本案例參考課程：百度架構師手把手教深度學習的內容。主要目的為練習vgg動態圖的PaddlePaddle實現。

本案例已經在AISTUDIO共享，連結為：

資料集iChallenge-PM：

資料集圖片 iChallenge-PM中既有病理性近視患者的眼底圖片，也有非病理性近視患者的圖片，命名規則如下：

病理性近視（PM）：檔名以P開頭

非病理性近視（non-PM）：

高度近似（high myopia）：檔名以H開頭

正常眼睛（normal）：檔名以N開頭

我們將病理性患者的圖片作為正樣本，標籤為1；非病理性患者的圖片作為負樣本，標籤為0。從資料集中選取兩張圖片，透過LeNet提取特徵，構建分類器，對正負樣本進行分類，並將圖片顯示出來。

演算法：

VGG VGG是當前最流行的CNN模型之一，2014年由Simonyan和Zisserman提出，其命名來源於論文作者所在的實驗室Visual Geometry Group。AlexNet模型透過構造多層網路，取得了較好的效果，但是並沒有給出深度神經網路設計的方向。VGG透過使用一系列大小為3x3的小尺寸卷積核和pooling層構造深度卷積神經網路，並取得了較好的效果。VGG模型因為結構簡單、應用性極強而廣受研究者歡迎，尤其是它的網路結構設計方法，為構建深度神經網路提供了方向。

圖3 是VGG-16的網路結構示意圖，有13層卷積和3層全連線層。VGG網路的設計嚴格使用3×33\times 33×3的卷積層和池化層來提取特徵，並在網路的最後面使用三層全連線層，將最後一層全連線層的輸出作為分類的預測。在VGG中每層卷積將使用ReLU作為啟用函式，在全連線層之後新增dropout來抑制過擬合。使用小的卷積核能夠有效地減少引數的個數，使得訓練和測試變得更加有效。比如使用兩層3×33\times 33×3卷積層，可以得到感受野為5的特徵圖，而比使用5×55 \times 55×5的卷積層需要更少的引數。由於卷積核比較小，可以堆疊更多的卷積層，加深網路的深度，這對於影像分類任務來說是有利的。VGG模型的成功證明了增加網路的深度，可以更好的學習影像中的特徵模式。

關鍵程式碼：

import os

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

from PIL import Image

DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400'

# 檔名以N開頭的是正常眼底圖片，以P開頭的是病變眼底圖片

file1 = 'N0012.jpg'

file2 = 'P0095.jpg'

# 讀取圖片

img1 = Image.open(os.path.join(DATADIR, file1))

img1 = np.array(img1)

img2 = Image.open(os.path.join(DATADIR, file2))

img2 = np.array(img2)

# 畫出讀取的圖片

plt.figure(figsize=(16, 8))

f = plt.subplot(121)

f.set_title('Normal', fontsize=20)

plt.imshow(img1)

f = plt.subplot(122)

f.set_title('PM', fontsize=20)

plt.imshow(img2)

plt.show()

In[3]

# 檢視圖片形狀

img1.shape, img2.shape

((2056, 2124, 3), (2056, 2124, 3))

In[5]

#定義資料讀取器

import cv2

import random

import numpy as np

# 對讀入的影像資料進行預處理

def transform_img(img):

# 將圖片尺寸縮放道 224x224

img = cv2.resize(img, (224, 224))

# 讀入的影像資料格式是[H, W, C]

# 使用轉置操作將其變成[C, H, W]

img = np.transpose(img, (2,0,1))

img = img.astype('float32')

# 將資料範圍調整到[-1.0, 1.0]之間

img = img / 255.

img = img * 2.0 - 1.0

return img

# 定義訓練集資料讀取器

def data_loader(datadir, batch_size=10, mode = 'train'):

# 將datadir目錄下的檔案列出來，每條檔案都要讀入

filenames = os.listdir(datadir)

def reader():

if mode == 'train':

# 訓練時隨機打亂資料順序

random.shuffle(filenames)

batch_imgs = []

batch_labels = []

for name in filenames:

filepath = os.path.join(datadir, name)

img = cv2.imread(filepath)

img = transform_img(img)

if name[0] == 'H' or name[0] == 'N':

# H開頭的檔名錶示高度近似，N開頭的檔名錶示正常視力

# 高度近視和正常視力的樣本，都不是病理性的，屬於負樣本，標籤為0

label = 0

elif name[0] == 'P':

# P開頭的是病理性近視，屬於正樣本，標籤為1

label = 1

else:

raise('Not excepted file name')

