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spatial_temporal_attention_network.py
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spatial_temporal_attention_network.py
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from keras.layers import Input,TimeDistributed,LSTM,Bidirectional
from keras.layers.core import Lambda,Flatten,Dense,Reshape,Activation,Lambda,Permute
from keras.layers.convolutional import Conv2D,UpSampling2D,Conv1D
from keras.layers.pooling import MaxPooling2D, AveragePooling1D,AveragePooling2D
from keras.layers.merge import Add,Concatenate,Dot,Multiply
from keras.regularizers import l2
from keras.optimizers import SGD,Adam
from keras.models import Model
from keras import backend as K
from keras.applications.resnet50 import ResNet50
train_classes = 314
def focal_loss(target, output, gamma=2):
output /= K.sum(output, axis=-1, keepdims=True)
eps = K.epsilon()
output = K.clip(output, eps, 1. - eps)
return -K.sum(K.pow(1. - output, gamma) * target * K.log(output),
axis=-1)
def generate_model(weight_decay=0.0005):
# spatial attention network
merged_input = Input(shape=(224, 224, 6))
split1 = Lambda(lambda x: x[:, :, :, 0:3], name='split1')
split2 = Lambda(lambda x: x[:, :, :, 3:], name='split2')
data1 = split1(merged_input)
data2 = split2(merged_input)
base_model = ResNet50(weights=None, include_top=False) # weights=None for test, weights='iamgenet' for train
share_conv_1 = Model(input=base_model.input, output=base_model.get_layer('activation_49').output)
x1 = share_conv_1(data1)
x2 = share_conv_1(data2)
reshape1 = Reshape((49, 2048))
x1 = reshape1(x1)
x2 = reshape1(x2)
l2_norm_channel = Lambda(lambda x: K.l2_normalize(x,axis=-1))
x1_l2 = l2_norm_channel(x1)
x2_l2 = l2_norm_channel(x2)
x2_l2 = Permute((2, 1))(x2_l2)
matrix_dot = Lambda(lambda x: K.batch_dot(x[0], x[1]))
x_com = matrix_dot([x1_l2, x2_l2])
x_com_T = Permute((2, 1))(x_com)
share_conv_2 = Conv1D(1, 1, padding="same", kernel_regularizer=l2(weight_decay))
x1_att = share_conv_2(x_com)
x2_att = share_conv_2(x_com_T)
reshape2 = Reshape((49,))
x1_att = reshape2(x1_att)
x2_att = reshape2(x2_att)
softmax = Activation('softmax')
x1_att = softmax(x1_att)
x2_att = softmax(x2_att)
reshape3 = Reshape((49, 1))
x1_att = reshape3(x1_att)
x2_att = reshape3(x2_att)
h1 = Multiply()([x1, x1_att])
h2 = Multiply()([x2, x2_att])
summary = Lambda(lambda x: K.sum(x, axis=1))
h1 = summary(h1)
h2 = summary(h2)
id_layer = Dense(train_classes, kernel_regularizer=l2(weight_decay), activation='softmax')
y1 = id_layer(h1)
y2 = id_layer(h2)
x_concat = Concatenate()([h1, h2])
x_concat = Dense(512, kernel_regularizer=l2(weight_decay), activation='relu')(x_concat)
spatial_model = Model(inputs=merged_input, outputs=[y1, y2, x_concat]) # spatial attention model
spatial_model.summary()
#spatial_model.load_weights('/media/tensend/dish_disk/MOT_keras/weights_spatial_dot_softmax/my_weights_on_mot16_0_0_15.h5', by_name=True) # fix the weights of the spatial attention network to train the temporal attention network
for layer in spatial_model.layers[:]:
layer.trainable = False
spatial_model.layers[-2].trainable = True
print spatial_model.layers[-2].name
# temporal attention network
time_steps = 8
seq_merged_input = Input(shape=(time_steps, 224, 224, 6))
ST_outputs = []
for i in range(2):
ST_outputs.append(TimeDistributed(Model(spatial_model.input, spatial_model.output[i]))(seq_merged_input))
lstm_input = TimeDistributed(Model(spatial_model.input, spatial_model.output[2]))(seq_merged_input)
temporal_model = Bidirectional(LSTM(512, kernel_regularizer=l2(weight_decay), recurrent_regularizer=l2(weight_decay), return_sequences=True))(lstm_input)
beta = TimeDistributed(Dense(1, kernel_regularizer=l2(weight_decay)))(temporal_model)
beta = Reshape((time_steps,))(beta)
beta = Activation('softmax')(beta)
beta = Reshape((time_steps, 1))(beta)
weighted_output = Multiply()([temporal_model, beta])
summary = Lambda(lambda x: K.sum(x, 1))
h = summary(weighted_output)
yf = Dense(2, kernel_regularizer=l2(weight_decay), activation='softmax')(h)
ST_outputs.append(yf)
ST_model = Model(inputs=seq_merged_input, outputs=ST_outputs)
ST_model.summary()
return ST_model
def compile_model(model, *args, **kw):
class SGD_new(SGD):
'''
redefinition of the original SGD
'''
def __init__(self, lr=0.01, momentum=0., decay=0.,
nesterov=False, **kwargs):
super(SGD, self).__init__(**kwargs)
self.__dict__.update(locals())
self.iterations = K.variable(0.)
self.lr = K.variable(lr)
self.momentum = K.variable(momentum)
self.decay = K.variable(decay)
self.inital_decay = decay
def get_updates(self, params, constraints, loss):
grads = self.get_gradients(loss, params)
self.updates = []
lr = self.lr
if self.inital_decay > 0:
lr *= (1. / (1. + self.decay * self.iterations)) ** 0.75
self.updates .append(K.update_add(self.iterations, 1))
# momentum
shapes = [K.get_variable_shape(p) for p in params]
moments = [K.zeros(shape) for shape in shapes]
self.weights = [self.iterations] + moments
for p, g, m in zip(params, grads, moments):
v = self.momentum * m - lr * g # velocity
self.updates.append(K.update(m, v))
if self.nesterov:
new_p = p + self.momentum * v - lr * g
else:
new_p = p + v
# apply constraints
if p in constraints:
c = constraints[p]
new_p = c(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
all_classes = {
'sgd_new': 'SGD_new(lr=0.01, momentum=0.9)',
'sgd': 'SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)',
'rmsprop': 'RMSprop(lr=0.0001, rho=0.9, epsilon=1e-06)',
'adagrad': 'Adagrad(lr=0.01, epsilon=1e-06)',
'adadelta': 'Adadelta(lr=1.0, rho=0.95, epsilon=1e-06)',
'adam': 'Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)',
'adamax': 'Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)',
'nadam': 'Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004)',
}
param = {'optimizer': 'adam', 'loss': 'categorical_crossentropy', 'metrics': 'accuracy'}
config = ''
if len(kw):
for (key, value) in kw.items():
if key in param:
param[key] = kw[key]
elif key in all_classes:
config = kw[key]
else:
print 'error'
if not len(config):
config = all_classes[param['optimizer']]
optimiz = eval(config)
model.compile(optimizer=optimiz,
loss=['categorical_crossentropy', 'categorical_crossentropy', focal_loss],
loss_weights=[0.5, 0.5, 1.0],
metrics=['accuracy'])
print("Model Compile Successful.")
return model
if __name__ == "__main__":
"""
Just for model testing.
"""
model = generate_model()
model = compile_model(model)