def __init__(self,h_size,rnn_cell,myScope):
self.scalarInput = tf.placeholder(shape=[None,21168],dtype=tf.float32)
self.imageIn = tf.reshape(self.scalarInput,shape=[-1,84,84,3])
self.conv1 = slim.convolution2d(inputs = self.imageIn,num_outputs=32,kernel_size = [8,8],
stride=[4,4],padding='VALID',biases_initializer=None,
scope=myScope + "_conv1") #[-1,20,20,32]
self.conv2 = slim.convolution2d(inputs = self.conv1, num_outputs = 64,kernel_size=[4,4],
stride=[2,2],padding='VALID',
biases_initializer= None,scope=myScope+"_conv2") # [-1,9,9,64]
self.conv3 = slim.convolution2d(inputs = self.conv2,num_outputs = 64,kernel_size=[3,3],stride=[1,1],
padding='VALID',biases_initializer=None,scope=myScope+"_conv3") #[-1,7,7,64]
self.conv4 = slim.convolution2d(inputs = self.conv3,num_outputs=h_size,kernel_size=[7,7],stride=[1,1],
padding='VALID',biases_initializer=None,scope=myScope+"_conv4") #[-1,1,1,h_size]
self.trainLength = tf.placeholder(tf.int32)
self.batch_size = tf.placeholder(tf.int32,[])
self.convFlat = tf.reshape(slim.flatten(self.conv4),[self.batch_size,self.trainLength,h_size])
self.state_in = rnn_cell.zero_state(self.batch_size,tf.float32)
self.rnn,self.rnn_state = tf.nn.dynamic_rnn(inputs=self.convFlat,cell=rnn_cell,dtype=tf.float32,
initial_state=self.state_in,scope=myScope+"rnn")
self.rnn = tf.reshape(self.rnn,shape=[-1,h_size])
self.streamA,self.streamV = tf.split(self.rnn,2,1)
self.AW = tf.Variable(tf.random_normal([h_size//2,4]))
self.VW = tf.Variable(tf.random_normal([h_size//2,1]))
self.Advantage = tf.matmul(self.streamA,self.AW)
self.Value = tf.matmul(self.streamV,self.VW)
self.salience = tf.gradients(self.Advantage,self.imageIn)
self.Qout = self.Value + tf.subtract(self.Advantage,tf.reduce_mean(self.Advantage,axis=1,keep_dims=True))
self.predict =tf.argmax(self.Qout,1)
self.targetQ = tf.placeholder(shape=[None],dtype=tf.float32)
self.actions = tf.placeholder(shape=[None],dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions,4,dtype=tf.float32)
self.Q = tf.reduce_sum(tf.multiply(self.Qout,self.actions_onehot),axis=1)
self.td_error = tf.square(self.targetQ - self.Q)
self.maskA = tf.zeros([self.batch_size,self.trainLength//2])
self.maskB = tf.ones([self.batch_size,self.trainLength//2])
self.mask = tf.concat([self.maskA,self.maskB],1)
self.mask = tf.reshape(self.mask,[-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate=0.001)
self.updateModel = self.trainer.minimize(self.loss)
def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.separable_convolution2d(input,
num_outputs=None,
stride=stride,
trainable=self.trainable,
depth_multiplier=1.0,
kernel_size=[k_h, k_w],
# activation_fn=common.activation_fn if relu else None,
activation_fn=None,
# normalizer_fn=slim.batch_norm,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
weights_regularizer=_l2_regularizer_00004,
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=common.activation_fn if relu else None,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
biases_initializer=_init_zero if set_bias else None,
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=None,
scope=name + '_pointwise')
return output
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
def convb(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True, set_tanh=False):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
output = slim.convolution2d(input, c_o, kernel_size=[k_h, k_w],
stride=stride,
normalizer_fn=slim.batch_norm,
weights_regularizer=_l2_regularizer_convb,
weights_initializer=_init_xavier,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=_init_zero if set_bias else None,
trainable=self.trainable,
activation_fn=common.activation_fn if relu else None,
scope=name)
if set_tanh:
output = tf.nn.sigmoid(output, name=name + '_extra_acv')
return output
def _depthwise_separable_conv(inputs,
num_pwc_filters,
sc,
kernel_size,
stride):
""" Helper function to build the depth-wise separable convolution layer.
"""
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=stride,
depth_multiplier=1,
kernel_size=kernel_size,
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
def pfld_inference_for_mobileNetV3_small(input, weight_decay, batch_norm_params):
layers = [
[16, 16, 3, 2, "RE", True, 16],
[16, 24, 3, 2, "RE", False, 72],
[24, 24, 3, 1, "RE", False, 88],
[24, 40, 5, 2, "HS", True, 96],
[40, 40, 5, 1, "HS", True, 240],
[40, 40, 5, 1, "HS", True, 240],
[40, 48, 5, 1, "HS", True, 120],
[48, 48, 5, 1, "HS", True, 144],
[48, 96, 5, 2, "HS", True, 288],
[96, 96, 5, 1, "HS", True, 576],
[96, 96, 5, 1, "HS", True, 576],
]
reduction_ratio = 4
multiplier = 1
with tf.variable_scope('pfld_inference'):
features = {}
with slim.arg_scope([slim.convolution2d, slim.separable_conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=tf.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
padding='SAME'):
print('PFLD input shape({}): {}'.format(input.name, input.get_shape()))
# 112*112*3
out = slim.convolution2d(input, 16 * multiplier, [3, 3], stride=1, activation_fn=hard_swish, scope='conv_1')
print(out.name, out.get_shape())
with tf.variable_scope("MobilenetV3_large"):
for index in range(3):
in_channels, out_channels, kernel_size, stride, activatation, se, expand_dims = layers[index]
out_channels *= multiplier
out = mobileNetV3_block(out, "bneck{}".format(index), expand_dims, out_channels, kernel_size,
stride, ratio=reduction_ratio, activation_fn=activatation, se=se,
short_cut=(in_channels == out_channels))
print(out.name, out.get_shape())
# 28*28
features['auxiliary_input'] = out
# 14*14
index = 3
in_channels, out_channels, kernel_size, stride, activatation, se, expand_dims = layers[index]
out_channels *= multiplier
out1 = mobileNetV3_block(out, "bneck{}".format(index), expand_dims, out_channels, kernel_size,
stride, ratio=reduction_ratio, activation_fn=activatation, se=se,
short_cut=(in_channels == out_channels))
print(out1.name, out1.get_shape())
for index in range(4, 8):
in_channels, out_channels, kernel_size, stride, activatation, se, expand_dims = layers[index]
out_channels *= multiplier
out1 = mobileNetV3_block(out1, "bneck{}".format(index), expand_dims, out_channels, kernel_size,
stride, ratio=reduction_ratio, activation_fn=activatation, se=se,
short_cut=(in_channels == out_channels))
print(out1.name, out1.get_shape())
# 7*7
index = 8
in_channels, out_channels, kernel_size, stride, activatation, se, expand_dims = layers[index]
out_channels *= multiplier
out2 = mobileNetV3_block(out1, "bneck{}".format(index), expand_dims, out_channels, kernel_size, stride,
ratio=reduction_ratio, activation_fn=activatation, se=se,
short_cut=(in_channels == out_channels))
print(out2.name, out2.get_shape())
for index in range(9, len(layers)):
in_channels, out_channels, kernel_size, stride, activatation, se, expand_dims = layers[index]
out_channels *= multiplier
out2 = mobileNetV3_block(out2, "bneck{}".format(index), expand_dims, out_channels, kernel_size,
stride, ratio=reduction_ratio, activation_fn=activatation, se=se,
short_cut=(in_channels == out_channels))
print(out2.name, out2.get_shape())
out3 = slim.convolution2d(out2, 576, [1, 1], stride=1, activation_fn=hard_swish, dscope='conv_2')
print(out3.name, out3.get_shape())
out3 = slim.avg_pool2d(out3, [out3.get_shape()[1], out3.get_shape()[2]], stride=1, scope='group_pool')
print(out3.name, out3.get_shape())
out3 = slim.convolution2d(out3, 1280, [1, 1], stride=1, normalizer_fn=None, activation_fn=hard_swish,
scope='conv_3')
print(out3.name, out3.get_shape())
s1 = slim.flatten(out1)
s2 = slim.flatten(out2)
s3 = slim.flatten(out3)
multi_scale = tf.concat([s1, s2, s3], 1)
landmarks = slim.fully_connected(multi_scale, num_outputs=136, activation_fn=None, scope='fc')
print(landmarks.name, landmarks.get_shape())
return features, landmarks
def mobilenet(inputs,
num_classes=1000,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
""" MobileNet
More detail, please refer to Google's paper(https://arxiv.org/abs/1704.04861).
