keras实现GoogLeNet-InceptionV1
GoogLeNet Inception v1 结构 及 pytorch、tensorflow、keras、paddle实现ImageNet识别
环境
python3.6, keras2.2.4, tensorflow-gpu 1.12.0
代码
# -*- coding: utf-8 -*-
# @Time : 2020/2/3 9:56
# @Author : Zhao HL
# @File : InceptionV1-keras.py
import keras
from keras.utils import Sequence
from keras.layers import *
from keras.models import *
from keras.optimizers import *
from keras.callbacks import *
import numpy as np
import pandas as pd
from PIL import Image
from my_utils import draw_loss_acc, dataInfo_show, dataset_divide
# region parameters
# region paths
Data_path = "./data/"
Data_csv_path = "./data/split.txt"
Model_path = 'model/'
Model_file_tf = "model/InceptionV1_tf.ckpt"
Model_file_keras = "model/InceptionV1_keras.h5"
Model_file_torch = "model/InceptionV1_torch.pth"
Model_file_paddle = "model/InceptionV1_paddle.model"
# endregion
# region image parameter
Img_size = 224
Img_chs = 3
Label_size = 1
Label_class = ['agricultural',
'airplane',
'baseballdiamond',
'beach',
'buildings',
'chaparral',
'denseresidential',
'forest',
'freeway',
'golfcourse',
'harbor',
'intersection',
'mediumresidential',
'mobilehomepark',
'overpass',
'parkinglot',
'river',
'runway',
'sparseresidential',
'storagetanks',
'tenniscourt']
Labels_nums = len(Label_class)
# endregion
# region net parameter
Conv1_kernel_size = 7
Conv1_chs = 64
Conv21_kernel_size = 1
Conv21_chs = 64
Conv2_kernel_size = 3
Conv2_chs = 192
Icp3a_size = (64, 96, 128, 16, 32, 32)
Icp3b_size = (128, 128, 192, 32, 96, 64)
Icp4a_size = (192, 96, 208, 16, 48, 64)
Icp4b_size = (160, 112, 224, 24, 64, 64)
Icp4c_size = (128, 128, 256, 24, 64, 64)
Icp4d_size = (112, 144, 288, 32, 64, 64)
Icp4e_size = (256, 160, 320, 32, 128, 128)
Icp5a_size = (256, 160, 320, 32, 128, 128)
Icp5b_size = (384, 192, 384, 48, 128, 128)
Out_chs1 = 128
Out_chs2 = 1024
# endregion
# region hpyerparameter
Learning_rate = 1e-3
Batch_size = 2
Buffer_size = 256
Infer_size = 1
Epochs = 5
Train_num = 1470
Train_batch_num = Train_num // Batch_size
Val_num = 210
Val_batch_num = Val_num // Batch_size
Test_num = 420
Test_batch_num = Test_num // Batch_size
# endregion
# endregion
class MyDataset(Sequence):
def __init__(self, root_path, batch_size, files_list=None,shuffle=True):
self.shuffle = shuffle
self.root_path = root_path
self.batch_size = batch_size
self.files_list = files_list if files_list else os.listdir(root_path)
self.size = len(files_list)
self.list_shuffle()
def __len__(self):
return self.size
def __getitem__(self, batch_index):
images, labels = [], []
if batch_index >= self.size // self.batch_size:
batch_index = batch_index%(self.size // self.batch_size)
start_index = batch_index * self.batch_size
end_index = (batch_index + 1) * self.batch_size
for index in range(start_index, end_index):
label_str = os.path.basename(self.files_list[index])[:-6]
label = Label_class.index(label_str)
img = Image.open(os.path.join(self.root_path, self.files_list[index]))
img, label = self.transform(img, label)
images.append(img)
labels.append(label)
images = np.array(images)
labels = np.array(labels)
return ({'input': images}, {'output': labels,'output1':labels,'output2':labels})
def transform(self, image, label):
def Normalize(image, means, stds):
for band in range(len(means)):
image[:, :, band] = image[:, :, band] / 255.0
image[:, :, band] = (image[:, :, band] - means[band]) / stds[band]
return image
def ToOnehot(labels):
labels = np.eye(Labels_nums)[labels].reshape(Labels_nums)
return labels
pass
image = image.resize((Img_size, Img_size), Image.ANTIALIAS)
image = Normalize(np.array(image).astype(np.float), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
label = ToOnehot(label)
return (image, label)
def list_shuffle(self):
if self.shuffle:
np.random.shuffle(self.files_list)
class InceptionV1:
def __init__(self, structShow=False):
