在图像处理中，关键点本质上是一种特征。它是对一个固定区域或者空间物理关系的抽象描述，描述的是一定邻域范围内的组合或上下文关系。它不仅仅是一个点信息，或代表一个位置，更代表着上下文与周围邻域的组合关系。关键点检测的目标就是通过计算机从图像中找出这些点的坐标，作为计算机视觉领域的一个基础任务，关键点的检测对于高级别任务，例如识别和分类具有至关重要的意义。
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本示例教程当前是基于2.0-rc版本Paddle做的案例实现，未来会随着2.0的系列版本发布进行升级。
关键点检测方法总体上可以分成两个类型，一个种是用坐标回归的方式来解决，另一种是将关键点建模成热力图，通过像素分类任务，回归热力图分布得到关键点位置。这两个方法，都是一种手段或者是途径，解决的问题就是要找出这个点在图像当中的位置与关系。
其中人脸关键点检测是关键点检测方法的一个成功实践，本示例简要介绍如何通过飞桨开源框架，实现人脸关键点检测的功能。这个案例用到的是第一种关键点检测方法——坐标回归。我们将使用到新版的Paddle2.0的API，集成式的训练接口，能够很方便对模型进行训练和预测。
导入必要的模块，确认自己的飞桨版本。 如果是cpu环境，请安装cpu版本的Paddle2.0环境，在 paddle.set_device() 输入对应运行设备。
import numpy as npimport matplotlib.pyplot as pltimport pandas as pdimport osimport paddlefrom paddle.io import Datasetfrom paddle.vision.transforms import transformsfrom paddle.vision.models import resnet18from paddle.nn import functional as Fprint(paddle.__version__)# device = paddle.set_device('gpu') device = paddle.set_device('cpu')
# if use static graph, do not setpaddle.disable_static(device)2.0.0-rc0
!unzip -o data/data60/test.zip -d data/data60/ !unzip -o data/data60/training.zip -d data/data60
unzip: cannot find or open data/data60/test.zip, data/data60/test.zip.zip or data/data60/test.zip.ZIP.
飞桨（PaddlePaddle）数据集加载方案是统一使用Dataset（数据集定义） + DataLoader（多进程数据集加载）。
首先我们先进行数据集的定义，数据集定义主要是实现一个新的Dataset类，继承父类paddle.io.Dataset，并实现父类中以下两个抽象方法，getitem__和__len：
Train_Dir = 'data/data60//training.csv'Test_Dir = 'data/data60//test.csv'lookid_dir = 'data/data60/IdLookupTable.csv'class ImgTransforms(object):
"""
图像预处理工具，用于将图像进行升维(96, 96) => (96, 96, 3)，
并对图像的维度进行转换从HWC变为CHW
"""
def __init__(self, fmt):
self.format = fmt def __call__(self, img):
if len(img.shape) == 2:
img = np.expand_dims(img, axis=2)
img = img.transpose(self.format) if img.shape[0] == 1:
img = np.repeat(img, 3, axis=0) return imgclass FaceDataset(Dataset):
def __init__(self, data_path, mode='train', val_split=0.2):
self.mode = mode assert self.mode in ['train', 'val', 'test'], \ "mode should be 'train' or 'test', but got {}".format(self.mode)
self.data_source = pd.read_csv(data_path) # 清洗数据, 数据集中有很多样本只标注了部分关键点, 这里有两种策略
# 第一种, 将未标注的位置从上一个样本对应的关键点复制过来
# self.data_source.fillna(method = 'ffill',inplace = True)
# 第二种, 将包含有未标注的样本从数据集中移除
self.data_source.dropna(how="any", inplace=True)
self.data_label_all = self.data_source.drop('Image', axis = 1)
# 划分训练集和验证集合
if self.mode in ['train', 'val']:
np.random.seed(43)
data_len = len(self.data_source) # 随机划分
shuffled_indices = np.random.permutation(data_len) # 顺序划分
# shuffled_indices = np.arange(data_len)
self.shuffled_indices = shuffled_indices
val_set_size = int(data_len*val_split) if self.mode == 'val':
val_indices = shuffled_indices[:val_set_size]
