练目标检测、实例分割和人体关键点检测的参考脚本允许轻松支持添加新的自定义数据集。数据集应继承自标准的torch.utils.data.Dataset类，并实现__len__和__getitem__。

我们唯一要求的特殊性是数据集 __getitem__ 应该返回一个元组：

图像：torchvision.tv_tensors.Image 形状为 [3, H, W]，一个纯张量，或大小为 (H, W) 的 PIL 图像
目标：包含以下字段的字典
- boxes, torchvision.tv_tensors.BoundingBoxes 形状为 [N, 4]: N 个边界框的坐标，格式为 [x0, y0, x1, y1]，范围从 0 到 W 和 0 到 H
- labels, 整数 torch.Tensor 形状为 [N]: 每个边界框的标签。 0 始终代表背景类。
- image_id, int: 一个图像标识符。它应该在数据集中的所有图像之间是唯一的，并在评估期间使用。
- area, float torch.Tensor of shape [N]: 边界框的面积。这在评估时使用COCO指标，用于区分小、中和大框的指标分数。
- iscrowd, uint8 torch.Tensor 形状为 [N]: 在评估期间，iscrowd=True 的实例将被忽略。
- （可选）masks，torchvision.tv_tensors.Mask 形状为 [N, H, W]：每个对象的分割掩码

如果你的数据集符合上述要求，那么它将适用于参考脚本中的训练和评估代码。评估代码将使用来自pycocotools的脚本，可以通过pip install pycocotools安装。

注意

对于Windows，请使用命令从gautamchitnis安装pycocotools

pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI

关于labels的一个注意事项。模型将类别0视为背景。如果你的数据集中不包含背景类别，你不应该在labels中包含0。例如，假设你只有两个类别，cat和dog，你可以定义1（而不是0）来表示cats，2来表示dogs。因此，例如，如果其中一个图像包含这两个类别，你的labels张量应该看起来像[1, 2]。

此外，如果您希望在训练期间使用宽高比分组（以便每个批次仅包含具有相似宽高比的图像），则建议还实现一个get_height_and_width 方法，该方法返回图像的高度和宽度。如果未提供此方法，我们将通过__getitem__查询数据集的所有元素，这会加载图像到内存中，并且比提供自定义方法时更慢。

为PennFudan编写自定义数据集

让我们为PennFudan数据集编写一个数据集。首先，让我们下载数据集并解压zip文件：

wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip -P data
cd data && unzip PennFudanPed.zip

我们有以下文件夹结构：

PennFudanPed/
  PedMasks/
    FudanPed00001_mask.png
    FudanPed00002_mask.png
    FudanPed00003_mask.png
    FudanPed00004_mask.png
    ...
  PNGImages/
    FudanPed00001.png
    FudanPed00002.png
    FudanPed00003.png
    FudanPed00004.png

这里是一对图像和分割掩码的示例

import matplotlib.pyplot as plt
from torchvision.io import read_image


image = read_image("data/PennFudanPed/PNGImages/FudanPed00046.png")
mask = read_image("data/PennFudanPed/PedMasks/FudanPed00046_mask.png")

plt.figure(figsize=(16, 8))
plt.subplot(121)
plt.title("Image")
plt.imshow(image.permute(1, 2, 0))
plt.subplot(122)
plt.title("Mask")
plt.imshow(mask.permute(1, 2, 0))

<matplotlib.image.AxesImage object at 0x7f4edba7ae30>

So each image has a corresponding segmentation mask, where each color correspond to a different instance. Let’s write a torch.utils.data.Dataset class for this dataset. In the code below, we are wrapping images, bounding boxes and masks into torchvision.tv_tensors.TVTensor classes so that we will be able to apply torchvision built-in transformations (new Transforms API) for the given object detection and segmentation task. Namely, image tensors will be wrapped by torchvision.tv_tensors.Image, bounding boxes into torchvision.tv_tensors.BoundingBoxes and masks into torchvision.tv_tensors.Mask. As torchvision.tv_tensors.TVTensor are torch.Tensor subclasses, wrapped objects are also tensors and inherit the plain torch.Tensor API. For more information about torchvision tv_tensors see this documentation.

