科大讯飞-人脸关键点检测挑战赛：基础思路 MAE 2.2
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该内容是人脸关键点检测竞赛方案，涉及4个关键点检测。使用5千张带标注训练集和2千张测试集，数据含图像与坐标标注。构建了全连接和CNN两种模型，经数据加载、预处理、训练验证，CNN模型表现更优，40轮训练后验证集MAE约0.061，最后用模型对测试集预测并可视化结果。
赛题介绍
人脸识别是基于人的面部特征信息进行身份识别的一种生物识别技术，金融和安防是目前人脸识别应用最广泛的两个领域。人脸关键点是人脸识别中的关键技术。人脸关键点检测需要识别出人脸的指定位置坐标，例如眉毛、眼睛、鼻子、嘴巴和脸部轮廓等位置坐标等。
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赛事任务
给定人脸图像，找到4个人脸关键点，赛题任务可以视为一个关键点检测问题。
训练集：5千张人脸图像，并且给定了具体的人脸关键点标注。
测试集：约2千张人脸图像，需要选手识别出具体的关键点位置。
数据说明
赛题数据由训练集和测试集组成，train.csv为训练集标注数据，train.npy和test.npy为训练集图片和测试集图片，可以使用numpy.load进行读取。
train.csv的信息为左眼坐标、右眼坐标、鼻子坐标和嘴巴坐标，总共8个点。
left_eye_center_x,left_eye_center_y,right_eye_center_x,right_eye_center_y,nose_tip_x,nose_tip_y,mouth_center_bottom_lip_x,mouth_center_bottom_lip_y66.3423640449,38.5236134831,28.9308404494,35.5777725843,49.256844943800004,68.2759550562,47.783946067399995,85.361582024568.9126037736,31.409116981100002,29.652226415100003,33.0280754717,51.913358490600004,48.408452830200005,50.6988679245,79.574037735868.7089943925,40.371149158899996,27.1308201869,40.9406803738,44.5025226168,69.9884859813,45.9264269159,86.2210093458登录后复制
评审规则
本次竞赛的评价标准回归MAE进行评价，数值越小性能更优，最高分为0。评估代码参考：
from sklearn.metrics import mean_absolute_errory_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]mean_absolute_error(y_true, y_pred)登录后复制
步骤1：数据集解压
In [1]!echo y | unzip -O CP936 /home/aistudio/data/data117050/人脸关键点检测挑战赛_数据集.zip!mv 人脸关键点检测挑战赛_数据集/* ./!echo y | unzip test.npy.zip!echo y | unzip train.npy.zip登录后复制
Archive: /home/aistudio/data/data117050/人脸关键点检测挑战赛_数据集.zip inflating: 人脸关键点检测挑战赛_数据集/sample_submit.csv inflating: 人脸关键点检测挑战赛_数据集/test.npy.zip inflating: 人脸关键点检测挑战赛_数据集/train.csv inflating: 人脸关键点检测挑战赛_数据集/train.npy.zip Archive: test.npy.zipreplace test.npy? [y]es, [n]o, [A]ll, [N]one, [r]ename: inflating: test.npy Archive: train.npy.zipreplace train.npy? [y]es, [n]o, [A]ll, [N]one, [r]ename: inflating: train.npy登录后复制
步骤2：数据集读取
In [2]import pandas as pdimport numpy as np登录后复制train.csv：存储的是八个关键点的坐标。train.npy：训练集图像test.npy：测试集图像In [3]
# 读取标注train_df = pd.read_csv('train.csv')train_df = train_df.fillna(48)train_df.head()登录后复制left_eye_center_x left_eye_center_y right_eye_center_x \0 66.342364 38.523613 28.930840 1 68.912604 31.409117 29.652226 2 68.708994 40.371149 27.130820 3 65.334176 35.471878 29.366461 4 68.634857 29.999486 31.094571 right_eye_center_y nose_tip_x nose_tip_y mouth_center_bottom_lip_x \0 35.577773 49.256845 68.275955 47.783946 1 33.028075 51.913358 48.408453 50.698868 2 40.940680 44.502523 69.988486 45.926427 3 37.767684 50.411373 64.934767 50.028780 4 29.616429 50.247429 51.450857 47.948571 mouth_center_bottom_lip_y 0 85.361582 1 79.574038 2 86.221009 3 74.883241 4 84.394286登录后复制In [4]
# 读取数据集train_img = np.load('train.npy')test_img = np.load('test.npy')train_img = np.transpose(train_img, [2, 0, 1])train_img = train_img.reshape(-1, 1, 96, 96)test_img = np.transpose(test_img, [2, 0, 1])test_img = test_img.reshape(-1, 1, 96, 96)print(train_img.shape, test_img.shape)登录后复制(5000, 1, 96, 96) (2049, 1, 96, 96)登录后复制
步骤3： 数据集可视化