self.data_img = self.data_source.reindex().iloc[val_indices]
self.data_label = self.data_label_all.reindex().iloc[val_indices] elif self.mode == 'train':
train_indices = shuffled_indices[val_set_size:]
self.data_img = self.data_source.reindex().iloc[train_indices]
self.data_label = self.data_label_all.reindex().iloc[train_indices] elif self.mode == 'test':
self.data_img = self.data_source
self.data_label = self.data_label_all
self.transforms = transforms.Compose([
ImgTransforms((2, 0, 1))
]) # 每次迭代时返回数据和对应的标签
def __getitem__(self, idx):
img = self.data_img['Image'].iloc[idx].split(' ')
img = ['0' if x == '' else x for x in img]
img = np.array(img, dtype = 'float32').reshape(96, 96)
img = self.transforms(img)
label = np.array(self.data_label.iloc[idx,:],dtype = 'float32')/96
return img, label # 返回整个数据集的总数
def __len__(self):
return len(self.data_img)# 训练数据集和验证数据集train_dataset = FaceDataset(Train_Dir, mode='train')
val_dataset = FaceDataset(Train_Dir, mode='val')# 测试数据集test_dataset = FaceDataset(Test_Dir, mode='test')实现好Dataset数据集后，我们来测试一下数据集是否符合预期，因为Dataset是一个可以被迭代的Class，我们通过for循环从里面读取数据来用matplotlib进行展示。关键点的坐标在数据集中进行了归一化处理，这里乘以图像的大小恢复到原始尺度，并用scatter函数将点画在输出的图像上。
def plot_sample(x, y, axis):
img = x.reshape(96, 96)
axis.imshow(img, cmap='gray')
axis.scatter(y[0::2], y[1::2], marker='x', s=10, color='b')
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(
left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)# 随机取16个样本展示for i in range(16):
axis = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[])
idx = np.random.randint(train_dataset.__len__()) # print(idx)
img, label = train_dataset[idx]
label = label*96
plot_sample(img[0], label, axis)
plt.show()<Figure size 720x504 with 16 Axes>
这里使用到paddle.vision.models中定义的resnet18网络模型。在ImageNet分类任务中，图像分成1000类，在模型后接一个全连接层，将输出的1000维向量映射成30维，对应15个关键点的横纵坐标。
class FaceNet(paddle.nn.Layer):
def __init__(self, num_keypoints, pretrained=False):
super(FaceNet, self).__init__()
self.backbone = resnet18(pretrained)
self.outLayer1 = paddle.nn.Sequential(
paddle.nn.Linear(1000, 512),
paddle.nn.ReLU(),
paddle.nn.Dropout(0.1))
self.outLayer2 = paddle.nn.Linear(512, num_keypoints*2) def forward(self, inputs):
out = self.backbone(inputs)
out = self.outLayer1(out)
out = self.outLayer2(out) return out调用飞桨提供的summary接口对组建好的模型进行可视化，方便进行模型结构和参数信息的查看和确认。
from paddle.static import InputSpec paddle.disable_static() num_keypoints = 15model = paddle.Model(FaceNet(num_keypoints))# 输入数据大小：batch_size: 3, channel:3, width:96, height:96model.summary((3, 3, 96, 96))
-------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===============================================================================