import os
import torch

from torchvision.io import read_image
from torchvision.ops.boxes import masks_to_boxes
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F


class PennFudanDataset(torch.utils.data.Dataset):
    def __init__(self, root, transforms):
        self.root = root
        self.transforms = transforms
        # load all image files, sorting them to
        # ensure that they are aligned
        self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages"))))
        self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks"))))

    def __getitem__(self, idx):
        # load images and masks
        img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
        mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
        img = read_image(img_path)
        mask = read_image(mask_path)
        # instances are encoded as different colors
        obj_ids = torch.unique(mask)
        # first id is the background, so remove it
        obj_ids = obj_ids[1:]
        num_objs = len(obj_ids)

        # split the color-encoded mask into a set
        # of binary masks
        masks = (mask == obj_ids[:, None, None]).to(dtype=torch.uint8)

        # get bounding box coordinates for each mask
        boxes = masks_to_boxes(masks)

        # there is only one class
        labels = torch.ones((num_objs,), dtype=torch.int64)

        image_id = idx
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        # suppose all instances are not crowd
        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)

        # Wrap sample and targets into torchvision tv_tensors:
        img = tv_tensors.Image(img)

        target = {}
        target["boxes"] = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=F.get_size(img))
        target["masks"] = tv_tensors.Mask(masks)
        target["labels"] = labels
        target["image_id"] = image_id
        target["area"] = area
        target["iscrowd"] = iscrowd

        if self.transforms is not None:
            img, target = self.transforms(img, target)

        return img, target

    def __len__(self):
        return len(self.imgs)

这就是数据集的所有内容。现在让我们定义一个可以对这个数据集进行预测的模型。

定义你的模型

在本教程中，我们将使用Mask R-CNN，它是基于Faster R-CNN的。Faster R-CNN是一个预测图像中潜在对象的边界框和类别分数的模型。

../_static/img/tv_tutorial/tv_image03.png

Mask R-CNN 在 Faster R-CNN 的基础上增加了一个额外的分支，该分支还预测每个实例的分割掩码。

../_static/img/tv_tutorial/tv_image04.png

有两种常见的情况可能需要修改TorchVision模型库中的可用模型。第一种情况是当我们想要从一个预训练模型开始，并且只微调最后一层。另一种情况是当我们想要用不同的模型替换模型的主干（例如，为了更快的预测）。

让我们看看在接下来的部分中我们将如何做这个或那个。

1 - 从预训练模型进行微调

假设你想从一个在COCO上预训练的模型开始，并想为你的特定类别进行微调。这里有一个可能的方法：

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor

# load a model pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")

# replace the classifier with a new one, that has
# num_classes which is user-defined
num_classes = 2  # 1 class (person) + background
# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

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2 - 修改模型以添加不同的骨干网络

import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

# load a pre-trained model for classification and return
# only the features
backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features
# ``FasterRCNN`` needs to know the number of
# output channels in a backbone. For mobilenet_v2, it's 1280
# so we need to add it here
backbone.out_channels = 1280

# let's make the RPN generate 5 x 3 anchors per spatial
# location, with 5 different sizes and 3 different aspect
# ratios. We have a Tuple[Tuple[int]] because each feature
# map could potentially have different sizes and
# aspect ratios
anchor_generator = AnchorGenerator(
    sizes=((32, 64, 128, 256, 512),),
    aspect_ratios=((0.5, 1.0, 2.0),)
)

# let's define what are the feature maps that we will
# use to perform the region of interest cropping, as well as
# the size of the crop after rescaling.
# if your backbone returns a Tensor, featmap_names is expected to
# be [0]. More generally, the backbone should return an
# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which
# feature maps to use.
roi_pooler = torchvision.ops.MultiScaleRoIAlign(
    featmap_names=['0'],
    output_size=7,
    sampling_ratio=2
)

# put the pieces together inside a Faster-RCNN model
model = FasterRCNN(
    backbone,
    num_classes=2,
    rpn_anchor_generator=anchor_generator,
    box_roi_pool=roi_pooler
)

Downloading: "https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/mobilenet_v2-7ebf99e0.pth

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用于PennFudan数据集的目标检测和实例分割模型

在我们的案例中，我们希望从一个预训练的模型进行微调，鉴于我们的数据集非常小，因此我们将遵循方法1。

在这里，我们还想计算实例分割掩码，因此我们将使用Mask R-CNN：

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor


def get_model_instance_segmentation(num_classes):
    # load an instance segmentation model pre-trained on COCO
    model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights="DEFAULT")

    # get number of input features for the classifier
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    # replace the pre-trained head with a new one
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

