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Create InceptionV3.py
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@Rain-am
Rain-am authored Dec 25, 2024
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import numpy as np
from keras import layers
from keras.layers import Conv2D,Dense,BatchNormalization,Activation,Input,concatenate,AveragePooling2D,MaxPooling2D,GlobalAveragePooling2D
from keras.applications.imagenet_utils import decode_predictions
from keras.models import Model
from keras.preprocessing import image


def conv2d_bn(x,filters,num_row,num_col,strides=(1,1),padding='same',name=None):
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
elif name is None:
bn_name = None
conv_name = None


x = Conv2D(filters,(num_row,num_col),strides=strides,padding=padding,use_bias=False,name=conv_name)(x)
x = BatchNormalization(scale=False, name=bn_name)(x) # scale-是否进行缩放
x = Activation('relu',name=name)(x)
return x

def InceptionV3(inputs=[299,299,3],classes=1000):
input_image = Input(shape=inputs)

# 在进行block之前，先提取公用特征。不是所有特征都要区分尺度的,Inception分叉的主要目的是提取不同尺度的特征。
# output_size = 149*149*32
x = conv2d_bn(input_image,32,3,3,strides=(2,2),padding='valid')
# output_size = 147*147*32
x = conv2d_bn(x,32,3,3,padding='valid')
# output_size = 147*147*64
x = conv2d_bn(x, 64, 3, 3)
# output_size = 73*73*64
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
# output_size = 71*71*80
x = conv2d_bn(x, 80, 1, 1, padding='valid')
# output_size = 69*69*192
x = conv2d_bn(x, 192, 3, 3, padding='valid')
# output_size = 35*35*192
x = MaxPooling2D((3, 3), strides=(2, 2))(x)

# --------------------------------#
# Block1 35x35
# --------------------------------#
# Block1 part1
# 35 x 35 x 192 -> 35 x 35 x 256
branch1x1 = conv2d_bn(x, 64, 1, 1) # 1*1 卷积

branch5x5 = conv2d_bn(x, 48, 1, 1) # branch1x1和branch5x5 是并列关系，因为输入都是x
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) # 标准是3*3，但我们训练用的是imagenet数据集，用5*5，效果会更好。因为imagenet里特别小尺度的图片相对来说少。

branch3x3dbl = conv2d_bn(x, 64, 1, 1) # branch1x1和branch5x5和branch3x3dbl 是并列关系，因为输入都是x
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) # 2个3*3=1个5*5

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 32, 1, 1)

# CONCAT-合并 通道数：64+64+96+32 = 256
x = concatenate(
[branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=3, # 按channel合并。tensor的排列方式-nhwc
name='mixed0')

# Block1 part2
# 35 x 35 x 256 -> 35 x 35 x 288
branch1x1 = conv2d_bn(x, 64, 1, 1)

branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)

# 64+64+96+64 = 288
x = concatenate(
[branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=3,
name='mixed1')

# Block1 part3
# 35 x 35 x 288 -> 35 x 35 x 288
branch1x1 = conv2d_bn(x, 64, 1, 1)

branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)

# 64+64+96+64 = 288
x = concatenate(
[branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=3,
name='mixed2')

# --------------------------------#
# Block2 17x17
# --------------------------------#
# Block2 part1
# 35 x 35 x 288 -> 17 x 17 x 768
branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')

branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(
branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid')

branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = concatenate(
[branch3x3, branch3x3dbl, branch_pool], axis=3, name='mixed3')

# Block2 part2
# 17 x 17 x 768 -> 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)

branch7x7 = conv2d_bn(x, 128, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

branch7x7dbl = conv2d_bn(x, 128, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) # 7*7
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) # 7*7。2个7*7代替1个9*7

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=3,
name='mixed4')

# Block2 part3 and part4
# 17 x 17 x 768 -> 17 x 17 x 768 -> 17 x 17 x 768
for i in range(2):
branch1x1 = conv2d_bn(x, 192, 1, 1)

branch7x7 = conv2d_bn(x, 160, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

branch7x7dbl = conv2d_bn(x, 160, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=3,
name='mixed' + str(5 + i))

# Block2 part5
# 17 x 17 x 768 -> 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)

branch7x7 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)

branch7x7dbl = conv2d_bn(x, 192, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)

branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=3,
name='mixed7')

# --------------------------------#
# Block3 8x8
# --------------------------------#
# Block3 part1
# 17 x 17 x 768 -> 8 x 8 x 1280
branch3x3 = conv2d_bn(x, 192, 1, 1)
branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
strides=(2, 2), padding='valid')

branch7x7x3 = conv2d_bn(x, 192, 1, 1)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
branch7x7x3 = conv2d_bn(
branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')

branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = concatenate(
[branch3x3, branch7x7x3, branch_pool], axis=3, name='mixed8')

# Block3 part2 part3
# 8 x 8 x 1280 -> 8 x 8 x 2048 -> 8 x 8 x 2048
for i in range(2):
branch1x1 = conv2d_bn(x, 320, 1, 1)

branch3x3 = conv2d_bn(x, 384, 1, 1)
branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
branch3x3 = concatenate(
[branch3x3_1, branch3x3_2], axis=3, name='mixed9_' + str(i))

branch3x3dbl = conv2d_bn(x, 448, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
branch3x3dbl = concatenate(
[branch3x3dbl_1, branch3x3dbl_2], axis=3)

branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch3x3, branch3x3dbl, branch_pool],
axis=3,
name='mixed' + str(9 + i))
# 平均池化后全连接。
x = GlobalAveragePooling2D()(x)
x = Dense(classes,activation='softmax', name='predictions')(x)

model = Model(input_image,x,name='inception_v3')
return model

def preprocess_input(x):
x /= 255.
x -= 0.5
x *= 2.
return x

if __name__=='__main__':
model = InceptionV3()
model.load_weights('E:/YAN/HelloWorld/cv/【12】图像识别/代码/inceptionV3_tf/inception_v3_weights_tf_dim_ordering_tf_kernels.h5')
img = image.load_img('E:/YAN/HelloWorld/cv/【12】图像识别/代码/inceptionV3_tf/elephant.jpg',target_size=(299,299))
img = image.img_to_array(img)
img = np.expand_dims(img,axis=0)
img = preprocess_input(img)
preds = model.predict(img)
print('Predicted:',decode_predictions(preds,1))

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