Conv2D-1 [[3, 3, 96, 96]] [3, 64, 48, 48] 9,408
BatchNorm2D-1 [[3, 64, 48, 48]] [3, 64, 48, 48] 256
ReLU-1 [[3, 64, 48, 48]] [3, 64, 48, 48] 0
MaxPool2D-1 [[3, 64, 48, 48]] [3, 64, 24, 24] 0
Conv2D-2 [[3, 64, 24, 24]] [3, 64, 24, 24] 36,864
BatchNorm2D-2 [[3, 64, 24, 24]] [3, 64, 24, 24] 256
ReLU-2 [[3, 64, 24, 24]] [3, 64, 24, 24] 0
Conv2D-3 [[3, 64, 24, 24]] [3, 64, 24, 24] 36,864
BatchNorm2D-3 [[3, 64, 24, 24]] [3, 64, 24, 24] 256
BasicBlock-1 [[3, 64, 24, 24]] [3, 64, 24, 24] 0
Conv2D-4 [[3, 64, 24, 24]] [3, 64, 24, 24] 36,864
BatchNorm2D-4 [[3, 64, 24, 24]] [3, 64, 24, 24] 256
ReLU-3 [[3, 64, 24, 24]] [3, 64, 24, 24] 0
Conv2D-5 [[3, 64, 24, 24]] [3, 64, 24, 24] 36,864
BatchNorm2D-5 [[3, 64, 24, 24]] [3, 64, 24, 24] 256
BasicBlock-2 [[3, 64, 24, 24]] [3, 64, 24, 24] 0
Conv2D-7 [[3, 64, 24, 24]] [3, 128, 12, 12] 73,728
BatchNorm2D-7 [[3, 128, 12, 12]] [3, 128, 12, 12] 512
ReLU-4 [[3, 128, 12, 12]] [3, 128, 12, 12] 0
Conv2D-8 [[3, 128, 12, 12]] [3, 128, 12, 12] 147,456
BatchNorm2D-8 [[3, 128, 12, 12]] [3, 128, 12, 12] 512
Conv2D-6 [[3, 64, 24, 24]] [3, 128, 12, 12] 8,192
BatchNorm2D-6 [[3, 128, 12, 12]] [3, 128, 12, 12] 512
BasicBlock-3 [[3, 64, 24, 24]] [3, 128, 12, 12] 0
Conv2D-9 [[3, 128, 12, 12]] [3, 128, 12, 12] 147,456
BatchNorm2D-9 [[3, 128, 12, 12]] [3, 128, 12, 12] 512
ReLU-5 [[3, 128, 12, 12]] [3, 128, 12, 12] 0
Conv2D-10 [[3, 128, 12, 12]] [3, 128, 12, 12] 147,456
BatchNorm2D-10 [[3, 128, 12, 12]] [3, 128, 12, 12] 512
BasicBlock-4 [[3, 128, 12, 12]] [3, 128, 12, 12] 0
Conv2D-12 [[3, 128, 12, 12]] [3, 256, 6, 6] 294,912
BatchNorm2D-12 [[3, 256, 6, 6]] [3, 256, 6, 6] 1,024
ReLU-6 [[3, 256, 6, 6]] [3, 256, 6, 6] 0
Conv2D-13 [[3, 256, 6, 6]] [3, 256, 6, 6] 589,824
BatchNorm2D-13 [[3, 256, 6, 6]] [3, 256, 6, 6] 1,024
Conv2D-11 [[3, 128, 12, 12]] [3, 256, 6, 6] 32,768
BatchNorm2D-11 [[3, 256, 6, 6]] [3, 256, 6, 6] 1,024
BasicBlock-5 [[3, 128, 12, 12]] [3, 256, 6, 6] 0
Conv2D-14 [[3, 256, 6, 6]] [3, 256, 6, 6] 589,824
BatchNorm2D-14 [[3, 256, 6, 6]] [3, 256, 6, 6] 1,024
ReLU-7 [[3, 256, 6, 6]] [3, 256, 6, 6] 0
Conv2D-15 [[3, 256, 6, 6]] [3, 256, 6, 6] 589,824
BatchNorm2D-15 [[3, 256, 6, 6]] [3, 256, 6, 6] 1,024
BasicBlock-6 [[3, 256, 6, 6]] [3, 256, 6, 6] 0
Conv2D-17 [[3, 256, 6, 6]] [3, 512, 3, 3] 1,179,648
BatchNorm2D-17 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,048
ReLU-8 [[3, 512, 3, 3]] [3, 512, 3, 3] 0
Conv2D-18 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,359,296
BatchNorm2D-18 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,048
Conv2D-16 [[3, 256, 6, 6]] [3, 512, 3, 3] 131,072
BatchNorm2D-16 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,048
BasicBlock-7 [[3, 256, 6, 6]] [3, 512, 3, 3] 0