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
scope: Optional scope for the variables.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, `num_classes`]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu,
fused=True):
net = slim.convolution2d(inputs, round(32 * width_multiplier), [3, 3], stride=2, padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net, 64, width_multiplier, sc='conv_ds_2')
net = _depthwise_separable_conv(net, 128, width_multiplier, downsample=True, sc='conv_ds_3')
net = _depthwise_separable_conv(net, 128, width_multiplier, sc='conv_ds_4')
net = _depthwise_separable_conv(net, 256, width_multiplier, downsample=True, sc='conv_ds_5')
net = _depthwise_separable_conv(net, 256, width_multiplier, sc='conv_ds_6')
net = _depthwise_separable_conv(net, 512, width_multiplier, downsample=True, sc='conv_ds_7')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_8')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_9')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_10')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_11')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_12')
net = _depthwise_separable_conv(net, 1024, width_multiplier, downsample=True, sc='conv_ds_13')
net = _depthwise_separable_conv(net, 1024, width_multiplier, sc='conv_ds_14')
net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points['squeeze'] = net
logits = slim.fully_connected(net, num_classes, activation_fn=None, scope='fc_16')
predictions = slim.softmax(logits, scope='Predictions')
end_points['Logits'] = logits
end_points['Predictions'] = predictions
return logits, end_points
def pfld_inference(input, weight_decay, batch_norm_params):
coefficient = 1
with tf.variable_scope('pfld_inference'):
features = {}
with slim.arg_scope([slim.convolution2d, slim.separable_conv2d], \
activation_fn=tf.nn.relu6,\
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=tf.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
padding='SAME'):
print('PFLD input shape({}): {}'.format(input.name,
input.get_shape()))
# 112*112*3
conv1 = slim.convolution2d(input,
64 * coefficient, [3, 3],
stride=2,
scope='conv_1')
print(conv1.name, conv1.get_shape())
# 56*56*64
conv2 = slim.separable_convolution2d(conv1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv2/dwise')
print(conv2.name, conv2.get_shape())
# 56*56*64
conv3_1 = slim.convolution2d(conv2,
128, [1, 1],
stride=2,
scope='conv3_1/expand')
print(conv3_1.name, conv3_1.get_shape())
conv3_1 = slim.separable_convolution2d(conv3_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_1/dwise')
print(conv3_1.name, conv3_1.get_shape())
conv3_1 = slim.convolution2d(conv3_1,
64 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_1/linear')
print(conv3_1.name, conv3_1.get_shape())
conv3_2 = slim.convolution2d(conv3_1,
128, [1, 1],
stride=1,
scope='conv3_2/expand')
print(conv3_2.name, conv3_2.get_shape())
conv3_2 = slim.separable_convolution2d(conv3_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_2/dwise')
print(conv3_2.name, conv3_2.get_shape())
conv3_2 = slim.convolution2d(conv3_2,
64 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_2/linear')
print(conv3_2.name, conv3_2.get_shape())
block3_2 = conv3_1 + conv3_2
print(block3_2.name, block3_2.get_shape())
conv3_3 = slim.convolution2d(block3_2,
128, [1, 1],
stride=1,
scope='conv3_3/expand')
print(conv3_3.name, conv3_3.get_shape())
conv3_3 = slim.separable_convolution2d(conv3_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_3/dwise')
print(conv3_3.name, conv3_3.get_shape())
conv3_3 = slim.convolution2d(conv3_3,
64 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_3linear')
print(conv3_3.name, conv3_3.get_shape())
block3_3 = block3_2 + conv3_3
print(block3_3.name, block3_3.get_shape())
conv3_4 = slim.convolution2d(block3_3,
128, [1, 1],
stride=1,
scope='conv3_4/expand')
print(conv3_4.name, conv3_4.get_shape())
conv3_4 = slim.separable_convolution2d(conv3_4,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_4/dwise')
print(conv3_4.name, conv3_4.get_shape())
conv3_4 = slim.convolution2d(conv3_4,
64 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_4/linear')
print(conv3_4.name, conv3_4.get_shape())
block3_4 = block3_3 + conv3_4
print(block3_4.name, block3_4.get_shape())
conv3_5 = slim.convolution2d(block3_4,
128, [1, 1],
stride=1,
scope='conv3_5/expand')
print(conv3_5.name, conv3_5.get_shape())
conv3_5 = slim.separable_convolution2d(conv3_5,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_5/dwise')
print(conv3_5.name, conv3_5.get_shape())
conv3_5 = slim.convolution2d(conv3_5,
64 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_5/linear')
print(conv3_5.name, conv3_5.get_shape())
block3_5 = block3_4 + conv3_5
print(block3_5.name, block3_5.get_shape())
features['auxiliary_input'] = block3_5
#28*28*64
conv4_1 = slim.convolution2d(block3_5,
128, [1, 1],
stride=2,
scope='conv4_1/expand')
print(conv4_1.name, conv4_1.get_shape())
conv4_1 = slim.separable_convolution2d(conv4_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv4_1/dwise')
print(conv4_1.name, conv4_1.get_shape())
conv4_1 = slim.convolution2d(conv4_1,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv4_1/linear')
print(conv4_1.name, conv4_1.get_shape())
#14*14*128
conv5_1 = slim.convolution2d(conv4_1,
512, [1, 1],
stride=1,
scope='conv5_1/expand')
print(conv5_1.name, conv5_1.get_shape())
conv5_1 = slim.separable_convolution2d(conv5_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_1/dwise')
print(conv5_1.name, conv5_1.get_shape())
conv5_1 = slim.convolution2d(conv5_1,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_1/linear')
print(conv5_1.name, conv5_1.get_shape())
conv5_2 = slim.convolution2d(conv5_1,
512, [1, 1],
stride=1,
scope='conv5_2/expand')
print(conv5_2.name, conv5_2.get_shape())
conv5_2 = slim.separable_convolution2d(conv5_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_2/dwise')
print(conv5_2.name, conv5_2.get_shape())
conv5_2 = slim.convolution2d(conv5_2,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_2/linear')
print(conv5_2.name, conv5_2.get_shape())
block5_2 = conv5_1 + conv5_2
print(block5_2.name, block5_2.get_shape())
conv5_3 = slim.convolution2d(block5_2,
512, [1, 1],
stride=1,
scope='conv5_3/expand')
print(conv5_3.name, conv5_3.get_shape())
conv5_3 = slim.separable_convolution2d(conv5_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_3/dwise')
print(conv5_3.name, conv5_3.get_shape())
conv5_3 = slim.convolution2d(conv5_3,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_3/linear')
print(conv5_3.name, conv5_3.get_shape())
block5_3 = block5_2 + conv5_3
print(block5_3.name, block5_3.get_shape())
conv5_4 = slim.convolution2d(block5_3,
512, [1, 1],
stride=1,
scope='conv5_4/expand')
print(conv5_4.name, conv5_4.get_shape())
conv5_4 = slim.separable_convolution2d(conv5_4,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_4/dwise')
print(conv5_4.name, conv5_4.get_shape())
conv5_4 = slim.convolution2d(conv5_4,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_4/linear')
print(conv5_4.name, conv5_4.get_shape())
block5_4 = block5_3 + conv5_4
print(block5_4.name, block5_4.get_shape())
conv5_5 = slim.convolution2d(block5_4,
512, [1, 1],
stride=1,
scope='conv5_5/expand')
print(conv5_5.name, conv5_5.get_shape())
conv5_5 = slim.separable_convolution2d(conv5_5,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_5/dwise')
print(conv5_5.name, conv5_5.get_shape())
conv5_5 = slim.convolution2d(conv5_5,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_5/linear')
print(conv5_5.name, conv5_5.get_shape())
block5_5 = block5_4 + conv5_5
print(block5_5.name, block5_5.get_shape())
conv5_6 = slim.convolution2d(block5_5,
512, [1, 1],
stride=1,
scope='conv5_6/expand')
print(conv5_6.name, conv5_6.get_shape())
conv5_6 = slim.separable_convolution2d(conv5_6,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_6/dwise')
print(conv5_6.name, conv5_6.get_shape())
conv5_6 = slim.convolution2d(conv5_6,
128 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_6/linear')
print(conv5_6.name, conv5_6.get_shape())
block5_6 = block5_5 + conv5_6
print(block5_6.name, block5_6.get_shape())
#14*14*128
conv6_1 = slim.convolution2d(block5_6,
256, [1, 1],
stride=1,
scope='conv6_1/expand')
print(conv6_1.name, conv6_1.get_shape())
conv6_1 = slim.separable_convolution2d(conv6_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv6_1/dwise')
print(conv6_1.name, conv6_1.get_shape())
conv6_1 = slim.convolution2d(conv6_1,
16 * coefficient, [1, 1],
stride=1,
activation_fn=None,
scope='conv6_1/linear')
print(conv6_1.name, conv6_1.get_shape())
#14*14*16
conv7 = slim.convolution2d(conv6_1,
32 * coefficient, [3, 3],
stride=2,
activation_fn=None,
scope='conv7')
print(conv7.name, conv7.get_shape())
#7*7*32
conv8 = slim.convolution2d(conv7,
128 * coefficient, [7, 7],
stride=1,
padding='VALID',
activation_fn=None,
scope='conv8')
print(conv8.name, conv8.get_shape())
avg_pool1 = slim.avg_pool2d(
conv6_1, [conv6_1.get_shape()[1],
conv6_1.get_shape()[2]],
stride=1)
print(avg_pool1.name, avg_pool1.get_shape())
avg_pool2 = slim.avg_pool2d(
conv7, [conv7.get_shape()[1],
conv7.get_shape()[2]], stride=1)
print(avg_pool2.name, avg_pool2.get_shape())
#s1 = slim.flatten(avg_pool1)
#s2 = slim.flatten(avg_pool2)
s1 = slim.flatten(conv6_1)
s2 = slim.flatten(conv7)
#1*1*128
s3 = slim.flatten(conv8)
multi_scale = tf.concat([s1, s2, s3], 1)
landmarks = slim.fully_connected(multi_scale,
num_outputs=196,
activation_fn=None,
scope='fc')
return features, landmarks
def mobilenet(name, inputs, num_class=1000, width_mult=1.0, train=False):
block = depthwise_seperable_block
with tf.variable_scope(name):
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer()):
with slim.arg_scope([slim.batch_norm],
is_training=train,
activation_fn=tf.nn.relu,
fused=True):
net = slim.convolution2d(inputs,
round(32 * width_mult), [3, 3],
stride=2,
padding="SAME",
scope="first_conv3x3")
net = slim.batch_norm(net, scope="first_batch_norm")
net = block("depthwise_seperable_1", net,
round(64 * width_mult))
net = block("depthwise_seperable_2",
net,
round(128 * width_mult),
downsample=True)
net = block("depthwise_seperable_3", net,
round(128 * width_mult))
net = block("depthwise_seperable_4",
net,
round(256 * width_mult),
downsample=True)
net = block("depthwise_seperable_5", net,
round(256 * width_mult))
net = block("depthwise_seperable_6",
net,
round(512 * width_mult),
downsample=True)
net = block("depthwise_seperable_7", net,
round(512 * width_mult))
net = block("depthwise_seperable_8", net,
round(512 * width_mult))
net = block("depthwise_seperable_9", net,
round(512 * width_mult))
net = block("depthwise_seperable_10", net,
round(512 * width_mult))
net = block("depthwise_seperable_11", net,
round(512 * width_mult))
net = block("depthwise_seperable_12",
net,
round(1024 * width_mult),
downsample=True)
net = block("depthwise_seperable_13", net,
round(1024 * width_mult))
net = slim.avg_pool2d(net, [7, 7], scope="avg_pool2d")
net = tf.squeeze(net, [1, 2], name="squeeze")
logits = slim.fully_connected(
net,
num_class,
activation_fn=None,
scope="fc",
weights_initializer=slim.initializers.xavier_initializer())
predictions = slim.softmax(logits, scope="softmax")
return predictions
def inference(self,
preprocessed_inputs,
width_multiplier=1,
scope="MobileNetV1"):
with slim.arg_scope(self.mobilenet_arg_scope()):
with tf.variable_scope(scope) as sc:
#在每一层卷积后不使用激活函数
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None):
#仅仅在归一化层后使用激活函数-ReLU
with slim.arg_scope([slim.batch_norm],
is_training=self.is_training,
activation_fn=tf.nn.relu,
fused=True,
decay=0.95): #fused:是否使用一种更快的融合方法
net = slim.convolution2d(preprocessed_inputs,
round(32 * width_multiplier),
[3, 3],
stride=2,
padding="SAME",
scope="conv_1")
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = self.depthwise_separable_conv(
net, 64, width_multiplier,
name='conv_ds_2') # 进行深度可卷积-depthwise和pointwise都执行
net = self.depthwise_separable_conv(net,
128,
width_multiplier,
downsampling=True,
name='conv_3')
net = self.depthwise_separable_conv(net,
128,
width_multiplier,
name='conv_ds_4')
net = self.depthwise_separable_conv(net,
256,
width_multiplier,
downsampling=True,
name='conv_5')
# net = self.depthwise_separable_conv(net, 256, width_multiplier, name='conv_ds_6')
# net = self.depthwise_separable_conv(net, 512, width_multiplier, downsampling=True, name='conv_7')
net = self.depthwise_separable_conv(net,
512,
width_multiplier,
name='conv_8')
net = self.depthwise_separable_conv(net,
512,
width_multiplier,
name='conv_9')
net = self.depthwise_separable_conv(net,
512,
width_multiplier,
name='conv_10')
net = self.depthwise_separable_conv(net,
512,
width_multiplier,
name='conv_11')
net = self.depthwise_separable_conv(net,
512,
width_multiplier,
name='conv_12')
# net = self.depthwise_separable_conv(net, 1024, width_multiplier, downsampling=True, name='conv_13')
# net = self.depthwise_separable_conv(net, 1024, width_multiplier, name='conv_ds_14')
net = slim.avg_pool2d(net, [2, 2], scope='avg_pool_15')
shape = net.get_shape().as_list()
