Inception-V4 Architecture Explained .
Paper : Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.
Authors : Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi. Google.
Published in : Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence .
Model Architecture :
Stem Block :
Inception-A Block :
Inception-B Block :
Inception-C Block :
Reduction-A Block :
Reduction-B Block :
Auxiliary Classifier Block :
keras :
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from keras.models import Model
from keras.layers.merge import concatenate
from keras.layers import Conv2D , MaxPool2D , Input , GlobalAveragePooling2D ,AveragePooling2D, Dense , Dropout ,Activation , BatchNormalization
def conv2d_with_Batch(prev_layer , nbr_kernels , filter_size , strides = (1,1) , padding = 'valid'):
x = Conv2D(filters = nbr_kernels, kernel_size = filter_size, strides=strides , padding=padding) (prev_layer)
x = BatchNormalization()(x)
x = Activation(activation = 'relu') (x)
return x
def stemBlock(prev_layer):
x = conv2d_with_Batch(prev_layer, nbr_kernels = 32, filter_size = (3,3), strides = (2,2))
x = conv2d_with_Batch(x, nbr_kernels = 32, filter_size = (3,3))
x = conv2d_with_Batch(x, nbr_kernels = 64, filter_size = (3,3))
x_1 = conv2d_with_Batch(x, nbr_kernels = 96, filter_size = (3,3), strides = (2,2) )
x_2 = MaxPool2D(pool_size=(3,3) , strides=(2,2) ) (x)
x = concatenate([x_1 , x_2], axis = 3)
x_1 = conv2d_with_Batch(x, nbr_kernels = 64, filter_size = (1,1))
x_1 = conv2d_with_Batch(x_1, nbr_kernels = 64, filter_size = (1,7) , padding ='same')
x_1 = conv2d_with_Batch(x_1, nbr_kernels = 64, filter_size = (7,1), padding ='same')
x_1 = conv2d_with_Batch(x_1, nbr_kernels = 96, filter_size = (3,3))
x_2 = conv2d_with_Batch(x, nbr_kernels = 96, filter_size = (1,1))
x_2 = conv2d_with_Batch(x_2, nbr_kernels = 96, filter_size = (3,3))
x = concatenate([x_1 , x_2], axis = 3)
x_1 = conv2d_with_Batch(x, nbr_kernels = 192, filter_size = (3,3) , strides=2)
x_2 = MaxPool2D(pool_size=(3,3) , strides=(2,2) ) (x)
x = concatenate([x_1 , x_2], axis = 3)
return x
def reduction_A_Block(prev_layer) :
x_1 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 192, filter_size = (1,1))
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 224, filter_size = (3,3) , padding='same')
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 256, filter_size = (3,3) , strides=(2,2))
x_2 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 384, filter_size = (3,3) , strides=(2,2))
x_3 = MaxPool2D(pool_size=(3,3) , strides=(2,2))(prev_layer)
x = concatenate([x_1 , x_2 , x_3], axis = 3)
return x
def reduction_B_Block(prev_layer):
x_1 = MaxPool2D(pool_size=(3,3) , strides=(2,2))(prev_layer)
x_2 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 192, filter_size = (1,1))
x_2 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 192, filter_size = (3,3) , strides=(2,2) )
x_3 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 256, filter_size = (1,1) )
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 256, filter_size = (1,7) , padding='same')
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 320, filter_size = (7,1) , padding='same')
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 320, filter_size = (3,3) , strides=(2,2))
x = concatenate([x_1 , x_2 , x_3], axis = 3)
return x
def InceptionBlock_A(prev_layer): #I'm Here
x_1 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 64, filter_size = (1,1))