self.structShow = structShow
def InceptionV1_Model(self, input, model_size):
con11_chs, con31_chs, con3_chs, con51_chs, con5_chs, pool1_chs = model_size
conv11 = Conv2D(con11_chs, 1, padding='SAME', activation='relu', kernel_initializer='he_normal')(input)
conv31 = Conv2D(con31_chs, 1, padding='SAME', activation='relu', kernel_initializer='he_normal')(input)
conv3 = Conv2D(con3_chs, 3, padding='SAME', activation='relu', kernel_initializer='he_normal')(conv31)
conv51 = Conv2D(con51_chs, 1, padding='SAME', activation='relu', kernel_initializer='he_normal')(input)
conv5 = Conv2D(con5_chs, 5, padding='SAME', activation='relu', kernel_initializer='he_normal')(conv51)
pool1 = MaxPooling2D(pool_size=3, strides=1, padding='SAME')(input)
conv1 = Conv2D(pool1_chs, 1, padding='SAME', activation='relu', kernel_initializer='he_normal')(pool1)
output = concatenate([conv11, conv3, conv5, conv1], axis=3)
return output
def InceptionV1_Out(self, input, name=None):
pool = AvgPool2D(pool_size=5, strides=3, padding='VALID')(input)
conv = Conv2D(Out_chs1, 1, padding='SAME', activation='relu', kernel_initializer='he_normal')(pool)
flat = Flatten()(conv)
dropout = Dropout(0.3)(flat)
output = Dense(Labels_nums,name=name)(dropout)
return output
def getNet(self):
input = Input(shape=(Img_size, Img_size, Img_chs),name='input')
# region conv pool
conv1 = Conv2D(Conv1_chs, kernel_size=Conv1_kernel_size, padding='SAME', activation='relu', strides=2,
kernel_initializer='he_normal')(input)
pool1 = MaxPooling2D(pool_size=3, strides=2, padding='SAME')(conv1)
conv21 = Conv2D(Conv21_chs, kernel_size=Conv21_kernel_size, padding='SAME', activation='relu',
kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(Conv2_chs, kernel_size=Conv2_kernel_size, padding='SAME', activation='relu',
kernel_initializer='he_normal')(conv21)
pool2 = MaxPooling2D(pool_size=3, strides=2, padding='SAME')(conv2)
# endregion
# region inception3
inception3a = self.InceptionV1_Model(pool2, Icp3a_size)
inception3b = self.InceptionV1_Model(inception3a, Icp3b_size)
pool3 = MaxPooling2D(pool_size=3, strides=2, padding='SAME')(inception3b)
# endregion
# region inception3
inception4a = self.InceptionV1_Model(pool3, Icp4a_size)
output1 = self.InceptionV1_Out(inception4a, 'output1')
inception4b = self.InceptionV1_Model(inception4a, Icp4b_size)
inception4c = self.InceptionV1_Model(inception4b, Icp4c_size)
inception4d = self.InceptionV1_Model(inception4c, Icp4d_size)
output2 = self.InceptionV1_Out(inception4d, 'output2')
inception4e = self.InceptionV1_Model(inception4d, Icp4e_size)
pool4 = MaxPooling2D(pool_size=3, strides=2, padding='SAME')(inception4e)
# endregion
# region inception5
inception5a = self.InceptionV1_Model(pool4, Icp5a_size)
inception5b = self.InceptionV1_Model(inception5a, Icp5b_size)
pool5 = MaxPooling2D(pool_size=7, strides=1, padding='SAME')(inception5b)
# endregion
# region output
flat = Flatten()(pool5)
dropout = Dropout(0.4)(flat)
output = Dense(Labels_nums,name='output')(dropout)
# endregion
model = Model(inputs=input, outputs=[output,output1,output2])
model.compile(Adam(lr=Learning_rate), loss='categorical_crossentropy', metrics=['accuracy'],
loss_weights=[0.6, 0.2, 0.2]
)
if self.structShow:
model.summary()
return model
def train():
df = pd.read_csv(Data_csv_path, header=0, index_col=0)
train_list = df[df['split'] == 'train']['filename'].tolist()
val_list = df[df['split'] == 'val']['filename'].tolist()
train_dataset = MyDataset(Data_path, batch_size=Batch_size, files_list=train_list)
val_dataset = MyDataset(Data_path, batch_size=Batch_size, files_list=val_list)
net = InceptionV1(structShow=True)
model = net.getNet()
# if os.path.exists(Model_file_keras):
# model = load_model(Model_file_keras)
# else:
# model = net.get_alexNet()
model_checkpoint = ModelCheckpoint(Model_file_keras, monitor='val_loss', save_best_only=True)