flat_height, flat_width, flat_channals = shape[1:]
flat_size = flat_height * flat_width * flat_channals
net = tf.reshape(net, shape=[-1, flat_size])
net = slim.fully_connected(net,
self.num_classes,
activation_fn=None,
scope='fc_16')
return net
def __init__(self,
h_size,
action_size,
img_size=84,
learning_rate=0.00025):
self.frame_in = tf.placeholder(tf.float32,
[None, img_size * img_size * 3],
name="frame_in")
img_in = tf.reshape(self.frame_in, [-1, img_size, img_size, 3])
conv1 = slim.convolution2d(scope="conv1",
inputs=img_in,
num_outputs=32,
kernel_size=[8, 8],
stride=[4, 4],
padding="VALID",
biases_initializer=None)
conv2 = slim.convolution2d(scope="conv2",
inputs=conv1,
num_outputs=64,
kernel_size=[4, 4],
stride=[2, 2],
padding="VALID",
biases_initializer=None)
conv3 = slim.convolution2d(scope="conv3",
inputs=conv2,
num_outputs=64,
kernel_size=[3, 3],
stride=[1, 1],
padding="VALID",
biases_initializer=None)
conv4 = slim.convolution2d(scope="conv4",
inputs=conv3,
num_outputs=h_size,
kernel_size=[7, 7],
stride=[1, 1],
padding="VALID",
biases_initializer=None)
self.train_len = tf.placeholder(tf.int32, [])
self.batch_size = tf.placeholder(tf.int32, [])
conv_flat = tf.reshape(slim.flatten(conv4),
[self.batch_size, self.train_len, h_size])
cell = tf.nn.rnn_cell.BasicLSTMCell(h_size,
state_is_tuple=True,
reuse=False)
self.state_init = cell.zero_state(self.batch_size, tf.float32)
rnn, self.rnn_state = tf.nn.dynamic_rnn(cell,
conv_flat,
dtype=tf.float32,
initial_state=self.state_init)
#print(rnn)
#print(self.rnn_state)
rnn = tf.reshape(rnn, [-1, h_size])
with tf.variable_scope("va_split"):
stream_a, stream_v = tf.split(rnn, 2, axis=1)
w_a = tf.Variable(tf.random_normal([h_size // 2, action_size]))
w_v = tf.Variable(tf.random_normal([h_size // 2, 1]))
advantage = tf.matmul(stream_a, w_a)
value = tf.matmul(stream_v, w_v)
# salience = tf.gradients(advantage, img_in)
with tf.variable_scope("predict"):
self.q_out = value + tf.subtract(
advantage, tf.reduce_mean(advantage, 1, keep_dims=True))
self.pred = tf.argmax(self.q_out, axis=1)
self.target_q = tf.placeholder(tf.float32, [None])
self.actions = tf.placeholder(tf.int32, [None])
actions_onehot = tf.one_hot(self.actions,
action_size,
dtype=tf.float32)
Q = tf.reduce_sum(tf.multiply(self.q_out, actions_onehot), axis=1)
td_error = tf.square(self.target_q - Q)
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat([self.maskA, self.maskB], 1)
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
loss = tf.reduce_mean(td_error)
#self.update = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(loss)
self.update = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(loss)
with tf.name_scope("summary"):
tf.summary.scalar("loss", loss)
tf.summary.scalar("mean_value", tf.reduce_mean(value))
tf.summary.scalar("max_advantage", tf.reduce_max(advantage))
tf.summary.scalar("mean_target_q", tf.reduce_mean(self.target_q))
tf.summary.scalar("mean_pred_q", tf.reduce_mean(self.q_out))
self.summary_op = tf.summary.merge_all()
def __init__(self, h_size, rnn_cell, myScope):
#The network recieves a frame from the game, flattened into an array.
#It then resizes it and processes it through four convolutional layers.
self.scalarInput = tf.placeholder(shape=[None, 21168],
dtype=tf.float32)
self.imageIn = tf.reshape(self.scalarInput, shape=[-1, 84, 84, 3])
self.conv1 = slim.convolution2d( \
inputs=self.imageIn,num_outputs=32,\
kernel_size=[8,8],stride=[4,4],padding='VALID', \
biases_initializer=None,scope=myScope+'_conv1')
self.conv2 = slim.convolution2d( \
inputs=self.conv1,num_outputs=64,\
kernel_size=[4,4],stride=[2,2],padding='VALID', \
biases_initializer=None,scope=myScope+'_conv2')
self.conv3 = slim.convolution2d( \
inputs=self.conv2,num_outputs=64,\
kernel_size=[3,3],stride=[1,1],padding='VALID', \
biases_initializer=None,scope=myScope+'_conv3')
self.conv4 = slim.convolution2d( \
inputs=self.conv3,num_outputs=h_size,\
kernel_size=[7,7],stride=[1,1],padding='VALID', \
biases_initializer=None,scope=myScope+'_conv4')
self.trainLength = tf.placeholder(dtype=tf.int32)
# We take the output from the final convolutional layer and send it to a
# recurrent layer. The input must be reshaped into [batch x trace x units] for
# rnn processing, and then returned to [batch x units] when sent through the
# upper levels.
self.batch_size = tf.placeholder(dtype=tf.int32)
self.convFlat = tf.reshape(slim.flatten(self.conv4),
[self.batch_size, self.trainLength, h_size])
self.state_in = cell.zero_state(self.batch_size, tf.float32)
self.rnn, self.rnn_state = tf.nn.dynamic_rnn(\
inputs=self.convFlat, cell=rnn_cell, dtype=tf.float32, initial_state=self.state_in, scope=myScope+"_rnn")
self.rnn = tf.reshape(self.rnn, shape=[-1, h_size])
# The output from the recurrent player is then split into separate Value and Advantage terms
self.streamA, self.streamV = tf.split(1, 2, self.rnn)
self.AW = tf.Variable(tf.random_normal([h_size // 2, 4]))
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
self.Advantage = tf.matmul(self.streamA, self.AW)
self.Value = tf.matmul(self.streamV, self.VW)
self.salience = tf.gradients(self.Advantage, self.imageIn)
# Then combine them together to get our final Q-Values.
self.Qout = self.Value + tf.subtract(
self.Advantage,
tf.reduce_mean(self.Advantage, axis=1, keep_dims=True))
self.predict = tf.argmax(self.Qout, 1)
# Bellow we obtain the loss by taking the sum of squared differences between the target
# and prediction Q values.
self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, 4, dtype=tf.float32)
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot),
axis=1)
self.td_error = tf.square(self.targetQ - self.Q)
# In order to only propagate accurate gradients through the network, we will mask the
# first half of the losses for each trace as per Lample & Chatlot 2016
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat(1, [self.maskA, self.maskB])
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate=0.0001)
self.updateModel = self.trainer.minimize(self.loss)
def init_mobilenet_v1(self, param):
resolution_multiplier = param['resolution_multiplier']
width_multiplier = param['width_multiplier']
depth_multiplier = param['depth_multiplier']
if 'input_dim' in param:
input_dim = param['input_dim']
else:
H_W = int(224 * resolution_multiplier)
input_dim = [1, H_W, H_W, 3]
if 'output_dim' in param:
out_dim = param['output_dim']
else:
out_dim = 1000
# Define the resolution based on resolution multiplier
# [1, 0.858, 0.715, 0.572 ] = [224, 192, 160, 128]
input = tf.placeholder(tf.float32, input_dim, name='input_tensor')
layer_1_conv = slim.convolution2d(input,
round(32 * width_multiplier), [3, 3],
stride=2,
padding='SAME',
scope='conv_1')
#layer_1_bn = slim.batch_norm(layer_1_conv, scope='conv_1/batch_norm')
layer_2_dw = self.dw_separable(layer_1_conv,
64,
width_multiplier,
depth_multiplier,
sc='conv_ds_2')
layer_3_dw = self.dw_separable(layer_2_dw,
128,
width_multiplier,
depth_multiplier,
downsample=True,
sc='conv_ds_3')
layer_4_dw = self.dw_separable(layer_3_dw,
128,
width_multiplier,
depth_multiplier,
sc='conv_ds_4')
layer_5_dw = self.dw_separable(layer_4_dw,
256,
width_multiplier,
depth_multiplier,
downsample=True,
sc='conv_ds_5')
layer_6_dw = self.dw_separable(layer_5_dw,
256,
width_multiplier,
depth_multiplier,
sc='conv_ds_6')
layer_7_dw = self.dw_separable(layer_6_dw,
512,
width_multiplier,
depth_multiplier,
downsample=True,
sc='conv_ds_7')
# repeatable layers can be put inside a loop
layer_8_12_dw = layer_7_dw
for i in range(8, 13):
layer_8_12_dw = self.dw_separable(layer_8_12_dw,
512,
width_multiplier,
depth_multiplier,
sc='conv_ds_' + str(i))
layer_13_dw = self.dw_separable(layer_8_12_dw,
1024,
width_multiplier,
depth_multiplier,
downsample=True,
sc='conv_ds_13')
layer_14_dw = self.dw_separable(layer_13_dw,
1024,
width_multiplier,
depth_multiplier,
sc='conv_ds_14')
# Pool and reduce to output dimension
global_pool = tf.reduce_mean(layer_14_dw, [1, 2],
keep_dims=True,
name='global_pool')
spatial_reduction = tf.squeeze(global_pool, [1, 2],
name='SpatialSqueeze')
logits = slim.fully_connected(spatial_reduction,
out_dim,
activation_fn=None,
scope='fc_16')
output = slim.softmax(logits, scope='Predictions')
output = tf.identity(output, name="output_tensor")
return {'input': input, 'output': output, 'logits': logits}
def mobilenet(inputs,
num_classes=1000,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
'''
Args:
inputs: a tensor of size [batch_size,height,width,channels]
num_classes: number of predicted classes
is_training: model is being trained
scope: scope for the variables
Returns:
logits
end_points
'''
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
#skip pointwise by setting num_output=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc +
'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc + '/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc + '/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc + '/pw_batch_norm')
return bn
with tf.Variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu,
fused=True):
net = slim.convolution2d(inputs,
round(32 * width_multiplier), [3, 3],
stride=2,
padding='same',
scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net,
64,
width_multiplier,
sc='conv_ds_2')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
downsample=True,
sc='conv_ds_3')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
sc='conv_ds_4')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
downsample=True,
sc='conv_ds_5')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
sc='conv_ds_6')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
downsample=True,
sc='conv_ds_7')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_8')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_9')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_10')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_11')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_12')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
downsample=True,
sc='conv_ds_13')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
sc='conv_ds_14')
net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
logits = slim.fully_connected(net,
num_classes,
activation_fn=None,
scope='fc_16')
predictions = slim.softmax(logits, scope='predictions')
end_points['Logits'] = logits
end_points['Predictions'] = predictions
return logits, end_points
def conv_bn_relu(input, out_channel, kernel_size, stride=1, dilation=1):
with tf.variable_scope(None, 'conv_bn_relu'):
input = slim.convolution2d(input, out_channel, kernel_size, stride, rate=dilation, activation_fn=None)
input = slim.batch_norm(input, activation_fn=tf.nn.relu, fused=False)
return input
def __init__(self,
h_size,
action_size,
img_size=84,
learning_rate=0.00025,
frame_count=4):
self.frame_in = tf.placeholder(
tf.float32, [None, img_size * img_size * frame_count],
name="frame_in")
img_in = tf.reshape(self.frame_in,
[-1, img_size, img_size, frame_count])
conv1 = slim.convolution2d(scope="conv1",
inputs=img_in,
num_outputs=32,
kernel_size=[8, 8],
stride=[4, 4],
padding="VALID",
biases_initializer=None)
conv2 = slim.convolution2d(scope="conv2",
inputs=conv1,
num_outputs=64,
kernel_size=[4, 4],
stride=[2, 2],
padding="VALID",
biases_initializer=None)
conv3 = slim.convolution2d(scope="conv3",
inputs=conv2,
num_outputs=64,
kernel_size=[3, 3],
stride=[1, 1],
padding="VALID",
biases_initializer=None)
conv4 = slim.convolution2d(scope="conv4",
inputs=conv3,
num_outputs=h_size,
kernel_size=[7, 7],
stride=[1, 1],
padding="VALID",
biases_initializer=None)
self.batch_size = tf.placeholder(tf.int32, [])
conv_flat = tf.reshape(slim.flatten(conv4), [self.batch_size, h_size])
with tf.variable_scope("va_split"):
stream_a, stream_v = tf.split(conv_flat, 2, axis=1)
w_a = tf.Variable(tf.random_normal([h_size // 2, action_size]))
w_v = tf.Variable(tf.random_normal([h_size // 2, 1]))
advantage = tf.matmul(stream_a, w_a)
value = tf.matmul(stream_v, w_v)
# salience = tf.gradients(advantage, img_in)
with tf.variable_scope("predict"):
self.q_out = value + tf.subtract(
advantage, tf.reduce_mean(advantage, axis=1, keep_dims=True))
self.pred = tf.argmax(self.q_out, axis=1)
self.target_q = tf.placeholder(tf.float32, [None])
self.actions = tf.placeholder(tf.int32, [None])
actions_onehot = tf.one_hot(self.actions,
action_size,
dtype=tf.float32)
Q = tf.reduce_sum(tf.multiply(self.q_out, actions_onehot), axis=1)
td_error = tf.square(self.target_q - Q)
loss = tf.reduce_mean(td_error)
self.update = tf.train.RMSPropOptimizer(
learning_rate=learning_rate).minimize(loss)
with tf.name_scope("summary"):
tf.summary.scalar("loss", loss)
tf.summary.scalar("mean_value", tf.reduce_mean(value))
tf.summary.scalar("max_advantage", tf.reduce_max(advantage))
tf.summary.scalar("min_advantage", tf.reduce_min(advantage))
tf.summary.scalar("mean_target_q", tf.reduce_mean(self.target_q))
tf.summary.scalar("mean_pred_q", tf.reduce_mean(self.q_out))
self.summary_op = tf.summary.merge_all()
def inference(images,
keep_probability,
phase_train=True,
bottleneck_layer_size=128,
weight_decay=0.0,
reuse=None):