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 96, filter_size = (3,3) , strides=(1,1), padding='same' )
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 96, filter_size = (3,3) , strides=(1,1) , padding='same')
x_2 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 64, filter_size = (1,1))
x_2 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 96, filter_size = (3,3) , padding='same')
x_3 = AveragePooling2D(pool_size=(3,3) , strides=1 , padding='same')(prev_layer)
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 96, filter_size = (1,1) , padding='same')
x_4 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 96, filter_size = (1,1))
output = concatenate([x_1 , x_2 , x_3 , x_4], axis = 3)
return output
def InceptionBlock_B(prev_layer):
x_1 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 192, filter_size = (1,1))
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 192, filter_size = (7,1) , padding='same')
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 224, filter_size = (1,7) , padding='same')
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 224, filter_size = (7,1) , padding='same')
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 256, filter_size = (1,7), padding='same')
x_2 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 192, filter_size = (1,1))
x_2 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 224, filter_size = (1,7) , padding='same')
x_2 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 256, filter_size = (7,1), padding='same')
x_3 = AveragePooling2D(pool_size=(3,3) , strides=1 , padding='same')(prev_layer)
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 128, filter_size = (1,1))
x_4 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 384, filter_size = (1,1))
output = concatenate([x_1 , x_2 ,x_3, x_4], axis = 3)
return output
def InceptionBlock_C(prev_layer):
x_1 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 384, filter_size = (1,1))
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 448, filter_size = (3,1) , padding='same')
x_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 512, filter_size = (1,3) , padding='same')
x_1_1 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 256, filter_size = (1,3), padding='same')
x_1_2 = conv2d_with_Batch(prev_layer = x_1, nbr_kernels = 256, filter_size = (3,1), padding='same')
x_1 = concatenate([x_1_1 , x_1_2], axis = 3)
x_2 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 384, filter_size = (1,1))
x_2_1 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 256, filter_size = (1,3), padding='same')
x_2_2 = conv2d_with_Batch(prev_layer = x_2, nbr_kernels = 256, filter_size = (3,1), padding='same')
x_2 = concatenate([x_2_1 , x_2_2], axis = 3)
x_3 = MaxPool2D(pool_size=(3,3),strides = 1 , padding='same')(prev_layer)
x_3 = conv2d_with_Batch(prev_layer = x_3, nbr_kernels = 256, filter_size = 3 , padding='same')
x_4 = conv2d_with_Batch(prev_layer = prev_layer, nbr_kernels = 256, filter_size = (1,1))
output = concatenate([x_1 , x_2 , x_3 , x_4], axis = 3)
return output
def InceptionV4():
input_layer = Input(shape=(299 , 299 , 3))
x = stemBlock(prev_layer=input_layer)
x = InceptionBlock_A(prev_layer=x)
x = InceptionBlock_A(prev_layer=x)
x = InceptionBlock_A(prev_layer=x)
x = InceptionBlock_A(prev_layer=x)
x = reduction_A_Block(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = InceptionBlock_B(prev_layer=x)
x = reduction_B_Block(prev_layer= x)
x = InceptionBlock_C(prev_layer=x)
