GluonCV：用Pascal VOC数据训练YOLO v3（下）训练部分
接上篇，GluonCV：用Pascal VOC数据训练YOLO v3（上）
本教程介绍了训练GluonCV提供的YOLOv3目标检测模型的基本步骤。
具体来说，展示了如何通过堆叠GluonCV组件来构建state-of-the-art的YOLOv3模型。
首先，关于训练有三点说明：
（1）初始学习率默认是0.001，我训练loss是nan，改为0.0001解决；
（2）如果八卡1080Ti，默认batch-size=64即可，四卡改成32，其他依次类推；
（3）关于下面的情况
[07:40:55] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the bestconvolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0to disable)
# 解决方法
# 运行train之前，输入
export MXNET_CUDNN_AUTOTUNE_DEFAULT = 01.数据集
请首先阅读上篇教程，在磁盘上准备好Pascal VOC数据集。
然后，我们准备加载训练和验证图像。
import gluoncv as gcv
from gluoncv.data import VOCDetection
# typically we use 2007+2012 trainval splits for training data
train_dataset = VOCDetection(splits=[(2007, 'trainval'), (2012, 'trainval')])
# and use 2007 test as validation data
val_dataset = VOCDetection(splits=[(2007, 'test')])
print('Training images:', len(train_dataset))
print('Validation images:', len(val_dataset))
Out:
Training images: 16551
Validation images: 49522. Data transform
我们可以从训练数据集中读取图像-标签对
train_image, train_label = train_dataset[80]
bboxes = train_label[:, :4]
cids = train_label[:, 4:5]
print('image:', train_image.shape)
print('bboxes:', bboxes.shape, 'class ids:', cids.shape)
Out:
image: (375, 500, 3)
bboxes: (2, 4) class ids: (2, 1)用matplotlib绘制图像以及边界框标签：
from matplotlib import pyplot as plt
from gluoncv.utils import viz
ax = viz.plot_bbox(train_image.asnumpy(), bboxes, labels=cids, class_names=train_dataset.classes)
plt.show()
验证图像与训练非常相似，因为它们基本上是随机分成不同的集合
val_image, val_label = val_dataset[120]
bboxes = val_label[:, :4]
cids = val_label[:, 4:5]
ax = viz.plot_bbox(val_image.asnumpy(), bboxes, labels=cids, class_names=train_dataset.classes)
plt.show()
Transform
from gluoncv.data.transforms import presets
from gluoncv import utils
from mxnet import nd
width, height = 416, 416 # resize image to 416x416 after all data augmentation
train_transform = presets.yolo.YOLO3DefaultTrainTransform(width, height)
val_transform = presets.yolo.YOLO3DefaultValTransform(width, height)
utils.random.seed(123) # fix seed in this tutorial
将变换应用于训练图像
train_image2, train_label2 = train_transform(train_image, train_label)
print('tensor shape:', train_image2.shape)
Out:
tensor shape: (3, 416, 416)张量中的图像被扭曲，因为它们不再位于（0,255）范围内。 让我们把它们转换回去，这样我们就能清楚地看到它们。
train_image2 = train_image2.transpose((1, 2, 0)) * nd.array((0.229, 0.224, 0.225)) + nd.array((0.485, 0.456, 0.406))
train_image2 = (train_image2 * 255).clip(0, 255)
ax = viz.plot_bbox(train_image2.asnumpy(), train_label2[:, :4],
labels=train_label2[:, 4:5],
class_names=train_dataset.classes)
plt.show()
训练中使用的变换包括随机颜色失真，随机扩展/裁剪，随机翻转，调整大小和固定颜色标准化。 相比之下，验证仅涉及调整大小和颜色标准化。
3.Data Loader
我们将在训练期间多次遍历整个数据集。 请记住，在将原始图像输入神经网络之前，必须将原始图像转换为张量（mxnet使用BCHW格式）。
一个方便的DataLoader非常方便我们将不同的转换和聚合数据应用到mini-batches中。
因为目标的数量在图像间变化很大，所以我们也有不同的标签大小。 因此，我们需要将这些标签填充到相同的大小。 为了解决这个问题，GluonCV提供了gluoncv.data.batchify.Pad，它可以自动处理填充。 还有gluoncv.data.batchify.Stack，用于堆叠具有一致形状的NDArrays。 gluoncv.data.batchify.Tuple用于处理来自转换函数的多个输出的不同行为。
from gluoncv.data.batchify import Tuple, Stack, Pad
from mxnet.gluon.data import DataLoader
batch_size = 2 # for tutorial, we use smaller batch-size
num_workers = 0 # you can make it larger(if your CPU has more cores) to accelerate data loading
# behavior of batchify_fn: stack images, and pad labels
batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
train_loader = DataLoader(train_dataset.transform(train_transform), batch_size, shuffle=True,
batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
val_loader = DataLoader(val_dataset.transform(val_transform), batch_size, shuffle=False,
batchify_fn=batchify_fn, last_batch='keep', num_workers=num_workers)
for ib, batch in enumerate(train_loader):
if ib > 3:
break
print('data 0:', batch[0][0].shape, 'label 0:', batch[1][0].shape)
print('data 1:', batch[0][1].shape, 'label 1:', batch[1][1].shape)
Out:
data 0: (3, 416, 416) label 0: (6, 6)
data 1: (3, 416, 416) label 1: (6, 6)
data 0: (3, 416, 416) label 0: (3, 6)
data 1: (3, 416, 416) label 1: (3, 6)
data 0: (3, 416, 416) label 0: (2, 6)
data 1: (3, 416, 416) label 1: (2, 6)
data 0: (3, 416, 416) label 0: (2, 6)
data 1: (3, 416, 416) label 1: (2, 6)4. YOLOv3 Network
GluonCV的YOLOv3实现是综合的Gluon HybridBlock。 在结构方面，YOLOv3网络由基本特征提取网络，卷积过渡层，上采样层和专门设计的YOLOv3输出层组成。
Gluon Model Zoo有一些内置的YOLO网络，可以使用一行简单的代码加载：
（为避免在本教程中下载mdoel，我们设置pretrained_base = False，实际上我们通常希望通过设置pretrained_base = True来加载预先训练的imagenet模型。）
from gluoncv import model_zoo
net = model_zoo.get_model('yolo3_darknet53_voc', pretrained_base=False)
print(net)
Out:
YOLOV3(
(_target_generator): YOLOV3TargetMerger(
(_dynamic_target): YOLOV3DynamicTargetGeneratorSimple(
(_batch_iou): BBoxBatchIOU(
(_pre): BBoxSplit(
)
)
)
)
(_loss): YOLOV3Loss(batch_axis=0, w=None)
(stages): HybridSequential(