    # now get the number of input features for the mask classifier
    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
    hidden_layer = 256
    # and replace the mask predictor with a new one
    model.roi_heads.mask_predictor = MaskRCNNPredictor(
        in_features_mask,
        hidden_layer,
        num_classes
    )

    return model

就是这样，这将使model准备好在你自定义的数据集上进行训练和评估。

将所有内容整合在一起

在references/detection/中，我们有许多辅助函数来简化训练和评估检测模型。这里，我们将使用references/detection/engine.py和references/detection/utils.py。只需将references/detection下的所有内容下载到您的文件夹中并在此处使用它们。在Linux上，如果您有wget，您可以使用以下命令下载它们：

os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/engine.py")
os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/utils.py")
os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_utils.py")
os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_eval.py")
os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/transforms.py")

自v0.15.0版本以来，torchvision提供了新的Transforms API，以便轻松编写用于目标检测和分割任务的数据增强管道。

让我们为数据增强/转换编写一些辅助函数：

from torchvision.transforms import v2 as T


def get_transform(train):
    transforms = []
    if train:
        transforms.append(T.RandomHorizontalFlip(0.5))
    transforms.append(T.ToDtype(torch.float, scale=True))
    transforms.append(T.ToPureTensor())
    return T.Compose(transforms)

测试 `forward()` 方法（可选）

在遍历数据集之前，最好先看看模型在训练和推理时对样本数据的期望。

import utils

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))
data_loader = torch.utils.data.DataLoader(
    dataset,
    batch_size=2,
    shuffle=True,
    collate_fn=utils.collate_fn
)

# For Training
images, targets = next(iter(data_loader))
images = list(image for image in images)
targets = [{k: v for k, v in t.items()} for t in targets]
output = model(images, targets)  # Returns losses and detections
print(output)

# For inference
model.eval()
x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
predictions = model(x)  # Returns predictions
print(predictions[0])

{'loss_classifier': tensor(0.0808, grad_fn=<NllLossBackward0>), 'loss_box_reg': tensor(0.0284, grad_fn=<DivBackward0>), 'loss_objectness': tensor(0.0186, grad_fn=<BinaryCrossEntropyWithLogitsBackward0>), 'loss_rpn_box_reg': tensor(0.0034, grad_fn=<DivBackward0>)}
{'boxes': tensor([], size=(0, 4), grad_fn=<StackBackward0>), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=<IndexBackward0>)}

现在让我们编写执行训练和验证的主函数：

from engine import train_one_epoch, evaluate

# train on the GPU or on the CPU, if a GPU is not available
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

# our dataset has two classes only - background and person
num_classes = 2
# use our dataset and defined transformations
dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))
dataset_test = PennFudanDataset('data/PennFudanPed', get_transform(train=False))

# split the dataset in train and test set
indices = torch.randperm(len(dataset)).tolist()
dataset = torch.utils.data.Subset(dataset, indices[:-50])
dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])

# define training and validation data loaders
data_loader = torch.utils.data.DataLoader(
    dataset,
    batch_size=2,
    shuffle=True,
    collate_fn=utils.collate_fn
)

data_loader_test = torch.utils.data.DataLoader(
    dataset_test,
    batch_size=1,
    shuffle=False,
    collate_fn=utils.collate_fn
)

# get the model using our helper function
model = get_model_instance_segmentation(num_classes)

# move model to the right device
model.to(device)

# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(
    params,
    lr=0.005,
    momentum=0.9,
    weight_decay=0.0005
)

# and a learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(
    optimizer,
    step_size=3,
    gamma=0.1
)

# let's train it just for 2 epochs
num_epochs = 2

for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
    # update the learning rate
    lr_scheduler.step()
    # evaluate on the test dataset
    evaluate(model, data_loader_test, device=device)

print("That's it!")

Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth

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/var/lib/workspace/intermediate_source/engine.py:30: FutureWarning:

`torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.