history = model.fit_generator(train_dataset,
steps_per_epoch=train_dataset.size//train_dataset.batch_size,
epochs=Epochs,
use_multiprocessing=True,
validation_data=val_dataset,
validation_steps=val_dataset.size//val_dataset.batch_size,
shuffle=True,
callbacks=[model_checkpoint]
)
print(history.history.keys())
train_losses = history.history['loss']
train_accs = history.history['output_acc']
train_accs1 = history.history['output1_acc']
train_accs2 = history.history['output2_acc']
val_losses = history.history['val_loss']
val_accs = history.history['val_output_acc']
val_accs1 = history.history['val_output1_acc']
val_accs2 = history.history['val_output2_acc']
draw_loss_acc(train_losses, train_accs, 'train')
draw_loss_acc(train_accs1, train_accs2, 'train')
draw_loss_acc(val_losses, val_accs, 'val')
draw_loss_acc(val_accs1, val_accs2, 'val')
print('best loss %.4f at epoch %d \n' % (max(val_losses), int(np.argmin(np.array(val_losses)))))
if __name__ == '__main__':
pass
# dataset_divide(r'E:\_Python\01_deeplearning\04_GoogLeNet\Inception1\data\split.txt')
train()
my_utils.py
# -*- coding: utf-8 -*-
# @Time : 2020/1/21 11:39
# @Author : Zhao HL
# @File : my_utils.py
import sys,os,random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
def process_show(num, nums, train_acc, train_loss, prefix='', suffix=''):
rate = num / nums
ratenum = int(round(rate, 2) * 100)
bar = '\r%s batch %3d/%d:train accuracy %.4f, train loss %00.4f [%s%s]%.1f%% %s; ' % (
prefix, num, nums, train_acc, train_loss, '#' * (ratenum//2), '_' * (50 - ratenum//2), ratenum, suffix)
sys.stdout.write(bar)
sys.stdout.flush()
if num >= nums:
print()
def dataInfo_show(data_path,csv_pth,cls_dic_path,shapesShow=True,classesShow=True):
cls_dict = get_cls_dic(cls_dic_path)
if classesShow:
print('\n'+'*'*50)
df = pd.read_csv(csv_pth)
labels = df['label'].unique()
label_cls = {label:cls_dict[label] for label in labels}
print(label_cls)
cls_count = df['label'].value_counts()
cls_count = {cls_dict[k]:v for k,v in cls_count.items()}
for k,v in cls_count.items():
print(k,v)
if shapesShow:
print('\n'+'*'*50)
shapes = []
for filename in os.listdir(data_path):
img = Image.open(os.path.join(data_path, filename))
img = np.array(img)
shapes.append(img.shape)
shapes = pd.Series(shapes)
print(shapes.value_counts())
def get_cls_dic(cls_dic_path):
# 读取类标签字典，只取第一个逗号前的信息
cls_df = pd.read_csv(cls_dic_path)
cls_df['cls'] = cls_df['info'].apply(lambda x:x[:9]).tolist()
cls_df['label'] = cls_df['info'].apply(lambda x: x[10:]).tolist()
cls_df = cls_df.drop(columns=['info','other'])
cls_dict = cls_df.set_index('cls').T.to_dict('list')
cls_dict = {k:v[0] for k,v in cls_dict.items()}
return cls_dict
def dataset_divide(csv_pth):
cls_df = pd.read_csv(csv_pth, header=0,index_col=0)
cls_df.insert(1,'split',None)
filenames = list(cls_df['filename'])
random.shuffle(filenames)
train_num,train_val_num = int(len(filenames)*0.7),int(len(filenames)*0.8)
train_names = filenames[:train_num]
val_names = filenames[train_num:train_val_num]
test_names = filenames[train_val_num:]
cls_df.loc[cls_df['filename'].isin(train_names),'split'] = 'train'
cls_df.loc[cls_df['filename'].isin(val_names), 'split'] = 'val'
cls_df.loc[cls_df['filename'].isin(test_names), 'split'] = 'test'
cls_df.to_csv(csv_pth)
def draw_loss_acc(loss,acc,type='',save_path=None):
assert len(acc) == len(loss)
x = [epoch for epoch in range(len(acc))]
plt.subplot(2, 1, 1)
plt.plot(x, acc, 'o-')
plt.title(type+' accuracy vs. epoches')
plt.ylabel('accuracy')
plt.subplot(2, 1, 2)
plt.plot(x, loss, '.-')
plt.xlabel(type+' loss vs. epoches')
plt.ylabel('loss')
plt.show()
if save_path:
plt.savefig(os.path.join(save_path,type+"_acc_loss.png"))
if __name__ == '__main__':
pass
作者：GISer_Lin
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