(0): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(2): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(3): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(4): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(5): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(6): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(7): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(8): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(9): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(10): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(11): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(12): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(13): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(14): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
)
(1): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(2): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(3): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(4): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(5): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(6): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(7): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(8): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
)
(2): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(2): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(3): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(4): DarknetBasicBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
)
)
(transitions): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(yolo_blocks): HybridSequential(
(0): YOLODetectionBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(2): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(3): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(4): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(tip): HybridSequential(
(0): Conv2D(None -> 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(1): YOLODetectionBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(2): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(3): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(4): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(tip): HybridSequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(2): YOLODetectionBlockV3(
(body): HybridSequential(
(0): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(1): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(2): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(3): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
(4): HybridSequential(
(0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
(tip): HybridSequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)
(2): LeakyReLU(0.1)
)
)
)
(yolo_outputs): HybridSequential(
(0): YOLOOutputV3(
(prediction): Conv2D(None -> 75, kernel_size=(1, 1), stride=(1, 1))
)
(1): YOLOOutputV3(
(prediction): Conv2D(None -> 75, kernel_size=(1, 1), stride=(1, 1))
)
(2): YOLOOutputV3(
(prediction): Conv2D(None -> 75, kernel_size=(1, 1), stride=(1, 1))
)
)
)YOLOv3网络可以使用图像张量进行调用
import mxnet as mx
x = mx.nd.zeros(shape=(1, 3, 416, 416))
net.initialize()
cids, scores, bboxes = net(x)YOLOv3返回三个值，其中cids是类标签，scores是每个预测的置信度分数，而bbox是相应边界框的绝对坐标。
5. 训练目标
端到端YOLOv3训练涉及四个损失。 损失惩罚不正确的class/box预测，并在gluoncv.loss.YOLOV3Loss中定义
loss = gcv.loss.YOLOV3Loss()
# which is already included in YOLOv3 network
print(net._loss)
Out:
YOLOV3Loss(batch_axis=0, w=None)为了加速训练，我们让CPU预先计算一些训练目标。 当CPU功能强大且可以使用 -j num_workers来使用多核CPU时，这一点尤为出色。
如果我们为训练变换函数提供网络，它将计算部分训练目标
from mxnet import autograd
train_transform = presets.yolo.YOLO3DefaultTrainTransform(width, height, net)
# return stacked images, center_targets, scale_targets, gradient weights, objectness_targets, class_targets
# additionally, return padded ground truth bboxes, so there are 7 components returned by dataloader
batchify_fn = Tuple(*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_loader = DataLoader(train_dataset.transform(train_transform), batch_size, shuffle=True,
batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
for ib, batch in enumerate(train_loader):
if ib > 0:
break
print('data:', batch[0][0].shape)
print('label:', batch[6][0].shape)
with autograd.record():
input_order = [0, 6, 1, 2, 3, 4, 5]
obj_loss, center_loss, scale_loss, cls_loss = net(*[batch[o] for o in input_order])
# sum up the losses
# some standard gluon training steps:
# autograd.backward(sum_loss)
# trainer.step(batch_size)
Out:
data: (3, 416, 416)
label: (4, 4)我们可以看到data loader实际上正在为我们返回训练目标。 然后加载数据和Gluon训练很自然地循环，并让它更新权重。
参考资料
-
GluonCV Github
-
准备pascal VOC数据集
-
训练yolo v3
-
Linux终端没有GUI，如何使用matplotlib绘图
-
Redmon, Joseph, and Ali Farhadi. “Yolov3: An incremental improvement.” arXiv preprint arXiv:1804.02767 (2018).