batch_norm_params = {
# Decay for the moving averages.
'decay': 0.995,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
}
width_multiplier = 0.25
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
weights_initializer=slim.xavier_initializer_conv2d(uniform=True),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with tf.variable_scope('mobilenet', [images], reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=phase_train):
net = slim.convolution2d(images,
round(32 * width_multiplier), [3, 3],
stride=2,
padding='SAME',
scope='conv_1')
net = _depthwise_separable_conv(net,
64,
width_multiplier,
sc='conv_ds_2')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
downsample=True,
sc='conv_ds_3')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
sc='conv_ds_4')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
downsample=True,
sc='conv_ds_5')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
sc='conv_ds_6')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
downsample=True,
sc='conv_ds_7')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_8')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_9')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_10')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_11')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_12')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
downsample=True,
sc='conv_ds_13')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
sc='conv_ds_14')
# net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
net = tf.reduce_mean(net, [1, 2],
name='avg_pool_15',
keep_dims=True)
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
net = slim.fully_connected(net,
bottleneck_layer_size,
activation_fn=None,
scope='fc_16')
sess = K.get_session()
graph = sess.graph
stats_graph(graph)
return net, None
def mobilenet(inputs,
net_id,
emb_size=128,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
""" MobileNet
More detail, please refer to Google's paper(https://arxiv.org/abs/1704.04861).
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
scope: Optional scope for the variables.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, `num_classes`]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc +
'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc + '/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc + '/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc + '/pw_batch_norm')
return bn
inputs = tf.image.resize_images(inputs, [224, 224])
mean = tf.constant([123.68, 116.779, 103.939],
dtype=tf.float32,
shape=[1, 1, 1, 3],
name='img_mean')
inputs = inputs - mean
print('---------------mobilenet--------------------')
with tf.variable_scope('net_' + str(net_id), reuse=is_training) as sc:
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(0.00004)):
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu,
fused=True):
net = slim.convolution2d(inputs,
round(32 * width_multiplier),
[3, 3],
stride=2,
padding='SAME',
scope='conv_1')
print(net)
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net,
64,
width_multiplier,
sc='conv_ds_2')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
downsample=True,
sc='conv_ds_3')
print(net)
net = _depthwise_separable_conv(net,
128,
width_multiplier,
sc='conv_ds_4')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
downsample=True,
sc='conv_ds_5')
print(net)
net = _depthwise_separable_conv(net,
256,
width_multiplier,
sc='conv_ds_6')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
downsample=True,
sc='conv_ds_7')
print(net)
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_8')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_9')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_10')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_11')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_12')
print(net)
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
downsample=True,
sc='conv_ds_13')
print(net)
################################## conv clm ###################################
clm_emb = net
#clm_emb = tf.pad(clm_emb, [[0, 0], [1, 0], [1, 0], [0, 0]])
clm_emb = clm.clm_enc(clm_emb,
net_id,
128,
stride=1,
padding='SAME',
is_training=is_training)
clm_emb, shared_emb = clm.clm_shared(clm_emb,
128,
padding='SAME',
is_training=is_training)
clm_emb = clm.clm_dec(clm_emb,
net_id,
1024,
stride=1,
padding='SAME',
is_training=is_training)
net = clm_emb + net
with tf.variable_scope('net_' + str(net_id), reuse=is_training) as sc:
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(0.00004)):
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu,
fused=True):
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
sc='conv_ds_14')
net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
print(net)
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points['squeeze'] = net
print(net)
emb = slim.fully_connected(net,
emb_size,
activation_fn=None,
scope='fc_16')
print(emb)
end_points['Logits'] = emb
#predictions = slim.softmax(logits, scope='Predictions')
#end_points['Predictions'] = predictions
return emb, shared_emb, None
def discriminator(bottom, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
initializer = tf.truncated_normal_initializer(stddev=0.02)
dis1 = slim.convolution2d(bottom,
64, [5, 5],
padding="SAME",
stride=2,
activation_fn=lrelu,
reuse=reuse,
scope='d_conv1',
weights_initializer=initializer)
# dis1 = tf.space_to_depth(dis1, 2)
dis2 = slim.convolution2d(dis1,
128, [5, 5],
padding="SAME",
stride=2,
normalizer_fn=slim.batch_norm,
activation_fn=lrelu,
reuse=reuse,
scope='d_conv2',
weights_initializer=initializer)
# dis2 = tf.space_to_depth(dis2, 2)
dis3 = slim.convolution2d(dis2,
256, [5, 5],
padding="SAME",
stride=2,
normalizer_fn=slim.batch_norm,
activation_fn=lrelu,
reuse=reuse,
scope='d_conv3',
weights_initializer=initializer)
# dis3 = tf.space_to_depth(dis3, 2)
dis4 = slim.convolution2d(dis3,
512, [5, 5],
padding="SAME",
stride=2,
normalizer_fn=slim.batch_norm,
activation_fn=lrelu,
reuse=reuse,
scope='d_conv4',
weights_initializer=initializer)
'''
dis4 = slim.fully_connected(slim.flatten(dis4), 1024, activation_fn=lrelu,
reuse=reuse, scope='d_fc1', weights_initializer=initializer)
'''
d_out = slim.fully_connected(slim.flatten(dis4),
1,
activation_fn=tf.nn.sigmoid,
reuse=reuse,
scope='d_out',
weights_initializer=initializer)
q_a = slim.fully_connected(slim.flatten(dis4),
128,
normalizer_fn=slim.batch_norm,
reuse=reuse,
scope='q_a',
weights_initializer=initializer)
# Here we define the unique layers used for the q-network. The number of outputs depends on the number of
# latent variables we choose to define.
q_cont_outs1 = slim.fully_connected(q_a,
2,
activation_fn=tf.nn.tanh,
reuse=reuse,
scope='q_out_cont1',
weights_initializer=initializer)
# q_cont_outs2 = slim.fully_connected(q_a, 2, activation_fn=tf.nn.tanh, reuse=reuse, scope='q_out_cont2', weights_initializer=initializer)
return d_out, q_cont_outs1
def O_Net_new1(inputs,
label=None,
bbox_target=None,
landmark_target=None,
training=True):
#define common param
#is_training=training
#dropout_keep_prob=0.8
#bottleneck_layer_size=1000,
width_multiplier = 1
weight_decay = 0.0005
#reuse = None,
batch_norm_params = {
# Decay for the moving averages.
'decay': 0.995,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
#scale
#'scale': True,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
}
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc +
'/depthwise_conv')
#padding='VALID')
#print(depthwise_conv.get_shape())
#bn = slim.batch_norm(depthwise_conv, scope=sc + '/dw_batch_norm')
pointwise_conv = slim.convolution2d(depthwise_conv,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc + '/pointwise_conv',
padding='SAME')
#print(pointwise_conv.get_shape())
#bn = slim.batch_norm(pointwise_conv, scope=sc + '/pw_batch_norm')
return pointwise_conv
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.xavier_initializer_conv2d(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(
weight_decay), #l1_regularizer?