In [5]%pylab inlineidx = 409xy = train_df.iloc[idx].values.reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(train_img[idx, 0, :, :], cmap='gray')登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized登录后复制
Populating the interactive namespace from numpy and matplotlib登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working if isinstance(obj, collections.Iterator):/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return list(data) if isinstance(data, collections.MappingView) else data登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))登录后复制登录后复制登录后复制登录后复制登录后复制
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idx = 4090xy = train_df.iloc[idx].values.reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(train_img[idx, 0, :, :], cmap='gray')登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))登录后复制登录后复制登录后复制登录后复制登录后复制
登录后复制登录后复制登录后复制登录后复制登录后复制登录后复制登录后复制In [7]
xy = 96 - train_df.mean(0).values.reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')登录后复制
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步骤4：构建模型和数据集
In [8]import paddlepaddle.__version__登录后复制
'2.2.2'登录后复制
全连接模型
In [9]from paddle.io import DataLoader, Datasetfrom PIL import Image# 自定义模型class MyDataset(Dataset): def __init__(self, img, keypoint): super(MyDataset, self).__init__() self.img = img self.keypoint = keypoint def __getitem__(self, index): img = Image.fromarray(self.img[index, 0, :, :]) return np.asarray(img).astype(np.float32)/255, self.keypoint[index] / 96.0 def __len__(self): return len(self.keypoint)# 训练集train_dataset = MyDataset( train_img[:-500, :, :, :], paddle.to_tensor(train_df.values[:-500].astype(np.float32)))train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)# 验证集val_dataset = MyDataset( train_img[-500:, :, :, :], paddle.to_tensor(train_df.values[-500:].astype(np.float32)))val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)# 测试集test_dataset = MyDataset( test_img[:, :, :], paddle.to_tensor(np.zeros((test_img.shape[2], 8))))test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)登录后复制In [10]
# 定义全连接模型model = paddle.nn.Sequential( paddle.nn.Flatten(), paddle.nn.Linear(96*96,128), paddle.nn.LeakyReLU(), paddle.nn.Linear(128, 8))paddle.summary(model, (64, 96, 96))登录后复制
W0123 00:43:41.304462 119 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1W0123 00:43:41.309953 119 device_context.cc:465] device: 0, cuDNN Version: 7.6.登录后复制
--------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =========================================================================== Flatten-1 [[64, 96, 96]] [64, 9216] 0 Linear-1 [[64, 9216]] [64, 128] 1,179,776 LeakyReLU-1 [[64, 128]] [64, 128] 0 Linear-2 [[64, 128]] [64, 8] 1,032 ===========================================================================Total params: 1,180,808Trainable params: 1,180,808Non-trainable params: 0---------------------------------------------------------------------------Input size (MB): 2.25Forward/backward pass size (MB): 4.63Params size (MB): 4.50Estimated Total Size (MB): 11.38---------------------------------------------------------------------------登录后复制
