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create-model.py
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import sys
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Add, Activation, Concatenate, Input, LeakyReLU
from tensorflow.keras.models import Model
from tensorflow.keras.applications.mobilenet import MobileNet
from src.keras_utils import save_model
def res_block(x,sz,filter_sz=3,in_conv_size=1):
xi = x
for i in range(in_conv_size):
# xi = Conv2D(sz, filter_sz, activation='linear', padding='same')(xi)
# xi = BatchNormalization()(xi)
# xi = LeakyReLU()(xi)
xi = Conv2D(sz, filter_sz, activation=None, padding='same')(xi)
xi = Activation(activation='linear')(xi)
xi = BatchNormalization()(xi)
xi = LeakyReLU()(xi)
# xi = LeakyReLU(alpha=0.01)(xi)
xi = Conv2D(sz, filter_sz, activation=None, padding='same')(xi)
xi = Activation(activation='linear')(xi)
xi = BatchNormalization()(xi)
xi = Add()([xi,x])
xi = LeakyReLU()(xi)
return xi
def conv_batch(_input,fsz,csz,activation='relu',padding='same',strides=(1,1)):
output = Conv2D(fsz, csz, activation=None, padding=padding, strides=strides)(_input)
output = Activation('linear')(output)
output = BatchNormalization()(output)
# output = Activation(activation)(output)
output = LeakyReLU()(output)
return output
def end_block(x):
# xprobs = Conv2D(2, 3, activation='softmax', padding='same')(x)
# xbbox = Conv2D(6, 3, activation='linear' , padding='same')(x)
xprobs = Conv2D(2, 3, activation=None, padding='same')(x)
xprobs = Activation('softmax')(xprobs)
xbbox = Conv2D(6, 3, activation=None, padding='same')(x)
xbbox = Activation('linear')(xbbox)
return Concatenate(3)([xprobs,xbbox])
def create_model_eccv():
input_layer = Input(shape=(None,None,3),name='input')
x = conv_batch(input_layer, 16, 3)
x = conv_batch(x, 16, 3)
x = MaxPooling2D(pool_size=(2,2))(x)
x = conv_batch(x, 32, 3)
x = res_block(x, 32)
x = MaxPooling2D(pool_size=(2,2))(x)
x = conv_batch(x, 64, 3)
x = res_block(x,64)
x = res_block(x,64)
x = MaxPooling2D(pool_size=(2,2))(x)
x = conv_batch(x, 64, 3)
x = res_block(x,64)
x = res_block(x,64)
x = MaxPooling2D(pool_size=(2,2))(x)
x = conv_batch(x, 128, 3)
x = res_block(x,128)
x = res_block(x,128)
x = res_block(x,128)
x = res_block(x,128)
x = end_block(x)
return Model(inputs=input_layer,outputs=x)
# Model not converging...
def create_model_mobnet():
input_layer = Input(shape=(None,None,3),name='input')
x = input_layer
mbnet = MobileNet(input_shape=(224,224,3),include_top=True)
backbone = keras.models.clone_model(mbnet)
for i,bblayer in enumerate(backbone.layers[1:74]):
layer = bblayer.__class__.from_config(bblayer.get_config())
layer.name = 'backbone_' + layer.name
x = layer(x)
x = end_block(x)
model = Model(inputs=input_layer,outputs=x)
backbone_layers = {'backbone_' + layer.name: layer for layer in backbone.layers}
for layer in model.layers:
if layer.name in backbone_layers:
print ('setting ' + layer.name)
layer.set_weights(backbone_layers[layer.name].get_weights())
return model
if __name__ == '__main__':
modules = [func.replace('create_model_','') for func in dir(sys.modules[__name__]) if 'create_model_' in func]
assert sys.argv[1] in modules, \
'Model name must be on of the following: %s' % ', '.join(modules)
modelf = getattr(sys.modules[__name__],'create_model_' + sys.argv[1])
print ('Creating model %s' % sys.argv[1])
model = modelf()
print ('Finished')
print ('Saving at %s' % sys.argv[2])
save_model(model,sys.argv[2])