x = InceptionBlock_C(prev_layer=x)
x = InceptionBlock_C(prev_layer=x)
x = GlobalAveragePooling2D()(x)
x = Dense(units = 1536, activation='relu') (x)
x = Dropout(rate = 0.8) (x)
x = Dense(units = 1000, activation='softmax')(x)
model = Model(inputs = input_layer , outputs = x , name ='Inception-V4')
return model
pyTorch :
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import torch.nn as nn
import torch
import torch.nn.functional as F
class conv_Block(nn.Module):
def __init__(self, in_channels , out_channels , kernel_size , stride , padding):
super(conv_Block , self).__init__()
self.conv = nn.Conv2d(in_channels , out_channels , kernel_size , stride , padding)
self.batchNormalization = nn.BatchNorm2d(out_channels)
self.activation = nn.ReLU()
def forward(self,x):
out = self.conv(x)
out = self.batchNormalization(out)
out = self.activation(out)
return out
class Stem_Block(nn.Module):
def __init__(self , in_channels):
super(Stem_Block , self).__init__()
self.conv1 = conv_Block(in_channels , 32 , 3 , 2 , 0)
self.conv2 = conv_Block(32 , 32 , 3 , 1 , 0)
self.conv3 = conv_Block(32 , 64 , 3 , 1 , 1)
self.branch1 = conv_Block(64 , 96 , 3 , 2 , 0)
self.branch2 = nn.MaxPool2d(kernel_size=(3,3) , stride=2 , padding=0)
self.branch1_1 = nn.Sequential(
conv_Block(160 , 64 , 1 , 1 , 0),
conv_Block(64 , 64 , (1,7) , 1 , (0,3)),
conv_Block(64 , 64 , (7,1) , 1 , (3,0)),
conv_Block(64 , 96 , 3 , 1 , 0)
)
self.branch2_1 = nn.Sequential(
conv_Block(160 , 64 , 1 , 1 , 0),
conv_Block(64 , 96 , 3 , 1 , 0)
)
self.branch1_2 = conv_Block(192 , 192,3 , 2 , 0)
self.branch2_2 = nn.MaxPool2d(kernel_size=(3,3) , stride=2 , padding=0)
def forward(self , x):
out = self.conv1(x)
out = self.conv2(out)
out = self.conv3(out)
branch1 = self.branch1(out)
branch2 = self.branch2(out)
out = torch.cat([branch1 , branch2] , 1)
branch1 = self.branch1_1(out)
branch2 = self.branch2_1(out)
out = torch.cat([branch1 , branch2] , 1)
branch1 = self.branch1_2(out)
branch2 = self.branch2_2(out)
out = torch.cat([branch1 , branch2] , 1)
return out
class inception_Block_A(nn.Module):
def __init__(self , in_channels):
super(inception_Block_A , self).__init__()
self.branch1 = nn.Sequential(
conv_Block(in_channels , 64 , 1 , 1 , 0),
conv_Block(64 , 96 , 3 , 1 , 1),
conv_Block(96 , 96 , 3 , 1 , 1)
)
self.branch2 = nn.Sequential(
conv_Block(in_channels , 64 , 1 , 1 , 0),
conv_Block(64 , 96 , 3 , 1 , 1)
)
self.branch3 = nn.Sequential(
nn.MaxPool2d(kernel_size=(3,3) , stride=1 , padding=1),
conv_Block(in_channels , 96 , 1 ,1 ,0 )
)
self.branch4 = conv_Block(in_channels , 96 , 1 , 1 , 0)
def forward(self , x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
out = torch.cat([branch1 , branch2 , branch3 , branch4] , 1)
return out
class inception_Block_B(nn.Module):
def __init__(self , in_channels):
super(inception_Block_B , self).__init__()
self.branch1 = nn.Sequential(
conv_Block(in_channels , 192 , 1 , 1 , 0),
conv_Block(192 , 192 , (7,1) , 1 , (3,0)),
conv_Block(192 , 224 , (1,7) , 1 , (0,3)),
conv_Block(224 , 224 , (7,1) , 1 , (3,0)),
conv_Block(224 , 256 , (1,7) , 1 , (0,3)),
)
self.branch2 = nn.Sequential(
conv_Block(in_channels , 192 , 1 , 1 , 0),
conv_Block(192 , 224 , (1,7) , 1 , (0,3)),
conv_Block(224 , 256 , (7,1) , 1 , (3,0)),
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(kernel_size=(3,3) , stride=1 , padding=1),
conv_Block(in_channels , 128 , 1 , 1 , 0)
)
self.branch4 = conv_Block(in_channels , 384 , 1 , 1 , 0)