Conv2D-19 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,359,296
BatchNorm2D-19 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,048
ReLU-9 [[3, 512, 3, 3]] [3, 512, 3, 3] 0
Conv2D-20 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,359,296
BatchNorm2D-20 [[3, 512, 3, 3]] [3, 512, 3, 3] 2,048
BasicBlock-8 [[3, 512, 3, 3]] [3, 512, 3, 3] 0
AdaptiveAvgPool2D-1 [[3, 512, 3, 3]] [3, 512, 1, 1] 0
Linear-1 [[3, 512]] [3, 1000] 513,000
ResNet-1 [[3, 3, 96, 96]] [3, 1000] 0
Linear-2 [[3, 1000]] [3, 512] 512,512
ReLU-10 [[3, 512]] [3, 512] 0
Dropout-1 [[3, 512]] [3, 512] 0
Linear-3 [[3, 512]] [3, 30] 15,390
===============================================================================
Total params: 12,227,014
Trainable params: 12,207,814
Non-trainable params: 19,200
-------------------------------------------------------------------------------
Input size (MB): 0.32
Forward/backward pass size (MB): 31.52
Params size (MB): 46.64
Estimated Total Size (MB): 78.48
-------------------------------------------------------------------------------{'total_params': 12227014, 'trainable_params': 12207814}在这个任务是对坐标进行回归，我们使用均方误差（Mean Square error ）损失函数paddle.nn.MSELoss()来做计算，飞桨2.0中，在nn下将损失函数封装成可调用类。我们这里使用paddle.Model相关的API直接进行训练，只需要定义好数据集、网络模型和损失函数即可。
使用模型代码进行Model实例生成，使用prepare接口定义优化器、损失函数和评价指标等信息，用于后续训练使用。在所有初步配置完成后，调用fit接口开启训练执行过程，调用fit时只需要将前面定义好的训练数据集、测试数据集、训练轮次（Epoch）和批次大小（batch_size）配置好即可。
model = paddle.Model(FaceNet(num_keypoints=15))
optim = paddle.optimizer.Adam(learning_rate=1e-3,
parameters=model.parameters())
model.prepare(optim, paddle.nn.MSELoss())
model.fit(train_dataset, val_dataset, epochs=60, batch_size=256)Epoch 1/60 step 7/7 - loss: 0.2203 - 570ms/step Eval begin... step 2/2 - loss: 0.2003 - 476ms/step Eval samples: 428 Epoch 2/60 step 7/7 - loss: 0.1293 - 574ms/step Eval begin... step 2/2 - loss: 0.1363 - 454ms/step Eval samples: 428 Epoch 3/60 step 7/7 - loss: 0.0499 - 799ms/step Eval begin... step 2/2 - loss: 0.0530 - 455ms/step Eval samples: 428 Epoch 4/60 step 7/7 - loss: 0.0128 - 557ms/step Eval begin... step 2/2 - loss: 0.0124 - 454ms/step Eval samples: 428 Epoch 5/60 step 7/7 - loss: 0.0079 - 555ms/step Eval begin... step 2/2 - loss: 0.0068 - 474ms/step Eval samples: 428 Epoch 6/60 step 7/7 - loss: 0.0028 - 573ms/step Eval begin... step 2/2 - loss: 0.0046 - 454ms/step Eval samples: 428 Epoch 7/60 step 7/7 - loss: 0.0021 - 559ms/step Eval begin... step 2/2 - loss: 0.0046 - 469ms/step Eval samples: 428 Epoch 8/60 step 7/7 - loss: 0.0020 - 560ms/step Eval begin... step 2/2 - loss: 0.0024 - 461ms/step Eval samples: 428 Epoch 9/60 step 7/7 - loss: 0.0018 - 561ms/step Eval begin... step 2/2 - loss: 0.0017 - 454ms/step Eval samples: 