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
#activation_fn = prelu,
#outputs_collections=[end_points_collection],
):
#padding = 'VALID'):
with slim.arg_scope(
[slim.batch_norm],
is_training=training,
):
print(inputs.get_shape())
net = slim.convolution2d(inputs,
round(16 * width_multiplier), [3, 3],
stride=1,
padding='SAME',
scope='conv_1')
#net = _depthwise_separable_conv(inputs, 32, width_multiplier, sc='conv_ds_1')
print(net.get_shape())
net = _depthwise_separable_conv(net,
32,
width_multiplier,
downsample=True,
sc='conv_ds_2')
print(net.get_shape())
net = _depthwise_separable_conv(net,
64,
width_multiplier,
downsample=True,
sc='conv_ds_3')
print(net.get_shape())
net = _depthwise_separable_conv(net,
64,
width_multiplier,
downsample=True,
sc='conv_ds_4')
print(net.get_shape())
net = _depthwise_separable_conv(net,
128,
width_multiplier,
downsample=True,
sc='conv_ds_5')
print(net.get_shape())
#net = _depthwise_separable_conv(net, 64, width_multiplier, sc='conv_ds_4')
#print(net.get_shape())
net = slim.avg_pool2d(net, [3, 3], scope='avg_pool_15')
print(net.get_shape())
#net = _depthwise_separable_conv(net, 256, width_multiplier, downsample=True, sc='conv_ds_6')
#print(net.get_shape())
fc_flatten = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
print(fc_flatten.get_shape())
#fc_flatten = slim.flatten(net)
#print (fc_flatten.get_shape())
fc1 = slim.fully_connected(fc_flatten,
num_outputs=256 * width_multiplier,
scope="fc1")
print(fc1.get_shape())
#cls_prob = slim.conv2d(net,num_outputs=2,kernel_size=[1,1],stride=1,scope='conv4_1',activation_fn=tf.nn.softmax)
cls_prob = slim.fully_connected(fc1,
2,
activation_fn=tf.nn.softmax,
scope='cls_fc')
print(cls_prob.get_shape())
#train
if training:
cls_loss = cls_ohem(cls_prob, label)
accuracy = cal_accuracy(cls_prob, label)
L2_loss = tf.add_n(slim.losses.get_regularization_losses())
return cls_loss, L2_loss, accuracy
else:
return cls_prob #,bbox_pred,landmark_pred
def __init__(self, h_size, rnn_cell, myScope):
self.scalarInput = tf.placeholder(shape=[None, 5808], dtype=tf.float32)
self.goal = tf.placeholder(shape=[None, 5808], dtype=tf.float32)
self.scalarInputReshape = tf.reshape(self.scalarInput,
shape=[-1, 44, 44, 3])
self.goalReshape = tf.reshape(self.goal, shape=[-1, 44, 44, 3])
self.input = tf.concat([self.goalReshape, self.scalarInputReshape], 3)
with tf.variable_scope(myScope):
self.conv1 = slim.convolution2d( \
inputs=self.input, num_outputs=32, \
kernel_size=[3, 3], stride=[4, 4], padding='VALID', \
biases_initializer=None, scope=myScope + '_critic_conv1')
self.conv2 = slim.convolution2d( \
inputs=self.conv1, num_outputs=32, \
kernel_size=[3, 3], stride=[2, 2], padding='VALID', \
biases_initializer=None, scope=myScope + '_critic_conv2')
self.conv3 = slim.convolution2d( \
inputs=self.conv2, num_outputs=32, \
kernel_size=[3, 3], stride=[1, 1], padding='VALID', \
biases_initializer=None, scope=myScope + '_critic_conv3')
self.conv4 = slim.convolution2d( \
inputs=self.conv3, num_outputs=h_size, \
kernel_size=[3, 3], stride=[1, 1], padding='VALID', \
biases_initializer=None, scope=myScope + '_critic_conv4')
self.trainLength = tf.placeholder(dtype=tf.int32)
self.batch_size = tf.placeholder(dtype=tf.int32, shape=[])
self.convFlat = tf.reshape(slim.flatten(
self.conv4), [self.batch_size, self.trainLength, h_size])
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
self.rnn, self.rnn_state = tf.nn.dynamic_rnn( \
inputs=self.convFlat, cell=rnn_cell, dtype=tf.float32, initial_state=self.state_in, scope=myScope + '_rnn')
self.rnn = tf.reshape(self.rnn, shape=[-1, h_size])
# The output from the recurrent player is then split into separate Value and Advantage streams
self.streamA, self.streamV = tf.split(self.rnn, 2, 1)
self.AW = tf.Variable(tf.random_normal([h_size // 2, 4]))
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
self.Advantage = tf.matmul(self.streamA, self.AW)
self.Value = tf.matmul(self.streamV, self.VW)
# Then combine them together to get our final Q-values.
self.Qout = self.Value + tf.subtract(
self.Advantage,
tf.reduce_mean(self.Advantage, axis=1, keep_dims=True))
self.predict = tf.argmax(self.Qout, 1)
# Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, 4, dtype=tf.float32)
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot),
axis=1)
self.td_error = tf.square(self.targetQ - self.Q)
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat([self.maskA, self.maskB], 1)
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate=0.0001)
self.updateModel = self.trainer.minimize(self.loss)
self.param = [
v for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
if myScope in v.name
]
def mobilenet_v3(inputs,
classes_num,
multiplier=1.0,
is_training=True,
type='small'):
end_points = {}
if type == 'small':
layers = [
[16, 16, 3, 2, "RE", True, 16],
[16, 24, 3, 2, "RE", False, 72],
[24, 24, 3, 1, "RE", False, 88],
[24, 40, 5, 2, "RE", True, 96],
[40, 40, 5, 1, "RE", True, 240],
[40, 40, 5, 1, "RE", True, 240],
[40, 48, 5, 1, "HS", True, 120],
[48, 48, 5, 1, "HS", True, 144],
[48, 96, 5, 2, "HS", True, 288],
[96, 96, 5, 1, "HS", True, 576],
[96, 96, 5, 1, "HS", True, 576],
]
else:
layers = [
[16, 16, 3, 1, "RE", False, 16],
[16, 24, 3, 2, "RE", False, 64],
[24, 24, 3, 1, "RE", False, 72],
[24, 40, 5, 2, "RE", True, 72],
[40, 40, 5, 1, "RE", True, 120],
[40, 40, 5, 1, "RE", True, 120],
[40, 80, 3, 2, "HS", False, 240],
[80, 80, 3, 1, "HS", False, 200],
[80, 80, 3, 1, "HS", False, 184],
[80, 80, 3, 1, "HS", False, 184],
[80, 112, 3, 1, "HS", True, 480],
[112, 112, 3, 1, "HS", True, 672],
[112, 160, 5, 1, "HS", True, 672],
[160, 160, 5, 2, "HS", True, 672],
[160, 160, 5, 1, "HS", True, 960],
]
batch_norm_params = {
'decay': 0.999,
'epsilon': 0.001,
'updates_collections': None, #tf.GraphKeys.UPDATE_OPS,
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
'is_training': is_training
}
input_size = inputs.get_shape().as_list()[1:-1]
assert ((input_size[0] % 32 == 0) and (input_size[1] % 32 == 0))
reduction_ratio = 4
x = slim.convolution2d(inputs,
int(16 * multiplier), [3, 3],
stride=2,
activation_fn=hard_swish,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
biases_initializer=None)
with tf.variable_scope("MobilenetV3"):
for idx, (in_channels, out_channels, kernel_size, stride, activatation,
se, exp_size) in enumerate(layers):
in_channels = int(in_channels * multiplier)
out_channels = int(out_channels * multiplier)
exp_size = int(exp_size * multiplier)
x = mobilenet_v3_block(x, [kernel_size, kernel_size],
batch_norm_params,
exp_size,
out_channels,
stride,
"bneck{}".format(idx),
is_training=is_training,
shortcut=(in_channels == out_channels),
activatation=activatation,
ratio=reduction_ratio,
se=se)
end_points["bneck{}".format(idx)] = x
if type == 'small':
conv1_out = int(576 * multiplier)
else:
conv1_out = int(960 * multiplier)
x = slim.convolution2d(x,
conv1_out, [1, 1],
stride=1,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
activation_fn=hard_swish)
if type == 'small':
x = _squeeze_excitation_layer(x,
out_dim=conv1_out,
ratio=reduction_ratio,
is_training=is_training,
reuse=None)
end_points["conv1_out_1x1"] = x
x = slim.avg_pool2d(x, x.get_shape()[1:-1], stride=1)
#x = hard_swish(x)
end_points["global_pool"] = x
with tf.variable_scope('Logits_out'):
conv2_out = int(1280 * multiplier)
x = slim.convolution2d(x,
conv2_out, [1, 1],
stride=1,
activation_fn=hard_swish)
end_points["conv2_out_1x1"] = x
x = slim.convolution2d(x,
classes_num, [1, 1],
stride=1,
activation_fn=None)
logits = tf.layers.flatten(x)
logits = tf.identity(logits, name='output')
end_points["Logits_out"] = logits
return logits, end_points
def inverted_bottleneck(self,
inputs,
up_channel_rate,
channels,
stride,
k_s=3,
dilation=1.0,
scope=""):
with tf.variable_scope("inverted_bottleneck_%s" % scope):
with slim.arg_scope([slim.batch_norm],
decay=0.999,
fused=True,
is_training=self.is4Train):
#stride = 2 if subsample else 1
output = slim.convolution2d(
inputs,
up_channel_rate * inputs.get_shape().as_list()[-1],
stride=1,
kernel_size=[1, 1],
weights_initializer=self.init_xavier,
biases_initializer=self.init_zero,
activation_fn=tf.nn.relu6,
normalizer_fn=slim.batch_norm,
weights_regularizer=None,
scope=scope + '_up_pointwise',
trainable=self.is4Train)
output = slim.separable_convolution2d(
output,
num_outputs=None,
stride=stride,
depth_multiplier=1,
activation_fn=tf.nn.relu6,
kernel_size=k_s,
weights_initializer=self.init_xavier,
weights_regularizer=self.l2_regularizer,
biases_initializer=None,
normalizer_fn=slim.batch_norm,
rate=dilation,
padding="SAME",
scope=scope + '_depthwise',
trainable=self.is4Train)
output = slim.convolution2d(
output,
channels,
stride=1,
kernel_size=[1, 1],
activation_fn=None,
weights_initializer=self.init_xavier,
biases_initializer=self.init_zero,
normalizer_fn=slim.batch_norm,
weights_regularizer=None,
scope=scope + '_pointwise',
trainable=self.is4Train)
if inputs.get_shape().as_list()[1:] == output.get_shape(
).as_list()[1:]:
output = tf.add(inputs, output)
print("")
return output
def create_ds_cnn_model(fingerprint_input, model_settings, model_size_info,
is_training):
"""Builds a model with depthwise separable convolutional neural network
Model definition is based on https://arxiv.org/abs/1704.04861 and
Tensorflow implementation: https://github.com/Zehaos/MobileNet
model_size_info: defines number of layers, followed by the DS-Conv layer
parameters in the order {number of conv features, conv filter height,
width and stride in y,x dir.} for each of the layers.
Note that first layer is always regular convolution, but the remaining
layers are all depthwise separable convolutions.
"""
def ds_cnn_arg_scope(weight_decay=0):
"""Defines the default ds_cnn argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the DS-CNN model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
def _depthwise_separable_conv(inputs, num_pwc_filters, sc, kernel_size,
stride):
""" Helper function to build the depth-wise separable convolution layer.