Epoch: [0]  [ 0/60]  eta: 0:00:23  lr: 0.000090  loss: 4.9024 (4.9024)  loss_classifier: 0.4325 (0.4325)  loss_box_reg: 0.1060 (0.1060)  loss_mask: 4.3588 (4.3588)  loss_objectness: 0.0028 (0.0028)  loss_rpn_box_reg: 0.0023 (0.0023)  time: 0.3958  data: 0.0135  max mem: 2430
Epoch: [0]  [10/60]  eta: 0:00:11  lr: 0.000936  loss: 1.7743 (2.7696)  loss_classifier: 0.4134 (0.3553)  loss_box_reg: 0.3051 (0.2540)  loss_mask: 0.9491 (2.1320)  loss_objectness: 0.0218 (0.0214)  loss_rpn_box_reg: 0.0056 (0.0069)  time: 0.2267  data: 0.0151  max mem: 2594
Epoch: [0]  [20/60]  eta: 0:00:08  lr: 0.001783  loss: 0.8078 (1.7882)  loss_classifier: 0.2145 (0.2678)  loss_box_reg: 0.2062 (0.2328)  loss_mask: 0.3990 (1.2594)  loss_objectness: 0.0134 (0.0202)  loss_rpn_box_reg: 0.0076 (0.0080)  time: 0.2064  data: 0.0154  max mem: 2628
Epoch: [0]  [30/60]  eta: 0:00:06  lr: 0.002629  loss: 0.6568 (1.4240)  loss_classifier: 0.1409 (0.2251)  loss_box_reg: 0.2294 (0.2425)  loss_mask: 0.2605 (0.9280)  loss_objectness: 0.0122 (0.0186)  loss_rpn_box_reg: 0.0101 (0.0099)  time: 0.2106  data: 0.0163  max mem: 2772
Epoch: [0]  [40/60]  eta: 0:00:04  lr: 0.003476  loss: 0.5629 (1.2055)  loss_classifier: 0.0928 (0.1906)  loss_box_reg: 0.2512 (0.2357)  loss_mask: 0.2267 (0.7537)  loss_objectness: 0.0076 (0.0156)  loss_rpn_box_reg: 0.0119 (0.0098)  time: 0.2101  data: 0.0167  max mem: 2772
Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3608 (1.0399)  loss_classifier: 0.0590 (0.1624)  loss_box_reg: 0.1578 (0.2174)  loss_mask: 0.1602 (0.6378)  loss_objectness: 0.0019 (0.0130)  loss_rpn_box_reg: 0.0071 (0.0093)  time: 0.2051  data: 0.0161  max mem: 2772
Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3463 (0.9410)  loss_classifier: 0.0383 (0.1445)  loss_box_reg: 0.1258 (0.2049)  loss_mask: 0.1596 (0.5712)  loss_objectness: 0.0015 (0.0115)  loss_rpn_box_reg: 0.0064 (0.0089)  time: 0.2012  data: 0.0153  max mem: 2772
Epoch: [0] Total time: 0:00:12 (0.2094 s / it)
creating index...
index created!
Test:  [ 0/50]  eta: 0:00:04  model_time: 0.0785 (0.0785)  evaluator_time: 0.0074 (0.0074)  time: 0.0988  data: 0.0124  max mem: 2772
Test:  [49/50]  eta: 0:00:00  model_time: 0.0420 (0.0571)  evaluator_time: 0.0049 (0.0072)  time: 0.0641  data: 0.0097  max mem: 2772
Test: Total time: 0:00:03 (0.0755 s / it)
Averaged stats: model_time: 0.0420 (0.0571)  evaluator_time: 0.0049 (0.0072)
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.645
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.984
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.854
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.282
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.699
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.699
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.692
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709
IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.669
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.975
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.793
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.394
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.685
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.290
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.720
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.724
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.633
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.734
Epoch: [1]  [ 0/60]  eta: 0:00:11  lr: 0.005000  loss: 0.2605 (0.2605)  loss_classifier: 0.0164 (0.0164)  loss_box_reg: 0.0640 (0.0640)  loss_mask: 0.1767 (0.1767)  loss_objectness: 0.0001 (0.0001)  loss_rpn_box_reg: 0.0032 (0.0032)  time: 0.1859  data: 0.0200  max mem: 2772
Epoch: [1]  [10/60]  eta: 0:00:10  lr: 0.005000  loss: 0.3323 (0.3736)  loss_classifier: 0.0424 (0.0505)  loss_box_reg: 0.1275 (0.1469)  loss_mask: 0.1594 (0.1662)  loss_objectness: 0.0008 (0.0017)  loss_rpn_box_reg: 0.0077 (0.0082)  time: 0.2085  data: 0.0170  max mem: 2772
Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.3343 (0.3471)  loss_classifier: 0.0412 (0.0440)  loss_box_reg: 0.1203 (0.1213)  loss_mask: 0.1660 (0.1731)  loss_objectness: 0.0009 (0.0016)  loss_rpn_box_reg: 0.0068 (0.0070)  time: 0.2051  data: 0.0155  max mem: 2772
Epoch: [1]  [30/60]  eta: 0:00:06  lr: 0.005000  loss: 0.3024 (0.3285)  loss_classifier: 0.0358 (0.0442)  loss_box_reg: 0.0852 (0.1143)  loss_mask: 0.1521 (0.1616)  loss_objectness: 0.0009 (0.0015)  loss_rpn_box_reg: 0.0045 (0.0068)  time: 0.2044  data: 0.0155  max mem: 2772
Epoch: [1]  [40/60]  eta: 0:00:04  lr: 0.005000  loss: 0.2724 (0.3243)  loss_classifier: 0.0425 (0.0435)  loss_box_reg: 0.0852 (0.1082)  loss_mask: 0.1456 (0.1638)  loss_objectness: 0.0012 (0.0016)  loss_rpn_box_reg: 0.0051 (0.0071)  time: 0.2043  data: 0.0161  max mem: 2772
Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2579 (0.3127)  loss_classifier: 0.0328 (0.0416)  loss_box_reg: 0.0590 (0.1009)  loss_mask: 0.1590 (0.1619)  loss_objectness: 0.0016 (0.0017)  loss_rpn_box_reg: 0.0040 (0.0066)  time: 0.2028  data: 0.0151  max mem: 2772
Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2166 (0.2985)  loss_classifier: 0.0293 (0.0406)  loss_box_reg: 0.0522 (0.0942)  loss_mask: 0.1260 (0.1557)  loss_objectness: 0.0008 (0.0016)  loss_rpn_box_reg: 0.0035 (0.0063)  time: 0.2050  data: 0.0157  max mem: 2772
Epoch: [1] Total time: 0:00:12 (0.2046 s / it)
creating index...
index created!
Test:  [ 0/50]  eta: 0:00:02  model_time: 0.0410 (0.0410)  evaluator_time: 0.0038 (0.0038)  time: 0.0579  data: 0.0126  max mem: 2772
Test:  [49/50]  eta: 0:00:00  model_time: 0.0396 (0.0405)  evaluator_time: 0.0030 (0.0040)  time: 0.0544  data: 0.0096  max mem: 2772
Test: Total time: 0:00:02 (0.0556 s / it)
Averaged stats: model_time: 0.0396 (0.0405)  evaluator_time: 0.0030 (0.0040)
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.767
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.986
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.933
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.702
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.783
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.331
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.810
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.810
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.792
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.822
IoU metric: segm
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.731
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.980
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.899
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.421
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.575
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.749
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.777
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.777
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.533
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.789
That's it!

