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PyTorch TorchVision 对象检测微调教程
2020-09-07 10:04:53 更新
提示
为了充分利用本教程,我们建议使用此Colab版本。这将允许您尝试下面提供的信息。
在本教程中，我们将在宾夕法尼亚复旦数据库中微调预先训练的 Mask R-CNN 模型，以进行行人检测和分割。它包含 170 张包含 345 个行人实例的图像，我们将使用它来说明如何使用 torchvision 中的新功能，以便在自定义数据集上训练实例分割模型。
定义数据集
用于训练对象检测、实例分段和人员关键点检测的参考脚本可以轻松支持添加新的自定义数据集。数据集应继承自标准类torch.utils.data.Dataset，并实现 __len__和__getitem__。
我们要求的唯一特异性是数据集应返回：__getitem__
- 图像：PIL 大小的图像
(H, W) - 目标：包含以下字段的字典
boxe（FloatTensor[N，4]）：N个边界框的坐标，格式为[x0，y0，x1，y1]，范围从0到W，从0到H.labels（Int64Tensor[N]）：每个边界框的标签。0始终代表后台类。image_id（Int64Tensor[1]）：图像标识符。它在数据集中的所有图像之间应该是唯一的，并在评估期间使用area (Tensor[N]):边界框的面积。在使用COCO度量进行评估时，会使用它来区分小盒子、中盒子和大盒子之间的度量分数。iscrowd（UInt8Tensor[N]）：iscrowd=True的实例将在计算期间被忽略。- （可选）
masks（UInt8Tensor[N，H，W]）：每个对象的分割遮罩. - （可选）
keypoints（FloatTensor[N，K，3]）：对于N个对象中的每一个，它包含[x，y，visibility]格式的K个关键点，定义对象。可见性=0表示关键点不可见。请注意，对于数据扩充，翻转关键点的概念取决于数据表示，您应该调整references/detection/transforms.py用于新的关键点表示
如果您的模型返回上述方法，它们将使培训和评估都有效，并将使用Pycocotools中的评估脚本，该脚本可以与pip install Pycools一起安装。
注意
对于Windows，请使用命令从gautamchitnis安装pycocotoolspip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
labels上有一个注释。该模型将0类视为背景。如果数据集不包含后台类，则labels中不应包含0。例如，假设只有两个类，cat和dog，可以定义1（而不是0）来表示cat，定义2来表示dogs。例如，如果其中一个图像同时具有两个类，那么labels张量应该类似于[1,2]。
此外，如果希望在训练期间使用纵横比分组（以便每个批次仅包含具有类似纵横比的图像），则建议还实现get_height_和_width方法，该方法返回图像的高度和宽度。如果不提供此方法，我们将通过__getitem__查询数据集的所有元素，__getitem__将图像加载到内存中，速度比提供自定义方法慢。
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。下载并解压缩zip文件后，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下面是一对图像和分割蒙版的一个示例
因此，每个图像都有一个相应的分割遮罩，每个颜色对应一个不同的实例。让我们为这个数据集编写一个torch.utils.data.Dataset类。
import os
import numpy as np
import torch
from PIL import Image
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 = Image.open(img_path).convert("RGB")
# note that we haven't converted the mask to RGB,
# because each color corresponds to a different instance
# with 0 being background
mask = Image.open(mask_path)
# convert the PIL Image into a numpy array
mask = np.array(mask)
# instances are encoded as different colors
obj_ids = np.unique(mask)
# first id is the background, so remove it
obj_ids = obj_ids[1:]
# split the color-encoded mask into a set
# of binary masks
masks = mask == obj_ids[:, None, None]
# get bounding box coordinates for each mask
num_objs = len(obj_ids)
boxes = []
for i in range(num_objs):
pos = np.where(masks[i])
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
boxes.append([xmin, ymin, xmax, ymax])
# convert everything into a torch.Tensor
boxes = torch.as_tensor(boxes, dtype=torch.float32)
# there is only one class
labels = torch.ones((num_objs,), dtype=torch.int64)
masks = torch.as_tensor(masks, dtype=torch.uint8)
image_id = torch.tensor([idx])
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
# suppose all instances are not crowd
iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["masks"] = masks
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)这就是数据集的全部内容。现在，让我们定义一个可以对此数据集执行预测的模型。
定义模型
在本教程中，我们将使用Mask R-CNN，它基于更快的R-CNN。更快的R-CNN模型可以预测图像中潜在对象的边界框和类分数。
Mask R-CNN为更快的R-CNN添加了一个额外分支，该分支还可以预测每个实例的分段掩码。
在两种常见情况下，可能需要修改torchvision modelzoo中的一个可用模型。第一个是当我们想从一个预先训练好的模型开始，只需微调最后一层。另一个是当我们想用另一个模型替换模型的主干时（例如，为了更快的预测）。
让我们来看看在接下来的部分中我们将如何做一个或另一个。
1 - 从预训练模型微调
假设您要从在 COCO 上预先训练的模型开始，并希望针对您的特定类对其进行微调。以下是一种可能的方法：
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
## load a model pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
## 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)2 - 修改模型以添加不同的主干
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(pretrained=True).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 FasterRCNN model
model = FasterRCNN(backbone,
num_classes=2,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
一种PennFudan数据集的实例分割模型
在我们的例子中，我们希望从预先训练的模型进行微调，因为我们的数据集非常小，所以我们将遵循方法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(pretrained=True)
# 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
就这样，这将使model准备好在自定义数据集上进行训练和评估。
将所有内容放在一起
在references/detection/中，我们有许多帮助函数来简化训练和评估检测模型。在这里，我们将使用references/detection/engine.py，references/detection/utils.py和references/detection/transforms.py。只需将references/detection下的所有内容复制到您的文件夹中，并在此处使用它们。
让我们为数据扩充/转换编写一些帮助函数：
import transforms as T
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
测试forward()方法（可选）
在迭代数据集之前，最好先查看模型在样本数据上的训练和推理时间内的预期内容。
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=2, shuffle=True, num_workers=4,
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
## For inference
model.eval()
x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
predictions = model(x) # Returns predictions现在，让我们编写执行训练和验证的 main 函数：
from engine import train_one_epoch, evaluate
import utils
def main():
# 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('PennFudanPed', get_transform(train=True))
dataset_test = PennFudanDataset('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, num_workers=4,
collate_fn=utils.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=1, shuffle=False, num_workers=4,
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 for 10 epochs
num_epochs = 10
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!")