"""
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=stride,
depth_multiplier=1,
kernel_size=kernel_size,
scope=sc +
'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc + '/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc + '/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc + '/pw_batch_norm')
return bn
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
label_count = model_settings['label_count']
input_frequency_size = model_settings['dct_coefficient_count']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size, 1])
t_dim = input_time_size
f_dim = input_frequency_size
#Extract model dimensions from model_size_info
num_layers = model_size_info[0]
conv_feat = [None] * num_layers
conv_kt = [None] * num_layers
conv_kf = [None] * num_layers
conv_st = [None] * num_layers
conv_sf = [None] * num_layers
i = 1
for layer_no in range(0, num_layers):
conv_feat[layer_no] = model_size_info[i]
i += 1
conv_kt[layer_no] = model_size_info[i]
i += 1
conv_kf[layer_no] = model_size_info[i]
i += 1
conv_st[layer_no] = model_size_info[i]
i += 1
conv_sf[layer_no] = model_size_info[i]
i += 1
scope = 'DS-CNN'
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
decay=0.96,
updates_collections=None,
activation_fn=tf.nn.relu):
for layer_no in range(0, num_layers):
if layer_no == 0:
net = slim.convolution2d(fingerprint_4d, conv_feat[layer_no],\
[conv_kt[layer_no], conv_kf[layer_no]], stride=[conv_st[layer_no], conv_sf[layer_no]], padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
else:
net = _depthwise_separable_conv(net, conv_feat[layer_no], \
kernel_size = [conv_kt[layer_no],conv_kf[layer_no]], \
stride = [conv_st[layer_no],conv_sf[layer_no]], sc='conv_ds_'+str(layer_no))
t_dim = math.ceil(t_dim / float(conv_st[layer_no]))
f_dim = math.ceil(f_dim / float(conv_sf[layer_no]))
net = slim.avg_pool2d(net, [t_dim, f_dim], scope='avg_pool')
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
logits = slim.fully_connected(net,
label_count,
activation_fn=None,
scope='fc1')
if is_training:
return logits, dropout_prob
else:
return logits
def inference(self, inputs, scope='MobileNetV2'):
with slim.arg_scope(self.mobilenet_arg_scope()):
with tf.variable_scope(scope):
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
activation_fn=None):
# 先batch_norm,再ReLU,所以激活函数写在slim.batch_norm的参数空间中
with slim.arg_scope([slim.batch_norm],
is_training=self._is_training,
activation_fn=tf.nn.relu6,
fused=True,
decay=0.90):
#一定要等到batch_norm的均值和方差稳定了,在测试时才能有高精度
#为了使得均值方差快速稳定,可以另batch_norm的decay变小，0.95不够就0.90甚至更小
net = slim.convolution2d(inputs,
num_outputs=32,
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='conv_1')
net = slim.batch_norm(net, scope="b1")
net = self.bootleneck(net, 1, 16, 1, "bottleneck1")
net = self.bootleneck(net, 6, 24, 2, "bottleneck2_1")
net = self.bootleneck(net, 6, 24, 1, "bottleneck2_2")
net = self.bootleneck(net, 6, 32, 2, "bottleneck3_1")
net = self.bootleneck(net, 6, 32, 1, "bottleneck3_2")
net = self.bootleneck(net, 6, 32, 1, "bottleneck3_3")
net = self.bootleneck(net, 6, 64, 2, "bottleneck4_1")
net = self.bootleneck(net, 6, 64, 1, "bottleneck4_2")
net = self.bootleneck(net, 6, 64, 1, "bottleneck4_3")
net = self.bootleneck(net, 6, 64, 1, "bottleneck4_4")
net = self.bootleneck(net, 6, 96, 1, "bottleneck5_1")
net = self.bootleneck(net, 6, 96, 1, "bottleneck5_2")
net = self.bootleneck(net, 6, 96, 1, "bottleneck5_3")
net = self.bootleneck(net, 6, 160, 2, "bottleneck6_1")
net = self.bootleneck(net, 6, 160, 1, "bottleneck6_2")
net = self.bootleneck(net, 6, 160, 1, "bottleneck6_3")
net = self.bootleneck(net, 6, 320, 1, "bottleneck7_1")
net = slim.convolution2d(net,
num_outputs=1280,
kernel_size=[3, 3],
stride=1,
scope='conv_2')
net = slim.batch_norm(net, scope="b2")
net = slim.avg_pool2d(net,
kernel_size=[7, 7],
stride=1)
net = slim.convolution2d(net,
num_outputs=self.num_classes,
kernel_size=[3, 3],
stride=1,
scope='conv_3')
logits = tf.reshape(net, shape=[-1, self.num_classes])
return logits
def __init__(self, h_size, rnn_cell, myScope):
#处理输入数据
self.scalarInput = tf.placeholder(shape=[None, 21168],
dtype=tf.float32)
self.imageIn = tf.reshape(self.scalarInput, shape=[-1, 84, 84, 3])
#第一层卷积
self.conv1 = slim.convolution2d(inputs=self.imageIn,
num_outputs=32,
kernel_size=[8, 8],
stride=[4, 4],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv1')
#第二层卷积
self.conv2 = slim.convolution2d(inputs=self.conv1,
num_outputs=64,
kernel_size=[4, 4],
stride=[2, 2],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv2')
#第三层卷积
self.conv3 = slim.convolution2d(inputs=self.conv2,
num_outputs=64,
kernel_size=[3, 3],
stride=[1, 1],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv3')
#第四层卷积
self.conv4 = slim.convolution2d(inputs=self.conv3,
num_outputs=h_size,
kernel_size=[7, 7],
stride=[1, 1],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv4')
#训练长度，即回溯多少帧数据
self.trainLength = tf.placeholder(dtype=tf.int32)
self.batch_size = tf.placeholder(dtype=tf.int32)
self.convFlat = tf.reshape(slim.flatten(self.conv4),
[self.batch_size, self.trainLength, h_size])
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
self.rnn, self.rnn_state = tf.nn.dynamic_rnn(
inputs=self.convFlat,
cell=rnn_cell,
dtype=tf.float32,
initial_state=self.state_in,
scope=myScope + '_rnn')
#从循环神经网络中输出分成值函数和优势函数
self.streamA, self.streamV = tf.split(self.rnn, 2, 1)
self.AW = tf.Variable(tf.random_normal([h_size // 2, 4]))
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
self.Advantage = tf.matmul(self.streamA, self.AW)
self.Value = tf.matmul(self.streamV, self.VW)
self.salience = tf.gradients(self.Advantage, self.imageIn)
self.Qout = self.Value + tf.subtract(
self.Advantage,
tf.reduce_mean(self.Advantage, axis=1, keep_dims=True))
self.predict = tf.argmax(self.Qout, 1)
#定义损失
self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, 4, dtype=tf.float32)
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot),
axis=1)
self.td_error = tf.square(self.targetQ - self.Q)
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat([self.maskA, self.maskB], 1)
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate=0.0001)
self.updateModel = self.trainer.minimize(self.loss)
def lookup_conv2d(tensor_in,
num_outputs,
kernel_size,
stride,
dict_size,
padding=1,
param_lambda=0.3,
initial_sparsity=None,
activation_fn=None,
biases_initializer=slim.init_ops.zeros_initializer()):
if not initial_sparsity:
initial_sparsity = 0.5
if isinstance(kernel_size, int):
kernel_size = [kernel_size, kernel_size]
if isinstance(stride, int):
stride = [stride, stride]
sparse_th = initial_sparsity / math.sqrt(
kernel_size[0] * kernel_size[1] * dict_size)
stddev = 1. / math.sqrt(kernel_size[0] * kernel_size[1] * dict_size)
padded = tf.pad(tensor_in,
[[0, 0], [padding, padding], [padding, padding], [0, 0]],
"CONSTANT")
pool_conv = slim.convolution2d(inputs=padded,
num_outputs=dict_size,
kernel_size=[1, 1],
stride=1,
padding='SAME',
activation_fn=None,
biases_initializer=None,
scope='pool_conv')
scope = tf.get_default_graph().get_name_scope()
gen_sparse_conv = False
if len(dense_weights.keys()) > 0:
kernel_dense = dense_weights['%s/%s' % (scope, 'kernel_dense')]
density = np.count_nonzero(kernel_dense) / kernel_dense.size
if density < 0.15:
gen_sparse_conv = True
# activation for kernel weight
if gen_sparse_conv:
dense_kernel_shp = dense_weights['%s/%s' % (scope, 'kernel_shape')]
dense_kernel_idx = dense_weights['%s/%s' % (scope, 'kernel')].indices
dense_kernel_val = dense_weights['%s/%s' % (scope, 'kernel')].values
dense_bias = tf.constant(dense_weights['%s/%s' % (scope, 'bias')])
mode = 'custom_op'
if mode == 'tf_op':
# sparse convolution using only tensorflow's operations. -- SLOW!
# im2col - image patche matrix
img2col = tf.extract_image_patches(
pool_conv, [1, kernel_size[0], kernel_size[1], 1],
[1, stride[0], stride[1], 1], [1, 1, 1, 1], 'VALID')
img2col = tf.transpose(img2col, [0, 3, 1, 2])
img2col_shape = img2col.get_shape().as_list()
img2col = tf.reshape(
img2col,
[img2col_shape[1], img2col_shape[2] * img2col_shape[3]])
# sparse kernel & bias
sparse_kernel = tf.SparseTensor(dense_kernel_idx, dense_kernel_val,
dense_kernel_shp)
# multiplication
matmul = tf.sparse_tensor_dense_matmul(sparse_kernel, img2col)
matmul = tf.transpose(matmul)
matmul = tf.reshape(
matmul,
[1, img2col_shape[2], img2col_shape[3], dense_kernel_shp[0]])
# bias & activation
output = tf.nn.bias_add(
matmul, dense_bias) if dense_bias is not None else matmul
output = tf.nn.relu(output)
return output
elif mode == 'custom_op':
conv = sparse_conv2d_m.sparse_conv2d(pool_conv,
dense_kernel_idx,
dense_kernel_val,
dense_shape=dense_kernel_shp,
strides=stride)
output = tf.nn.bias_add(
conv, dense_bias) if dense_bias is not None else conv
output = tf.nn.relu(output)
return output
else:
raise
else:
# dense convolution
align_conv, layer = lookupalign_conv(
inputs=pool_conv,
filters=num_outputs,
kernel_size=kernel_size,
strides=(stride[0], stride[1]),
padding='valid',
param_lambda=param_lambda * sparse_th,
sparse_th=sparse_th,
activation=activation_fn,
kernel_initializer=tf.random_uniform_initializer(
-1 * stddev, stddev),
bias_initializer=biases_initializer,
name='align_conv')
scope = tf.get_default_graph().get_name_scope()
dense_layers[scope] = layer
return align_conv
def __init__(self, h_size, rnn_cell, myScope):
# Network receives a game frame flattened into an array.
# It resizes the frame and processes it through 4 conv layers.
self.scalarInput = tf.placeholder(shape = [None, 21168], dtype = tf.float32)
self.imageIn = tf.reshape(self.scalarInput, shape = [-1, 84, 84, 3])
self.conv1 = slim.convolution2d(inputs = self.imageIn,
num_outputs = 32,
kernel_size = [8, 8],
stride = [4, 4],
padding = 'VALID',
biases_initializer = None,
scope = myScope + '_conv1')
self.conv2 = slim.convolution2d(inputs = self.conv1,
num_outputs = 64,
kernel_size = [4, 4],
stride = [2, 2],
padding = 'VALID',
biases_initializer = None,
scope = myScope + '_conv2')
self.conv3 = slim.convolution2d(inputs = self.conv2,
num_outputs = 64,
kernel_size = [3, 3],
stride = [1, 1],
padding = 'VALID',
biases_initializer = None,
scope = myScope + '_conv3')
self.conv4 = slim.convolution2d(inputs = self.conv3,
num_outputs = h_size,
kernel_size = [7, 7],
stride = [1, 1],
padding = 'VALID',
biases_initializer = None,
scope = myScope + '_conv4')
# We use consecutive frames to understand temporal dependencies. This is the # of consecutive frames.
self.trainLength = tf.placeholder(dtype = tf.int32)
# Final conv layer output is sent to recurrent layer.
# A trace is a sequence of experiences from within an episode.
# We use consecutive frames to understand temporal dependencies.
# Input must be reshaped into [batch x trace x units] for RNN processing,
# and then returned to [batch x units*] when sent through the upper levels.
# Reshaping must be done because that's what the "tf.nn.dynamic_rnn" function accepts.
# I guess we change back so each frame can be evaluated for value and action computation.
self.batch_size = tf.placeholder(dtype = tf.int32, shape = [])
# "flatten" converts "conv4" from (?, 1, 1, h_size) to (?, h_size).
# Reshaping must be done because that's what the "tf.nn.dynamic_rnn" function accepts.
self.convFlat = tf.reshape(slim.flatten(self.conv4),
[self.batch_size, self.trainLength, h_size])