因此，经过一个训练周期后，我们获得了COCO风格的mAP大于50，以及一个65的mask mAP。

但是预测结果是什么样的呢？让我们从数据集中取一张图片来验证

import matplotlib.pyplot as plt

from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks


image = read_image("data/PennFudanPed/PNGImages/FudanPed00046.png")
eval_transform = get_transform(train=False)

model.eval()
with torch.no_grad():
    x = eval_transform(image)
    # convert RGBA -> RGB and move to device
    x = x[:3, ...].to(device)
    predictions = model([x, ])
    pred = predictions[0]


image = (255.0 * (image - image.min()) / (image.max() - image.min())).to(torch.uint8)
image = image[:3, ...]
pred_labels = [f"pedestrian: {score:.3f}" for label, score in zip(pred["labels"], pred["scores"])]
pred_boxes = pred["boxes"].long()
output_image = draw_bounding_boxes(image, pred_boxes, pred_labels, colors="red")

masks = (pred["masks"] > 0.7).squeeze(1)
output_image = draw_segmentation_masks(output_image, masks, alpha=0.5, colors="blue")


plt.figure(figsize=(12, 12))
plt.imshow(output_image.permute(1, 2, 0))

<matplotlib.image.AxesImage object at 0x7f4edb52d7b0>

结果看起来不错！

总结

在本教程中，您学习了如何为自定义数据集上的目标检测模型创建自己的训练管道。为此，您编写了一个torch.utils.data.Dataset类，该类返回图像、真实框和分割掩码。您还利用了在COCO train2017上预训练的Mask R-CNN模型，以便在这个新数据集上进行迁移学习。

有关更完整的示例，包括多机/多GPU训练，请查看references/detection/train.py，该文件存在于torchvision仓库中。

脚本总运行时间： ( 0 分钟 46.025 秒)

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