{'total_params': 1180808, 'trainable_params': 1180808}登录后复制In [11]# 损失函数和优化器optimizer = paddle.optimizer.Adam(parameters=model.parameters(), learning_rate=0.0001)criterion = paddle.nn.MSELoss()from sklearn.metrics import mean_absolute_errorfor epoch in range(0, 40): Train_Loss, Val_Loss = [], [] Train_MAE, Val_MAE = [], [] # 训练 model.train() for i, (x, y) in enumerate(train_loader): pred = model(x) loss = criterion(pred, y) Train_Loss.append(loss.item()) loss.backward() optimizer.step() optimizer.clear_grad() Train_MAE.append(mean_absolute_error(y.numpy(), pred.numpy()) * 96 / y.shape[0]) # 验证 model.eval() for i, (x, y) in enumerate(val_loader): pred = model(x) loss = criterion(pred, y) Val_Loss.append(loss.item()) Val_MAE.append(mean_absolute_error(y.numpy(), pred.numpy()) * 96 / y.shape[0]) if epoch % 1 == 0: print(f'\nEpoch: {epoch}') print(f'Loss {np.mean(Train_Loss):3.5f}/{np.mean(Val_Loss):3.5f}') print(f'MAE {np.mean(Train_MAE):3.5f}/{np.mean(Val_MAE):3.5f}')登录后复制Epoch: 0Loss 0.05956/0.02340MAE 0.25278/0.18601Epoch: 1Loss 0.02075/0.02269MAE 0.17376/0.17984Epoch: 2Loss 0.01832/0.01881MAE 0.16236/0.16371Epoch: 3Loss 0.01752/0.01729MAE 0.15944/0.15727Epoch: 4Loss 0.01630/0.01783MAE 0.15351/0.16075Epoch: 5Loss 0.01535/0.01593MAE 0.14883/0.15059Epoch: 6Loss 0.01489/0.01655MAE 0.14582/0.15519Epoch: 7Loss 0.01469/0.01596MAE 0.14487/0.14971Epoch: 8Loss 0.01362/0.01582MAE 0.13930/0.15087Epoch: 9Loss 0.01355/0.01506MAE 0.13915/0.14637Epoch: 10Loss 0.01293/0.01490MAE 0.13586/0.14514Epoch: 11Loss 0.01289/0.01367MAE 0.13555/0.13847Epoch: 12Loss 0.01187/0.01372MAE 0.12944/0.13950Epoch: 13Loss 0.01184/0.01281MAE 0.12905/0.13358Epoch: 14Loss 0.01181/0.01534MAE 0.12995/0.14891Epoch: 15Loss 0.01124/0.01334MAE 0.12593/0.13727Epoch: 16Loss 0.01083/0.01371MAE 0.12342/0.14003Epoch: 17Loss 0.01057/0.01181MAE 0.12188/0.12769Epoch: 18Loss 0.01041/0.01207MAE 0.12105/0.12884Epoch: 19Loss 0.01017/0.01149MAE 0.11868/0.12613Epoch: 20Loss 0.00965/0.01348MAE 0.11610/0.13499Epoch: 21Loss 0.00993/0.01133MAE 0.11817/0.12543Epoch: 22Loss 0.00906/0.01080MAE 0.11226/0.12200Epoch: 23Loss 0.00883/0.01117MAE 0.11127/0.12394Epoch: 24Loss 0.00865/0.01064MAE 0.10986/0.12086Epoch: 25Loss 0.00924/0.01023MAE 0.11396/0.11844Epoch: 26Loss 0.00850/0.01001MAE 0.10874/0.11812Epoch: 27Loss 0.00801/0.00998MAE 0.10525/0.11665Epoch: 28Loss 0.00809/0.00978MAE 0.10666/0.11558Epoch: 29Loss 0.00743/0.01073MAE 0.10161/0.12184Epoch: 30Loss 0.00752/0.00916MAE 0.10146/0.11186Epoch: 31Loss 0.00715/0.00982MAE 0.09895/0.11673Epoch: 32Loss 0.00717/0.00907MAE 0.09980/0.11068Epoch: 33Loss 0.00718/0.00967MAE 0.09976/0.11560Epoch: 34Loss 0.00677/0.01463MAE 0.09663/0.14721Epoch: 35Loss 0.00764/0.00852MAE 0.10249/0.10766Epoch: 36Loss 0.00650/0.00916MAE 0.09434/0.11061Epoch: 37Loss 0.00644/0.00840MAE 0.09397/0.10676Epoch: 38Loss 0.00642/0.00852MAE 0.09410/0.10684Epoch: 39Loss 0.00611/0.00798MAE 0.09161/0.10284登录后复制In [13]
# 预测函数def make_predict(model, loader): model.eval() predict_list = [] for i, (x, y) in enumerate(loader): pred = model(x) predict_list.append(pred.numpy()) return np.vstack(predict_list)test_pred = make_predict(model, test_loader) * 96登录后复制In [14]
idx = 40xy = test_pred[idx, :].reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(test_img[idx, 0, :, :], cmap='gray')登录后复制登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))登录后复制登录后复制登录后复制登录后复制登录后复制