# Return zero-filled state tensor (initial state)
# "state_in" is actually 2 Tensors. I think one is for the output and the other is for the hidden state.
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
# Feed "self.convFlat" into "rnn_cell". "rnn_cell" has initial state "self.state_in".
# "self.rnn" / "self.rnn_state" is RNN output / final state.
self.rnn, self.rnn_state = tf.nn.dynamic_rnn(inputs = self.convFlat,
cell = rnn_cell,
dtype = tf.float32,
initial_state = self.state_in,
scope = myScope + '_rnn')
# I guess we change back so each frame can be evaluated for value and action computation
self.rnn = tf.reshape(self.rnn, shape = [-1, h_size])
# Split recurrent layer output into value and advantage streams
self.streamA, self.streamV = tf.split(self.rnn, 2, 1)
self.AW = tf.Variable(tf.random_normal([h_size // 2, 4])) # Create variables w/ values initialized by random normal distribution
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
# "streamA"/"streamV" are relevant frame + RNN info to find action/state value.
# Multiply them w/ weights "AW"/"VW" to get (predicted) action/state value.
self.Advantage = tf.matmul(self.streamA, self.AW)
self.Value = tf.matmul(self.streamV, self.VW)
# Derivative of sum of "Advantage" wrt to each pixel in each image in "imageIn".
# This is the only mention of "salience" in this script! See "helper.py" for more
# "salience" has dimensions: (?, 84, 84, 3)
# "salience" gives an idea of what pixels contribute the most to changing "Advantage".
# A large gradient for a pixel indicates that pixel changing would change "Advantage" a lot.
# A 0 gradient for a pixel indicates that pixel doesn't really matter for "Advantage".
# See "https://raghakot.github.io/keras-vis/visualizations/saliency/" for more info
self.salience = tf.gradients(self.Advantage, self.imageIn)
# Get final Q-values. Add state value to relative action advantage.
# For each action, subtract the average action value. That leaves the relative value of each action.
self.Qout = self.Value + tf.subtract(self.Advantage,
tf.reduce_mean(self.Advantage, axis = 1, keepdims = True))
self.predict = tf.argmax(self.Qout, 1)
# Get loss by taking sum of squares difference between target and predicted Q-values
self.targetQ = tf.placeholder(shape = [None], dtype = tf.float32)
self.actions = tf.placeholder(shape = [None], dtype = tf.int32)
self.actions_onehot = tf.one_hot(self.actions, 4, dtype = tf.float32)
# These are predicted Q-values for chosen actions.
# The inner "multiply" keeps the Q-values only for actions that were taken, because of the "actions_onehot" term.
# "reduce_sum" reduces dimensions by removing zero terms.
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis = 1)
self.td_error = tf.square(self.targetQ - self.Q)
# To only propogate accurate gradients through the network, mask 1st half of
# losses for each trace as per Lample & Chatlot 2016.
# A trace is a sequence of experiences from within an episode.
# We use consecutive frames to understand temporal dependencies.
# Research has shown only sending the last half of gradients improves performance
# by only sending more meaningful info through the network.
# Reminds me of dropout.
# See blog post for more info: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat([self.maskA, self.maskB], 1)
self.mask = tf.reshape(self.mask, [-1]) # Make mask 1D
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate = 0.0001)
self.updateModel = self.trainer.minimize(self.loss)
def squeeze(self, inputs, output_channels):
return slim.convolution2d(inputs,
num_outputs=output_channels,
kernel_size=1,
stride=1,
scope="squeeze")
def mobilenet(inputs, width_multiplier=1, scope=None, is_training=True):
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
# helper function to build the depthwise separable convolution layer.
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs = None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc +
'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc + '/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc + '/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc + '/pw_batch_norm')
return bn
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu):
net = slim.convolution2d(inputs,
round(32 * width_multiplier), [3, 3],
stride=2,
padding='SAME',
scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net,
64,
width_multiplier,
sc='conv_ds_2')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
downsample=True,
sc='conv_ds_3')
net = _depthwise_separable_conv(net,
128,
width_multiplier,
sc='conv_ds_4')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
downsample=True,
sc='conv_ds_5')
net = _depthwise_separable_conv(net,
256,
width_multiplier,
sc='conv_ds_6')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
downsample=True,
sc='conv_ds_7')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_8')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_9')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_10')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_11')
net = _depthwise_separable_conv(net,
512,
width_multiplier,
sc='conv_ds_12')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
downsample=True,
sc='conv_ds_13')
net = _depthwise_separable_conv(net,
1024,
width_multiplier,
sc='conv_ds_14')
net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
end_points = slim.utils.convert_collection_to_dict(
end_points_collection)
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points['squeeze'] = net
return net, end_points
def __init__(self,
h_size,
a_size,
action_h_size,
rnn_cell,
scopeName,
discount=0.99):
self.h_size, self.a_size, self.discount = h_size, a_size, discount
self.scalarInput = tf.placeholder(shape=[None, 7056],
dtype=tf.uint8,
name='frameInput')
self.batch_size = tf.placeholder(dtype=tf.int32,
shape=[],
name='batchSize')
self.trainLength = tf.placeholder(dtype=tf.int32,
shape=[],
name='trainLength')
self.actionsInput = tf.placeholder(shape=[None],
dtype=tf.int32,
name='actionsInput')
self.actionsInputOnehot = tf.one_hot(self.actionsInput, a_size)
self.actionsInputWeights = tf.Variable(
tf.random_normal([a_size, action_h_size]))
self.actionsInputProjected = tf.matmul(self.actionsInputOnehot,
self.actionsInputWeights)
self.frameShape = tf.constant((84, 84, 1), dtype=tf.int32)
# self.frames = tf.reshape(self.scalarInput, tf.concat(([self.batch_size*self.trainLength], self.frameShape), 0))
self.frames = tf.reshape(self.scalarInput / 255, [-1, 84, 84, 1])
self.conv1 = slim.convolution2d(inputs=self.frames,
num_outputs=32,
kernel_size=(8, 8),
stride=(4, 4),
padding='VALID',
biases_initializer=None,
scope=scopeName + '_conv1')
self.conv2 = slim.convolution2d(inputs=self.conv1,
num_outputs=64,
kernel_size=(4, 4),
stride=(2, 2),
padding='VALID',
biases_initializer=None,
scope=scopeName + '_conv2')
self.conv3 = slim.convolution2d(inputs=self.conv2,
num_outputs=64,
kernel_size=(3, 3),
stride=(1, 1),
padding='VALID',
biases_initializer=None,
scope=scopeName + '_conv3')
self.conv4 = slim.convolution2d(inputs=self.conv3,
num_outputs=h_size,
kernel_size=(7, 7),
stride=(1, 1),
padding='VALID',
biases_initializer=None,
scope=scopeName + '_conv4')
# self.convFlat = tf.reshape(
# slim.flatten(self.conv4), [self.batch_size, self.trainLength, h_size])
# print(self.convFlat.shape)
self.rnnInput = tf.concat([
tf.reshape(slim.flatten(self.conv4),
[self.batch_size, self.trainLength, h_size]),
tf.reshape(self.actionsInputProjected,
[self.batch_size, self.trainLength, action_h_size])
], 2)
print(self.rnnInput.shape)
self.state_init = rnn_cell.zero_state(self.batch_size, tf.float32)
self.rnn, self.rnn_state = tf.nn.dynamic_rnn(
inputs=self.rnnInput,
cell=rnn_cell,
dtype=tf.float32,
initial_state=self.state_init,
scope=scopeName + '_rnn')
self.rnn = tf.reshape(self.rnn, shape=[-1, h_size])
self.streamA, self.streamV = tf.split(self.rnn, 2, axis=1)
print(self.streamA.shape)
self.AW = tf.Variable(tf.random_normal([h_size // 2, a_size]))
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
self.A = tf.matmul(self.streamA, self.AW)
self.V = tf.matmul(self.streamV, self.VW)
self.salience = tf.gradients(self.A, self.scalarInput)
self.Qout = self.V + \
(self.A - tf.reduce_mean(self.A, axis=1, keepdims=True))
self.predict = tf.argmax(self.Qout, 1)
self.action = self.predict[-1]
# self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions,
a_size,
dtype=tf.float32)
self.Q = tf.reduce_sum(self.Qout * self.actions_onehot,
reduction_indices=1)
self.sample_terminals = tf.placeholder(tf.int32,
shape=[None],
name='sample_terminals')
end_multiplier = tf.cast(-(self.sample_terminals - 1), tf.float32)
self.sample_rewards = tf.placeholder(tf.float32,
shape=[None],
name='sample_rewards')
self.doubleQ = tf.placeholder(tf.float32, shape=(None), name='doubleQ')
self.targetQ = self.sample_rewards + self.discount * self.doubleQ * end_multiplier
# only train on first half of every trace per Lample & Chatlot 2016
self.mask = tf.concat(
(tf.zeros((self.batch_size, self.trainLength // 2)),
tf.ones((self.batch_size, self.trainLength // 2))), 1)
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.losses.huber_loss(self.Q * self.mask,
self.targetQ * self.mask)
if scopeName == 'main':
tf.summary.scalar('loss', self.loss)
tf.summary.histogram('Q', self.Qout)
tf.summary.histogram('hidden', self.rnn_state)
self.trainer = tf.train.RMSPropOptimizer(0.00025,
momentum=0.95,
epsilon=0.01)
self.updateModel = self.trainer.minimize(self.loss)
def __init__(self, h_size, rnn_cell, myScope):
# Network recieves a game frame, flattened
# Resize and processes through 4 convolutional layers
self.scalarInput = tf.placeholder(shape=[None, 21168],
dtype=tf.float32)
self.imageIn = tf.reshape(self.scalarInput, shape=[-1, 84, 84, 3])
self.conv1 = slim.convolution2d(inputs=self.imageIn,
num_outputs=32,
kernel_size=[8, 8],
stride=[4, 4],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv1')
self.conv2 = slim.convolution2d(inputs=self.conv1,
num_outputs=64,
kernel_size=[4, 4],
stride=[2, 2],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv2')
self.conv3 = slim.convolution2d(inputs=self.conv2,
num_outputs=64,
kernel_size=[3, 3],
stride=[1, 1],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv3')
self.conv4 = slim.convolution2d(inputs=self.conv3,
num_outputs=h_size,
kernel_size=[7, 7],
stride=[1, 1],
padding='VALID',
biases_initializer=None,
scope=myScope + '_conv4')
self.trainLength = tf.placeholder(dtype=tf.int32)
# We take the output from final conv layer and send to recurrent layer
# Input must be shaped into [batch x trace x units] for rnn processing and returned to [batch x units] when sent thorugh uppers
self.batch_size = tf.placeholder(dtype=tf.int32, shape=[])
self.convFlat = tf.reshape(slim.flatten(self.conv4),
[self.batch_size, self.trainLength, h_size])
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
self.rnn, self.rnn_state = tf.nn.dynamic_rnn(
inputs=self.convFlat,
cell=rnn_cell,
dtype=tf.float32,
initial_state=self.state_in,
scope=myScope + '_rnn')
self.rnn = tf.reshape(self.rnn, shape=[-1, h_size])
# The output from the recurrent player is split into Value and Advantage streams
self.streamA, self.streamV = tf.split(self.rnn, 2, 1)
self.AW = tf.Variable(tf.random_normal([h_size // 2, 4]))
self.VW = tf.Variable(tf.random_normal([h_size // 2, 1]))
self.Advantage = tf.matmul(self.streamA, self.AW)
self.Value = tf.matmul(self.streamV, self.VW)
self.salience = tf.gradients(self.Advantage, self.imageIn)
# Then combine them together for our final Q-value
self.Qout = self.Value + tf.subtract(
self.Advantage,
tf.reduce_mean(self.Advantage, axis=1, keep_dims=True))
self.predict = tf.argmax(self.Qout, 1)
# Obtain loss by taking sum of squares difference between the target and predict Q values
self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, 4, dtype=tf.float32)
self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot),
axis=1)
self.td_error = tf.square(self.targetQ - self.Q)
# in order to only propogate accurate gradients, we mask the first half of the losses for each trace
self.maskA = tf.zeros([self.batch_size, self.trainLength // 2])
self.maskB = tf.ones([self.batch_size, self.trainLength // 2])
self.mask = tf.concat([self.maskA, self.maskB], 1)
self.mask = tf.reshape(self.mask, [-1])
self.loss = tf.reduce_mean(self.td_error * self.mask)
self.trainer = tf.train.AdamOptimizer(learning_rate=0.0001)
self.updateModel = self.trainer.minimize(self.loss)
def __init__(self):
action_input = Input(shape=[None, 1])
state_input = Input(shape=[None, 224, 224, 3])
value_dcn = dcn_resnet()
policy_dcn = dcn_resnet() # 변수 공유? 다른 변수?