# 每讀取一個樣本的資料，就將其放入資料列表中

batch_imgs.append(img)

batch_labels.append(label)

if len(batch_imgs) == batch_size:

# 當資料列表的長度等於batch_size的時候，

# 把這些資料當作一個mini-batch，並作為資料生成器的一個輸出

imgs_array = np.array(batch_imgs).astype('float32')

labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)

yield imgs_array, labels_array

batch_imgs = []

batch_labels = []

if len(batch_imgs) > 0:

# 剩餘樣本數目不足一個batch_size的資料，一起打包成一個mini-batch

imgs_array = np.array(batch_imgs).astype('float32')

labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)

yield imgs_array, labels_array

return reader

# 檢視資料形狀

DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400'

train_loader = data_loader(DATADIR,

batch_size=10, mode='train')

data_reader = train_loader()

data = next(data_reader)

data[0].shape, data[1].shape

((10, 3, 224, 224), (10, 1))

In[6]

!pip install xlrd

import pandas as pd

df=pd.read_excel('/home/aistudio/work/palm/PALM-Validation-GT/PM_Label_and_Fovea_Location.xlsx')

df.to_csv('/home/aistudio/work/palm/PALM-Validation-GT/labels.csv',index=False)

#訓練和評估程式碼

import os

import random

import paddle

import paddle.fluid as fluid

import numpy as np

DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400'

DATADIR2 = '/home/aistudio/work/palm/PALM-Validation400'

CSVFILE = '/home/aistudio/work/palm/PALM-Validation-GT/labels.csv'

# 定義訓練過程

def train(model):

with fluid.dygraph.guard():

print('start training ... ')

model.train()

epoch_num = 5

# 定義最佳化器

opt = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)

# 定義資料讀取器，訓練資料讀取器和驗證資料讀取器

train_loader = data_loader(DATADIR, batch_size=10, mode='train')

valid_loader = valid_data_loader(DATADIR2, CSVFILE)

for epoch in range(epoch_num):

for batch_id, data in enumerate(train_loader()):

x_data, y_data = data

img = fluid.dygraph.to_variable(x_data)

label = fluid.dygraph.to_variable(y_data)

# 執行模型前向計算，得到預測值

logits = model(img)

# 進行loss計算

loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label)

avg_loss = fluid.layers.mean(loss)

if batch_id % 10 == 0:

print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))

# 反向傳播，更新權重，清除梯度

avg_loss.backward()

opt.minimize(avg_loss)

model.clear_gradients()

model.eval()

accuracies = []

losses = []

for batch_id, data in enumerate(valid_loader()):

x_data, y_data = data

img = fluid.dygraph.to_variable(x_data)

label = fluid.dygraph.to_variable(y_data)

# 執行模型前向計算，得到預測值

logits = model(img)

# 二分類，sigmoid計算後的結果以0.5為閾值分兩個類別

# 計算sigmoid後的預測機率，進行loss計算

pred = fluid.layers.sigmoid(logits)

loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label)

# 計算預測機率小於0.5的類別

pred2 = pred * (-1.0) + 1.0

# 得到兩個類別的預測機率，並沿第一個維度級聯

pred = fluid.layers.concat([pred2, pred], axis=1)

acc = fluid.layers.accuracy(pred, fluid.layers.cast(label, dtype='int64'))

accuracies.append(acc.numpy())

losses.append(loss.numpy())

print("[validation] accuracy/loss: {}/{}".format(np.mean(accuracies), np.mean(losses)))

model.train()

# save params of model

fluid.save_dygraph(model.state_dict(), 'mnist')

# save optimizer state

fluid.save_dygraph(opt.state_dict(), 'mnist')

# 定義評估過程

def evaluation(model, params_file_path):

with fluid.dygraph.guard():

print('start evaluation .......')

#載入模型引數

model_state_dict, _ = fluid.load_dygraph(params_file_path)

model.load_dict(model_state_dict)

model.eval()

eval_loader = load_data('eval')

acc_set = []

avg_loss_set = []

for batch_id, data in enumerate(eval_loader()):

x_data, y_data = data

img = fluid.dygraph.to_variable(x_data)

label = fluid.dygraph.to_variable(y_data)

# 計算預測和精度

prediction, acc = model(img, label)

# 計算損失函式值

loss = fluid.layers.cross_entropy(input=prediction, label=label)

avg_loss = fluid.layers.mean(loss)

acc_set.append(float(acc.numpy()))

avg_loss_set.append(float(avg_loss.numpy()))