def forward(self , x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
out = torch.cat([branch1 , branch2 , branch3 , branch4] , 1)
return out
class inception_Block_C(nn.Module):
def __init__(self , in_channels):
super(inception_Block_C , self).__init__()
self.branch1 = nn.Sequential(
conv_Block(in_channels , 384 , 1 , 1 , 0),
conv_Block(384 , 448 , (3,1) , 1 , (1,0)),
conv_Block(448 , 512 , (1,3) , 1 , (0,1))
)
self.branch1_1 = conv_Block(512 , 256 , (1,3) , 1 , (0,1))
self.branch1_2 = conv_Block(512 , 256 , (3,1) , 1 , (1,0))
self.branch2 = conv_Block(in_channels , 384 , 1 , 1 , 0)
self.branch2_1 = conv_Block(384 , 256 , (1,3) , 1 , (0,1))
self.branch2_2 = conv_Block(384 , 256 , (3,1) , 1 , (1,0))
self.branch3 = nn.Sequential(
nn.MaxPool2d(kernel_size=3 , stride=1 , padding=1),
conv_Block(in_channels , 256 , 3 , 1 , 1)
)
self.branch4 = conv_Block(in_channels , 256 , 1 , 1 , 0)
def forward(self , x):
branch1 = self.branch1(x)
branch1_1 = self.branch1_1(branch1)
branch1_2 = self.branch1_2(branch1)
branch1 = torch.cat([branch1_1 , branch1_2] , 1)
branch2 = self.branch2(x)
branch2_1 = self.branch2_1(branch2)
branch2_2 = self.branch2_2(branch2)
branch2 = torch.cat([branch2_1 , branch2_2] , 1)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
out = torch.cat([branch1 , branch2 , branch3 , branch4] , 1)
return out
class reduction_Block_A(nn.Module):
def __init__(self , in_channels):
super(reduction_Block_A , self).__init__()
self.branch1 = nn.Sequential(
conv_Block(in_channels , 192 , 1 , 1 , 0),
conv_Block(192 , 224 , 3 , 1 , 1),
conv_Block(224 , 256 , 3 , 2 , 0)
)
self.branch2 = conv_Block(in_channels , 384 , 3 , 2 , 0)
self.branch3 = nn.MaxPool2d(kernel_size = 3 , stride=2 , padding=0 )
def forward(self , x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
out = torch.cat([branch1 ,branch2 , branch3 ] , 1)
return out
class reduction_Block_B(nn.Module):
def __init__(self , in_channels):
super(reduction_Block_B , self).__init__()
self.branch1 = nn.Sequential(
conv_Block(in_channels , 256 , 1 , 1 , 0),
conv_Block(256 , 256 , (1,7) , 1 , (0,3)),
conv_Block(256 , 320 , (7,1) , 1 , (3,0)),
conv_Block(320 , 320 , 3 , 2 , 0)
)
self.branch2 = nn.Sequential(
conv_Block(in_channels , 192 , 1 , 1 , 0),
conv_Block(192 , 192 , 3 , 2 , 0)
)
self.branch3 = nn.MaxPool2d(kernel_size=3 , stride=2 , padding=0)
def forward(self , x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
out = torch.cat([branch1 , branch2 , branch3] , 1)
return out
class InceptionV4(nn.Module):
def __init__(self):
super(InceptionV4 , self).__init__()
self.stem = Stem_Block(3)
self.inceptionA = inception_Block_A(384)
self.reductionA = reduction_Block_A(384)
self.inceptionB = inception_Block_B(1024)
self.reductionB = reduction_Block_B(1024)
self.inceptionC = inception_Block_C(1536)
self.fc1 = nn.Linear(in_features=1536 , out_features=1536)
self.fc2 = nn.Linear(in_features=1536 , out_features=1000)
self.globalAvgPool = conv_Block(1536 , 1536 , 8 , 1 , 0)
def forward(self,x):
out = self.stem(x)
out = self.inceptionA(out)
out = self.inceptionA(out)
out = self.inceptionA(out)
out = self.inceptionA(out)
out = self.reductionA(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.inceptionB(out)
out = self.reductionB(out)
out = self.inceptionC(out)
out = self.inceptionC(out)
out = self.inceptionC(out)
out = self.globalAvgPool(out)
out = out.reshape(out.shape[0] , -1)
out = self.fc1(out)
out = nn.ReLU()(out)
out = self.fc2(out)
out = nn.Softmax()(out)
return out