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idx = 42xy = test_pred[idx, :].reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(test_img[idx, 0, :, :], cmap='gray')登录后复制登录后复制
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CNN模型
In [17]from paddle.io import DataLoader, Datasetfrom PIL import Imageclass MyDataset(Dataset): def __init__(self, img, keypoint): super(MyDataset, self).__init__() self.img = img self.keypoint = keypoint def __getitem__(self, index): img = Image.fromarray(self.img[index, 0, :, :]) return np.asarray(img).reshape(1, 96, 96).astype(np.float32)/255, self.keypoint[index] / 96.0 def __len__(self): return len(self.keypoint)train_dataset = MyDataset( train_img[:-500, :, :, :], paddle.to_tensor(train_df.values[:-500].astype(np.float32)))train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)val_dataset = MyDataset( train_img[-500:, :, :, :], paddle.to_tensor(train_df.values[-500:].astype(np.float32)))val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)test_dataset = MyDataset( test_img[:, :, :], paddle.to_tensor(np.zeros((test_img.shape[2], 8))))test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)登录后复制In [18]
# 卷积模型model = paddle.nn.Sequential( paddle.nn.Conv2D(1, 10, (5, 5)), paddle.nn.ReLU(), paddle.nn.MaxPool2D((2, 2)), paddle.nn.Conv2D(10, 20, (5, 5)), paddle.nn.ReLU(), paddle.nn.MaxPool2D((2, 2)), paddle.nn.Conv2D(20, 40, (5, 5)), paddle.nn.ReLU(), paddle.nn.MaxPool2D((2, 2)), paddle.nn.Flatten(), paddle.nn.Linear(2560, 8),)paddle.summary(model, (64, 1, 96, 96))登录后复制
--------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =========================================================================== Conv2D-4 [[64, 1, 96, 96]] [64, 10, 92, 92] 260 ReLU-4 [[64, 10, 92, 92]] [64, 10, 92, 92] 0 MaxPool2D-4 [[64, 10, 92, 92]] [64, 10, 46, 46] 0 Conv2D-5 [[64, 10, 46, 46]] [64, 20, 42, 42] 5,020 ReLU-5 [[64, 20, 42, 42]] [64, 20, 42, 42] 0 MaxPool2D-5 [[64, 20, 42, 42]] [64, 20, 21, 21] 0 Conv2D-6 [[64, 20, 21, 21]] [64, 40, 17, 17] 20,040 ReLU-6 [[64, 40, 17, 17]] [64, 40, 17, 17] 0 MaxPool2D-6 [[64, 40, 17, 17]] [64, 40, 8, 8] 0 Flatten-3 [[64, 40, 8, 8]] [64, 2560] 0 Linear-4 [[64, 2560]] [64, 8] 20,488 ===========================================================================Total params: 45,808Trainable params: 45,808Non-trainable params: 0---------------------------------------------------------------------------Input size (MB): 2.25Forward/backward pass size (MB): 145.54Params size (MB): 0.17Estimated Total Size (MB): 147.97---------------------------------------------------------------------------登录后复制
{'total_params': 45808, 'trainable_params': 45808}登录后复制In [19]# 损失函数和优化器optimizer = paddle.optimizer.Adam(parameters=model.parameters(), learning_rate=0.0001)criterion = paddle.nn.MSELoss()from sklearn.metrics import mean_absolute_errorfor epoch in range(0, 40): Train_Loss, Val_Loss = [], [] Train_MAE, Val_MAE = [], [] # 训练 model.train() for i, (x, y) in enumerate(train_loader): pred = model(x) loss = criterion(pred, y) Train_Loss.append(loss.item()) loss.backward() optimizer.step() optimizer.clear_grad() Train_MAE.append(mean_absolute_error(y.numpy(), pred.numpy()) * 