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model_Inception.py
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model_Inception.py
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from keras.models import Model
from keras.layers import Input, Conv3D, MaxPooling3D, Dense, GlobalMaxPooling3D, Dropout, BatchNormalization, \
Concatenate, AveragePooling3D, Activation
from keras.optimizers import Adam
from config import *
def conv_bn_relu(x, filters, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding='same'):
x = Conv3D(filters, kernel_size=kernel_size, strides=strides, padding=padding)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def inception_base(x):
x = conv_bn_relu(x, filters=32)
x = conv_bn_relu(x, filters=32)
x = conv_bn_relu(x, filters=64)
b0 = MaxPooling3D(pool_size=(2, 2, 2))(x)
b1 = conv_bn_relu(x, 64, strides=(2, 2, 2))
x = Concatenate(axis=4)([b0, b1])
print('inception_base')
print(b0.get_shape())
print(b1.get_shape())
print(x.get_shape())
return x
def inception_block(x, filters=256):
shrinkaged_filters = int(filters * INCEPTION_ENABLE_DEPTHWISE_SEPARABLE_CONV_SHRINKAGE)
b0 = conv_bn_relu(x, filters=filters, kernel_size=(1, 1, 1))
b1 = conv_bn_relu(x, filters=shrinkaged_filters, kernel_size=(1, 1, 1))
b1 = conv_bn_relu(b1, filters=filters, kernel_size=(3, 3, 3))
b2 = conv_bn_relu(x, filters=shrinkaged_filters, kernel_size=(1, 1, 1))
b2 = conv_bn_relu(b2, filters=filters, kernel_size=(3, 3, 3))
b2 = conv_bn_relu(b2, filters=filters, kernel_size=(3, 3, 3))
b3 = AveragePooling3D(pool_size=(3, 3, 3), strides=(1, 1, 1), padding='same')(x)
b3 = conv_bn_relu(b3, filters=filters, kernel_size=(1, 1, 1))
bs = [b0, b1, b2, b3]
print('inception_block')
print(b0.get_shape())
print(b1.get_shape())
print(b2.get_shape())
print(b3.get_shape())
if INCEPTION_ENABLE_SPATIAL_SEPARABLE_CONV:
b4 = conv_bn_relu(x, filters=shrinkaged_filters, kernel_size=(1, 1, 1))
b4 = conv_bn_relu(b4, filters=filters, kernel_size=(5, 1, 1))
b4 = conv_bn_relu(b4, filters=filters, kernel_size=(1, 5, 1))
b4 = conv_bn_relu(b4, filters=filters, kernel_size=(1, 1, 5))
bs.append(b4)
print(b4.get_shape())
x = Concatenate(axis=4)(bs)
print(x.get_shape())
return x
def reduction_block(x, filters=256):
b0 = conv_bn_relu(x, filters=filters, kernel_size=(3, 3, 3), strides=(2, 2, 2), padding='same')
b1 = conv_bn_relu(x, filters=filters, kernel_size=(1, 1, 1))
b1 = conv_bn_relu(b1, filters=filters, kernel_size=(3, 3, 3))
b1 = conv_bn_relu(b1, filters=filters, kernel_size=(3, 3, 3), strides=(2, 2, 2), padding='same')
b2 = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), padding='same')(x)
b2 = conv_bn_relu(b2, filters=filters, kernel_size=(1, 1, 1))
bs = [b0, b1, b2]
print('reduction_block')
print(b0.get_shape())
print(b1.get_shape())
print(b2.get_shape())
if INCEPTION_ENABLE_SPATIAL_SEPARABLE_CONV:
b3 = conv_bn_relu(x, filters=filters, kernel_size=(1, 1, 1))
b3 = conv_bn_relu(b3, filters=filters, kernel_size=(5, 1, 1))
b3 = conv_bn_relu(b3, filters=filters, kernel_size=(1, 5, 1))
b3 = conv_bn_relu(b3, filters=filters, kernel_size=(1, 1, 5))
b3 = conv_bn_relu(b3, filters=filters, kernel_size=(3, 3, 3), strides=(2, 2, 2), padding='same')
bs.append(b3)
print(b3.get_shape())
x = Concatenate(axis=4)(bs)
print(x.get_shape())
return x
def get_Inception_classifier():
inputs = Input((CLASSIFY_INPUT_WIDTH, CLASSIFY_INPUT_HEIGHT, CLASSIFY_INPUT_DEPTH, CLASSIFY_INPUT_CHANNEL))
print('inputs')
print(inputs.get_shape())
# Make inception base
x = inception_base(inputs)
for i in range(INCEPTION_BLOCKS):
x = inception_block(x, filters=INCEPTION_KEEP_FILTERS)
if (i + 1) % INCEPTION_REDUCTION_STEPS == 0 and i != INCEPTION_BLOCKS - 1:
x = reduction_block(x, filters=INCEPTION_KEEP_FILTERS // 2)
print('top')
x = GlobalMaxPooling3D()(x)
print(x.get_shape())
x = Dropout(INCEPTION_DROPOUT)(x)
x = Dense(2, activation='softmax')(x)
print(x.get_shape())
model = Model(inputs=inputs, outputs=x)
model.compile(optimizer=Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE), loss='binary_crossentropy', metrics=['accuracy'])
return model