# 求平均精度

acc_val_mean = np.array(acc_set).mean()

avg_loss_val_mean = np.array(avg_loss_set).mean()

print('loss={}, acc={}'.format(avg_loss_val_mean, acc_val_mean))

In[8]

# -*- coding:utf-8 -*-

# VGG模型程式碼

import numpy as np

import paddle

import paddle.fluid as fluid

from paddle.fluid.layer_helper import LayerHelper

from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC

from paddle.fluid.dygraph.base import to_variable

# 定義vgg塊，包含多層卷積和1層2x2的最大池化層

class vgg_block(fluid.dygraph.Layer):

def __init__(self, name_scope, num_convs, num_channels):

"""

num_convs, 卷積層的數目

num_channels, 卷積層的輸出通道數，在同一個Incepition塊內，卷積層輸出通道數是一樣的

"""

super(vgg_block, self).__init__(name_scope)

self.conv_list = []

for i in range(num_convs):

conv_layer = self.add_sublayer('conv_' + str(i), Conv2D(self.full_name(),

num_filters=num_channels, filter_size=3, padding=1, act='relu'))

self.conv_list.append(conv_layer)

self.pool = Pool2D(self.full_name(), pool_stride=2, pool_size = 2, pool_type='max')

def forward(self, x):

for item in self.conv_list:

x = item(x)

return self.pool(x)

class VGG(fluid.dygraph.Layer):

def __init__(self, name_scope, conv_arch=((2, 64),

(2, 128), (3, 256), (3, 512), (3, 512))):

super(VGG, self).__init__(name_scope)

self.vgg_blocks=[]

iter_id = 0

# 新增vgg_block

# 這裡一共5個vgg_block，每個block裡面的卷積層數目和輸出通道數由conv_arch指定

for (num_convs, num_channels) in conv_arch:

block = self.add_sublayer('block_' + str(iter_id),

vgg_block(self.full_name(), num_convs, num_channels))

self.vgg_blocks.append(block)

iter_id += 1

self.fc1 = FC(self.full_name(),

size=4096,

act='relu')

self.drop1_ratio = 0.5

self.fc2= FC(self.full_name(),

size=4096,

act='relu')

self.drop2_ratio = 0.5

self.fc3 = FC(self.full_name(),

size=1,

)

def forward(self, x):

for item in self.vgg_blocks:

x = item(x)

x = fluid.layers.dropout(self.fc1(x), self.drop1_ratio)

x = fluid.layers.dropout(self.fc2(x), self.drop2_ratio)

x = self.fc3(x)

return x

with fluid.dygraph.guard():

model = VGG("VGG")

train(model)

start training ...

epoch: 0, batch_id: 0, loss is: [0.7242754]

epoch: 0, batch_id: 10, loss is: [0.6634571]

epoch: 0, batch_id: 20, loss is: [0.7898234]

epoch: 0, batch_id: 30, loss is: [0.60537547]

[validation] accuracy/loss: 0.9424999952316284/0.35623037815093994

epoch: 1, batch_id: 0, loss is: [0.31599292]

epoch: 1, batch_id: 10, loss is: [0.1198744]

epoch: 1, batch_id: 20, loss is: [0.46862125]

epoch: 1, batch_id: 30, loss is: [0.2300901]

[validation] accuracy/loss: 0.92249995470047/0.2342415601015091

epoch: 2, batch_id: 0, loss is: [0.22039299]

epoch: 2, batch_id: 10, loss is: [0.65977865]

epoch: 2, batch_id: 20, loss is: [0.37409317]

epoch: 2, batch_id: 30, loss is: [0.1841044]

[validation] accuracy/loss: 0.9325000643730164/0.22097690403461456

epoch: 3, batch_id: 0, loss is: [0.4992897]

epoch: 3, batch_id: 10, loss is: [0.31177607]

epoch: 3, batch_id: 20, loss is: [0.1721839]

epoch: 3, batch_id: 30, loss is: [0.38319916]

[validation] accuracy/loss: 0.9199999570846558/0.20679759979248047

epoch: 4, batch_id: 0, loss is: [0.20610766]

epoch: 4, batch_id: 10, loss is: [0.06688808]

epoch: 4, batch_id: 20, loss is: [0.3352648]

epoch: 4, batch_id: 30, loss is: [0.28062168]

[validation] accuracy/loss: 0.9149999618530273/0.21788272261619568

with fluid.dygraph.guard():

model = VGG("VGG")

train(model)

PaddlePaddle動態圖實現VGG（眼底篩查為例）

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