96 / y.shape[0]) # 验证 model.eval() for i, (x, y) in enumerate(val_loader): pred = model(x) loss = criterion(pred, y) Val_Loss.append(loss.item()) Val_MAE.append(mean_absolute_error(y.numpy(), pred.numpy()) * 96 / y.shape[0]) if epoch % 1 == 0: print(f'\nEpoch: {epoch}') print(f'Loss {np.mean(Train_Loss):3.5f}/{np.mean(Val_Loss):3.5f}') print(f'MAE {np.mean(Train_MAE):3.5f}/{np.mean(Val_MAE):3.5f}')登录后复制Epoch: 0Loss 0.23343/0.03865MAE 0.44735/0.23946Epoch: 1Loss 0.03499/0.03301MAE 0.22689/0.22072Epoch: 2Loss 0.03006/0.02846MAE 0.20913/0.20492Epoch: 3Loss 0.02614/0.02548MAE 0.19541/0.19341Epoch: 4Loss 0.02270/0.02314MAE 0.18112/0.18211Epoch: 5Loss 0.01965/0.01952MAE 0.16927/0.16763Epoch: 6Loss 0.01704/0.01763MAE 0.15715/0.15866Epoch: 7Loss 0.01492/0.01483MAE 0.14711/0.14516Epoch: 8Loss 0.01260/0.01268MAE 0.13498/0.13350Epoch: 9Loss 0.01034/0.00996MAE 0.12187/0.11828Epoch: 10Loss 0.00855/0.00836MAE 0.11041/0.10738Epoch: 11Loss 0.00751/0.00737MAE 0.10320/0.10133Epoch: 12Loss 0.00644/0.00657MAE 0.09478/0.09471Epoch: 13Loss 0.00592/0.00626MAE 0.09048/0.09321Epoch: 14Loss 0.00556/0.00568MAE 0.08704/0.08790Epoch: 15Loss 0.00518/0.00538MAE 0.08444/0.08551Epoch: 16Loss 0.00491/0.00524MAE 0.08204/0.08433Epoch: 17Loss 0.00474/0.00495MAE 0.08087/0.08178Epoch: 18Loss 0.00450/0.00476MAE 0.07885/0.08041Epoch: 19Loss 0.00431/0.00460MAE 0.07685/0.07922Epoch: 20Loss 0.00421/0.00458MAE 0.07596/0.07887Epoch: 21Loss 0.00393/0.00421MAE 0.07302/0.07515Epoch: 22Loss 0.00387/0.00419MAE 0.07282/0.07502Epoch: 23Loss 0.00373/0.00416MAE 0.07131/0.07482Epoch: 24Loss 0.00354/0.00385MAE 0.06945/0.07177Epoch: 25Loss 0.00347/0.00386MAE 0.06882/0.07173Epoch: 26Loss 0.00340/0.00368MAE 0.06781/0.06999Epoch: 27Loss 0.00323/0.00363MAE 0.06601/0.06949Epoch: 28Loss 0.00320/0.00349MAE 0.06580/0.06794Epoch: 29Loss 0.00307/0.00349MAE 0.06427/0.06842Epoch: 30Loss 0.00300/0.00336MAE 0.06357/0.06692Epoch: 31Loss 0.00291/0.00329MAE 0.06240/0.06611Epoch: 32Loss 0.00287/0.00326MAE 0.06206/0.06594Epoch: 33Loss 0.00280/0.00323MAE 0.06119/0.06572Epoch: 34Loss 0.00276/0.00312MAE 0.06076/0.06427Epoch: 35Loss 0.00268/0.00304MAE 0.05994/0.06345Epoch: 36Loss 0.00262/0.00301MAE 0.05915/0.06306Epoch: 37Loss 0.00256/0.00294MAE 0.05834/0.06231Epoch: 38Loss 0.00256/0.00288MAE 0.05833/0.06166Epoch: 39Loss 0.00246/0.00284MAE 0.05717/0.06128登录后复制In [20]
def make_predict(model, loader): model.eval() predict_list = [] for i, (x, y) in enumerate(loader): pred = model(x) predict_list.append(pred.numpy()) return np.vstack(predict_list)test_pred = make_predict(model, test_loader) * 96登录后复制In [21]
idx = 40xy = test_pred[idx, :].reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(test_img[idx, 0, :, :], cmap='gray')登录后复制登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))登录后复制登录后复制登录后复制登录后复制登录后复制
登录后复制登录后复制登录后复制登录后复制登录后复制登录后复制登录后复制In [22]
idx = 42xy = test_pred[idx, :].reshape(-1, 2)plt.scatter(xy[:, 0], xy[:, 1], c='r')plt.imshow(test_img[idx, 0, :, :], cmap='gray')登录后复制登录后复制
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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))登录后复制登录后复制登录后复制登录后复制登录后复制
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