428 Epoch 10/60 step 7/7 - loss: 0.0018 - 563ms/step Eval begin... step 2/2 - loss: 0.0014 - 454ms/step Eval samples: 428 Epoch 11/60 step 7/7 - loss: 0.0018 - 560ms/step Eval begin... step 2/2 - loss: 0.0011 - 454ms/step Eval samples: 428 Epoch 12/60 step 7/7 - loss: 0.0016 - 554ms/step Eval begin... step 2/2 - loss: 0.0010 - 456ms/step Eval samples: 428 Epoch 13/60 step 7/7 - loss: 0.0018 - 559ms/step Eval begin... step 2/2 - loss: 9.8745e-04 - 453ms/step Eval samples: 428 Epoch 14/60 step 7/7 - loss: 0.0019 - 616ms/step Eval begin... step 2/2 - loss: 9.8130e-04 - 478ms/step Eval samples: 428 Epoch 15/60 step 7/7 - loss: 0.0015 - 553ms/step Eval begin... step 2/2 - loss: 9.7889e-04 - 454ms/step Eval samples: 428 Epoch 16/60 step 7/7 - loss: 0.0017 - 567ms/step Eval begin... step 2/2 - loss: 9.5601e-04 - 463ms/step Eval samples: 428 Epoch 17/60 step 7/7 - loss: 0.0017 - 565ms/step Eval begin... step 2/2 - loss: 9.4202e-04 - 462ms/step Eval samples: 428 Epoch 18/60 step 7/7 - loss: 0.0017 - 559ms/step Eval begin... step 2/2 - loss: 0.0010 - 457ms/step Eval samples: 428 Epoch 19/60 step 7/7 - loss: 0.0017 - 567ms/step Eval begin... step 2/2 - loss: 9.7536e-04 - 465ms/step Eval samples: 428 Epoch 20/60 step 7/7 - loss: 0.0015 - 565ms/step Eval begin... step 2/2 - loss: 9.0152e-04 - 468ms/step Eval samples: 428 Epoch 21/60 step 7/7 - loss: 0.0014 - 570ms/step Eval begin... step 2/2 - loss: 9.0222e-04 - 462ms/step Eval samples: 428 Epoch 22/60 step 7/7 - loss: 0.0015 - 568ms/step Eval begin... step 2/2 - loss: 7.8668e-04 - 453ms/step Eval samples: 428 Epoch 23/60 step 7/7 - loss: 0.0013 - 557ms/step Eval begin... step 2/2 - loss: 7.7608e-04 - 456ms/step Eval samples: 428 Epoch 24/60 step 7/7 - loss: 0.0013 - 555ms/step Eval begin... step 2/2 - loss: 7.8894e-04 - 458ms/step Eval samples: 428 Epoch 25/60 step 7/7 - loss: 0.0013 - 566ms/step Eval begin... step 2/2 - loss: 7.6504e-04 - 456ms/step Eval samples: 428 Epoch 26/60 step 7/7 - loss: 0.0012 - 578ms/step Eval begin... step 2/2 - loss: 7.1451e-04 - 452ms/step Eval samples: 428 Epoch 27/60 step 7/7 - loss: 0.0012 - 572ms/step Eval begin... step 2/2 - loss: 7.3616e-04 - 749ms/step Eval samples: 428 Epoch 28/60 step 7/7 - loss: 0.0013 - 553ms/step Eval begin... step 2/2 - loss: 7.3413e-04 - 457ms/step Eval samples: 428 Epoch 29/60 step 7/7 - loss: 0.0011 - 560ms/step Eval begin... step 2/2 - loss: 7.0764e-04 - 457ms/step Eval samples: 428 Epoch 30/60 step 7/7 - loss: 0.0011 - 560ms/step Eval begin... step 2/2 - loss: 7.2735e-04 - 457ms/step Eval samples: 428 Epoch 31/60 step 7/7 - loss: 0.0010 - 567ms/step Eval begin... step 2/2 - loss: 7.9266e-04 - 453ms/step Eval samples: 428 Epoch 