您应该获得第一个历元的输出：
Epoch: [0] [ 0/60] eta: 0:01:18 lr: 0.000090 loss: 2.5213 (2.5213) loss_classifier: 0.8025 (0.8025) loss_box_reg: 0.2634 (0.2634) loss_mask: 1.4265 (1.4265) loss_objectness: 0.0190 (0.0190) loss_rpn_box_reg: 0.0099 (0.0099) time: 1.3121 data: 0.3024 max mem: 3485
Epoch: [0] [10/60] eta: 0:00:20 lr: 0.000936 loss: 1.3007 (1.5313) loss_classifier: 0.3979 (0.4719) loss_box_reg: 0.2454 (0.2272) loss_mask: 0.6089 (0.7953) loss_objectness: 0.0197 (0.0228) loss_rpn_box_reg: 0.0121 (0.0141) time: 0.4198 data: 0.0298 max mem: 5081
Epoch: [0] [20/60] eta: 0:00:15 lr: 0.001783 loss: 0.7567 (1.1056) loss_classifier: 0.2221 (0.3319) loss_box_reg: 0.2002 (0.2106) loss_mask: 0.2904 (0.5332) loss_objectness: 0.0146 (0.0176) loss_rpn_box_reg: 0.0094 (0.0123) time: 0.3293 data: 0.0035 max mem: 5081
Epoch: [0] [30/60] eta: 0:00:11 lr: 0.002629 loss: 0.4705 (0.8935) loss_classifier: 0.0991 (0.2517) loss_box_reg: 0.1578 (0.1957) loss_mask: 0.1970 (0.4204) loss_objectness: 0.0061 (0.0140) loss_rpn_box_reg: 0.0075 (0.0118) time: 0.3403 data: 0.0044 max mem: 5081
Epoch: [0] [40/60] eta: 0:00:07 lr: 0.003476 loss: 0.3901 (0.7568) loss_classifier: 0.0648 (0.2022) loss_box_reg: 0.1207 (0.1736) loss_mask: 0.1705 (0.3585) loss_objectness: 0.0018 (0.0113) loss_rpn_box_reg: 0.0075 (0.0112) time: 0.3407 data: 0.0044 max mem: 5081
Epoch: [0] [50/60] eta: 0:00:03 lr: 0.004323 loss: 0.3237 (0.6703) loss_classifier: 0.0474 (0.1731) loss_box_reg: 0.1109 (0.1561) loss_mask: 0.1658 (0.3201) loss_objectness: 0.0015 (0.0093) loss_rpn_box_reg: 0.0093 (0.0116) time: 0.3379 data: 0.0043 max mem: 5081
Epoch: [0] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.2540 (0.6082) loss_classifier: 0.0309 (0.1526) loss_box_reg: 0.0463 (0.1405) loss_mask: 0.1568 (0.2945) loss_objectness: 0.0012 (0.0083) loss_rpn_box_reg: 0.0093 (0.0123) time: 0.3489 data: 0.0042 max mem: 5081
Epoch: [0] Total time: 0:00:21 (0.3570 s / it)
creating index...
index created!
Test: [ 0/50] eta: 0:00:19 model_time: 0.2152 (0.2152) evaluator_time: 0.0133 (0.0133) time: 0.4000 data: 0.1701 max mem: 5081
Test: [49/50] eta: 0:00:00 model_time: 0.0628 (0.0687) evaluator_time: 0.0039 (0.0064) time: 0.0735 data: 0.0022 max mem: 5081
Test: Total time: 0:00:04 (0.0828 s / it)
Averaged stats: model_time: 0.0628 (0.0687) evaluator_time: 0.0039 (0.0064)
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 ] = 0.606
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.984
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.780
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.313
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.582
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.270
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.672
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.672
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.755
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.704
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.979
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.871
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.488
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.316
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.748
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.749
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.673
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.758
因此，经过一个时期的训练，我们获得了60.6的COCO式mAP和70.4的掩模mAP。 经过 10 个纪元的训练，我得到了以下指标
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.799
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.969
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.935
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.592
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.324
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.844
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.844
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.777
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.870
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.761
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.969
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.919
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.341
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.788
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.303
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.799
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.799
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.769
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818但是预测是什么样的呢？让我们在数据集中获取一个图像并进行验证
经过训练的模型预测了此图像中的 9 个人员实例，让我们看一下其中的几个实例：
结果看起来相当不错！
结束语
在本教程中，您学习了如何在自定义数据集上创建自己的培训管道，例如分段模型。为此，你写了一个torch.utils.data.Dataset类，该类返回图像、地面真值框和分割遮罩。为了在这个新数据集上执行转移学习，您还利用了COCO train2017上预先培训的Mask R-CNN模型。
有关更完整的示例，包括多机/多gpu培训，请查看references/detection/train.py，它出现在torchvision回购协议中。
在此处下载本教程的完整源文件。