32/60 step 7/7 - loss: 0.0012 - 561ms/step Eval begin... step 2/2 - loss: 6.9983e-04 - 458ms/step Eval samples: 428 Epoch 33/60 step 7/7 - loss: 0.0010 - 568ms/step Eval begin... step 2/2 - loss: 6.7500e-04 - 457ms/step Eval samples: 428 Epoch 34/60 step 7/7 - loss: 0.0011 - 568ms/step Eval begin... step 2/2 - loss: 6.6316e-04 - 459ms/step Eval samples: 428 Epoch 35/60 step 7/7 - loss: 0.0011 - 561ms/step Eval begin... step 2/2 - loss: 6.3884e-04 - 467ms/step Eval samples: 428 Epoch 36/60 step 7/7 - loss: 9.9395e-04 - 559ms/step Eval begin... step 2/2 - loss: 6.0092e-04 - 456ms/step Eval samples: 428 Epoch 37/60 step 7/7 - loss: 0.0011 - 550ms/step Eval begin... step 2/2 - loss: 5.8750e-04 - 460ms/step Eval samples: 428 Epoch 38/60 step 7/7 - loss: 9.3226e-04 - 565ms/step Eval begin... step 2/2 - loss: 5.3959e-04 - 462ms/step Eval samples: 428 Epoch 39/60 step 7/7 - loss: 9.2422e-04 - 562ms/step Eval begin... step 2/2 - loss: 5.2893e-04 - 454ms/step Eval samples: 428 Epoch 40/60 step 7/7 - loss: 9.9294e-04 - 558ms/step Eval begin... step 2/2 - loss: 5.6088e-04 - 461ms/step Eval samples: 428 Epoch 41/60 step 7/7 - loss: 9.5442e-04 - 551ms/step Eval begin... step 2/2 - loss: 5.2197e-04 - 454ms/step Eval samples: 428 Epoch 42/60 step 7/7 - loss: 9.2407e-04 - 572ms/step Eval begin... step 2/2 - loss: 5.5484e-04 - 473ms/step Eval samples: 428 Epoch 43/60 step 7/7 - loss: 9.3951e-04 - 576ms/step Eval begin... step 2/2 - loss: 5.0847e-04 - 483ms/step Eval samples: 428 Epoch 44/60 step 7/7 - loss: 9.3463e-04 - 566ms/step Eval begin... step 2/2 - loss: 5.2639e-04 - 457ms/step Eval samples: 428 Epoch 45/60 step 7/7 - loss: 9.6260e-04 - 863ms/step Eval begin... step 2/2 - loss: 5.2387e-04 - 459ms/step Eval samples: 428 Epoch 46/60 step 7/7 - loss: 7.9236e-04 - 562ms/step Eval begin... step 2/2 - loss: 5.2232e-04 - 488ms/step Eval samples: 428 Epoch 47/60 step 7/7 - loss: 8.8867e-04 - 561ms/step Eval begin... step 2/2 - loss: 5.7546e-04 - 472ms/step Eval samples: 428 Epoch 48/60 step 7/7 - loss: 9.2187e-04 - 563ms/step Eval begin... step 2/2 - loss: 5.1036e-04 - 467ms/step Eval samples: 428 Epoch 49/60 step 7/7 - loss: 8.4267e-04 - 573ms/step Eval begin... step 2/2 - loss: 5.0897e-04 - 457ms/step Eval samples: 428 Epoch 50/60 step 7/7 - loss: 8.1840e-04 - 559ms/step Eval begin... step 2/2 - loss: 4.8478e-04 - 460ms/step Eval samples: 428 Epoch 51/60 step 7/7 - loss: 7.9169e-04 - 564ms/step Eval begin... step 2/2 - loss: 4.9722e-04 - 457ms/step Eval samples: 428 Epoch 52/60 step 7/7 - loss: 7.4572e-04 - 561ms/step Eval begin... step 2/2 - loss: 4.6681e-04 - 459ms/step Eval samples: 428 Epoch 53/60 step 7/7 - loss: 7.7328e-04 - 563ms/step Eval begin... step 2/2 - loss: 4.4126e-04 - 468ms/step Eval samples: 428 Epoch 54/60 step 7/7 - loss: 8.3519e-04 - 565ms/step Eval begin... step 2/2 - loss: 4.5718e-04 - 461ms/step Eval samples: 428 Epoch 55/60 step 7/7 - loss: 7.3492e-04 - 586ms/step Eval begin... step 2/2 - loss: 4.7409e-04 - 461ms/step Eval samples: 428 Epoch 56/60 step 7/7 - loss: 7.7133e-04 - 561ms/step Eval begin... step 2/2 - loss: 4.4491e-04 - 462ms/step Eval samples: 428 Epoch 57/60 step 7/7 - loss: 7.5041e-04 - 561ms/step Eval begin... step 2/2 - loss: 4.4271e-04 - 843ms/step Eval samples: 428 Epoch 58/60 step 7/7 - loss: 7.5371e-04 - 848ms/step Eval begin... step 2/2 - loss: 4.9145e-04 - 451ms/step Eval samples: 428 Epoch 59/60 step 7/7 - loss: 7.7562e-04 - 570ms/step Eval begin... step 2/2 - loss: 4.1318e-04 - 456ms/step Eval samples: 428 Epoch 60/60 step 7/7 - loss: 7.3029e-04 - 858ms/step Eval begin... step 2/2 - loss: 4.2197e-04 - 454ms/step Eval samples: 428
为了更好的观察预测结果，我们分别可视化验证集结果与标注点的对比，和在未标注的测试集的预测结果。
红色的关键点为网络预测的结果， 绿色的关键点为标注的groundtrue。
result = model.predict(val_dataset, batch_size=1)
Predict begin... step 428/428 [==============================] - 17ms/step Predict samples: 428
def plot_sample(x, y, axis, gt=[]):
img = x.reshape(96, 96)
axis.imshow(img, cmap='gray')
axis.scatter(y[0::2], y[1::2], marker='x', s=10, color='r') if gt!=[]:
axis.scatter(gt[0::2], gt[1::2], marker='x', s=10, color='lime')
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(
left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)for i in range(16):
axis = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[])
idx = np.random.randint(val_dataset.__len__())
img, gt_label = val_dataset[idx]
gt_label = gt_label*96
label_pred = result[0][idx].reshape(-1)
label_pred = label_pred*96
plot_sample(img[0], label_pred, axis, gt_label)
plt.show()/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:5: DeprecationWarning: elementwise comparison failed; this will raise an error in the future. """
<Figure size 720x504 with 16 Axes>
result = model.predict(test_dataset, batch_size=1)
Predict begin... step 1783/1783 [==============================] - 17ms/step Predict samples: 1783
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(
left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)for i in range(16):
axis = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[])
idx = np.random.randint(test_dataset.__len__())
img, _ = test_dataset[idx]
label_pred = result[0][idx].reshape(-1)
label_pred = label_pred*96
plot_sample(img[0], label_pred, axis)
plt.show()<Figure size 720x504 with 16 Axes>
以上就是Paddle2.0案例： 人脸关键点检测的详细内容，更多请关注php中文网其它相关文章！
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