value_lstm = CuDNNLSTM(256)(value_dcn, state_input) # state-value : expected return
policy_lstm = CuDNNLSTM(256)(policy_dcn) # policy : agent's action selection
self.value_model = Dense(1, activation='relu')(value_lstm)
self.policy_model = Dense(1, activation='relu')(policy_lstm)
self.action_max = 2
self.conv1 = slim.conv2d(self.input_image,
activation_fn=tf.nn.relu,
num_outputs=32,
kernel_size=[8, 8],
stride=[4, 4],
padding='VALID')
self.conv2 = slim.conv2d(self.conv1,
activation_fn=tf.nn.relu,
num_outputs=64,
kernel_size=[4, 4],
stride=[2, 2],
padding='VALID')
self.conv3 = slim.convolution2d(
inputs=self.conv2, num_outputs=64,
kernel_size=[3, 3], stride=[1, 1], padding='VALID',
activation_fn=tf.nn.relu)
self.conv4 = slim.convolution2d(
inputs=self.conv3, num_outputs=256,
kernel_size=[7, 7], stride=[1, 1], padding='VALID',
activation_fn=tf.nn.relu)
hidden = slim.fully_connected(slim.flatten(self.conv4), 256,
activation_fn=tf.nn.relu)
# temporal dependency
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(256, reuse=tf.AUTO_REUSE)
c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
# self.state_in = (c_in, h_in)
self.rnn_in = tf.expand_dims(hidden, [0])
# step_size = tf.shape(self.imageIn[:1]) # 84 84 3
self.state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in) # c --> hidden, h --> output
lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(lstm_cell, self.rnn_in, initial_state=self.state_in,
time_major=False, scope="A3C")
lstm_c, lstm_h = self.lstm_state
self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
rnn_out = tf.reshape(lstm_outputs, [-1, 256])
# self.policy = slim.fully_connected(rnn_out, a_size,
# activation_fn=tf.nn.relu,
# weights_initializer=normalized_columns_initializer(0.01),
# biases_initializer=None)
#
# hidden1 = tf.layers.dense(rnn_out, 16, activation=tf.nn.relu)
# hidden2 = tf.layers.dense(hidden1, 16, activation=tf.nn.relu)
# hidden3 = tf.layers.dense(hidden2, 16, activation=tf.nn.relu)
self.policy = tf.layers.dense(rnn_out, 9, activation=tf.nn.relu)
self.policy = tf.nn.softmax(self.policy)
self.value = slim.fully_connected(rnn_out, 1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=None)
self.true_val = tf.placeholder(tf.int32, shape=[9])
# self.error = tf.reduce_mean(tf.square(self.true_val - self.policy))
# self.train_op = tf.train.AdamOptimizer(0.001)
self.error = tf.nn.softmax_cross_entropy_with_logits(labels=self.true_val, logits=self.policy)
# self.error = tf.reduce_mean(tf.square(tf.subtract(self.true_val, self.policy)))
self.train_op = tf.train.AdamOptimizer(0.01).minimize(self.error)
self.saver = tf.train.Saver()
def create_ds_cnn_model(fingerprint_input, model_settings, model_size_info,
is_training):
"""Builds a model with depthwise separable convolutional neural network
Model definition is based on https://arxiv.org/abs/1704.04861 and
Tensorflow implementation: https://github.com/Zehaos/MobileNet
model_size_info: defines number of layers, followed by the DS-Conv layer
parameters in the order {number of conv features, conv filter height,
width and stride in y,x dir.} for each of the layers.
Note that first layer is always regular convolution, but the remaining
layers are all depthwise separable convolutions.
"""
def ds_cnn_arg_scope(weight_decay=0):
"""Defines the default ds_cnn argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the DS-CNN model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
def _depthwise_separable_conv(inputs,
num_pwc_filters,
sc,
kernel_size,
stride):
""" Helper function to build the depth-wise separable convolution layer.
"""
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=stride,
depth_multiplier=1,
kernel_size=kernel_size,
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
label_count = model_settings['label_count']
input_frequency_size = model_settings['dct_coefficient_count']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size, 1])
t_dim = input_time_size
f_dim = input_frequency_size
#Extract model dimensions from model_size_info
num_layers = model_size_info[0]
conv_feat = [None]*num_layers
conv_kt = [None]*num_layers
conv_kf = [None]*num_layers
conv_st = [None]*num_layers
conv_sf = [None]*num_layers
i=1
for layer_no in range(0,num_layers):
conv_feat[layer_no] = model_size_info[i]
i += 1
conv_kt[layer_no] = model_size_info[i]
i += 1
conv_kf[layer_no] = model_size_info[i]
i += 1
conv_st[layer_no] = model_size_info[i]
i += 1
conv_sf[layer_no] = model_size_info[i]
i += 1
scope = 'DS-CNN'
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
decay=0.96,
updates_collections=None,
activation_fn=tf.nn.relu):
for layer_no in range(0,num_layers):
if layer_no==0:
net = slim.convolution2d(fingerprint_4d, conv_feat[layer_no],\
[conv_kt[layer_no], conv_kf[layer_no]], stride=[conv_st[layer_no], conv_sf[layer_no]], padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
else:
net = _depthwise_separable_conv(net, conv_feat[layer_no], \
kernel_size = [conv_kt[layer_no],conv_kf[layer_no]], \
stride = [conv_st[layer_no],conv_sf[layer_no]], sc='conv_ds_'+str(layer_no))
t_dim = math.ceil(t_dim/float(conv_st[layer_no]))
f_dim = math.ceil(f_dim/float(conv_sf[layer_no]))
net = slim.avg_pool2d(net, [t_dim, f_dim], scope='avg_pool')
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
logits = slim.fully_connected(net, label_count, activation_fn=None, scope='fc1')
if is_training:
return logits, dropout_prob
else:
return logits
def mobilenet_v2(input, weight_decay, batch_norm_params):
features = {}
with tf.variable_scope('Mobilenet'):
with slim.arg_scope([slim.convolution2d, slim.separable_conv2d], \
activation_fn=tf.nn.relu6,\
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=tf.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
padding='SAME'):
print('Mobilnet input shape({}): {}'.format(
input.name, input.get_shape()))
# 96*96*3 112*112*3
conv_1 = slim.convolution2d(input,
32, [3, 3],
stride=2,
scope='conv_1')
print(conv_1.name, conv_1.get_shape())
# 48*48*32 56*56*32
conv2_1 = slim.separable_convolution2d(conv_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv2_1/dwise')
print(conv2_1.name, conv2_1.get_shape())
conv2_1 = slim.convolution2d(conv2_1,
16, [1, 1],
stride=1,
activation_fn=None,
scope='conv2_1/linear')
print(conv2_1.name, conv2_1.get_shape())
features['feature2'] = conv2_1
# 48*48*16 56*56*16
conv3_1 = slim.convolution2d(conv2_1,
96, [1, 1],
stride=1,
scope='conv3_1/expand')
print(conv3_1.name, conv3_1.get_shape())
conv3_1 = slim.separable_convolution2d(conv3_1,
num_outputs=None,
stride=2,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_1/dwise')
print(conv3_1.name, conv3_1.get_shape())
conv3_1 = slim.convolution2d(conv3_1,
24, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_1/linear')
print(conv3_1.name, conv3_1.get_shape())
conv3_2 = slim.convolution2d(conv3_1,
144, [1, 1],
stride=1,
scope='conv3_2/expand')
print(conv3_2.name, conv3_2.get_shape())
conv3_2 = slim.separable_convolution2d(conv3_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv3_2/dwise')
print(conv3_2.name, conv3_2.get_shape())
conv3_2 = slim.convolution2d(conv3_2,
24, [1, 1],
stride=1,
activation_fn=None,
scope='conv3_2/linear')
print(conv3_2.name, conv3_2.get_shape())
block_3_2 = conv3_1 + conv3_2
print(block_3_2.name, block_3_2.get_shape())
features['feature3'] = block_3_2
features['pfld'] = block_3_2
# 24*24*24 28*28*24
conv4_1 = slim.convolution2d(block_3_2,
144, [1, 1],
stride=1,
scope='conv4_1/expand')
print(conv4_1.name, conv4_1.get_shape())
conv4_1 = slim.separable_convolution2d(conv4_1,
num_outputs=None,
stride=2,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv4_1/dwise')
print(conv4_1.name, conv4_1.get_shape())
conv4_1 = slim.convolution2d(conv4_1,
32, [1, 1],
stride=1,
activation_fn=None,
scope='conv4_1/linear')
print(conv4_1.name, conv4_1.get_shape())
conv4_2 = slim.convolution2d(conv4_1,
192, [1, 1],
stride=1,
scope='conv4_2/expand')
print(conv4_2.name, conv4_2.get_shape())
conv4_2 = slim.separable_convolution2d(conv4_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv4_2/dwise')
print(conv4_2.name, conv4_2.get_shape())
conv4_2 = slim.convolution2d(conv4_2,
32, [1, 1],
stride=1,
activation_fn=None,
scope='conv4_2/linear')
print(conv4_2.name, conv4_2.get_shape())
block_4_2 = conv4_1 + conv4_2
print(block_4_2.name, block_4_2.get_shape())
conv4_3 = slim.convolution2d(block_4_2,
192, [1, 1],
stride=1,
scope='conv4_3/expand')
print(conv4_3.name, conv4_3.get_shape())
conv4_3 = slim.separable_convolution2d(conv4_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv4_3/dwise')
print(conv4_3.name, conv4_3.get_shape())
conv4_3 = slim.convolution2d(conv4_3,
32, [1, 1],
stride=1,
activation_fn=None,
scope='conv4_3/linear')
print(conv4_3.name, conv4_3.get_shape())
block_4_3 = block_4_2 + conv4_3
print(block_4_3.name, block_4_3.get_shape())
# 12*12*32 14*14*32
features['feature4'] = block_4_3
conv5_1 = slim.convolution2d(block_4_3,
192, [1, 1],
stride=1,
scope='conv5_1/expand')
print(conv5_1.name, conv5_1.get_shape())
conv5_1 = slim.separable_convolution2d(conv5_1,
num_outputs=None,
stride=2,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_1/dwise')
print(conv5_1.name, conv5_1.get_shape())
conv5_1 = slim.convolution2d(conv5_1,
64, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_1/linear')
print(conv5_1.name, conv5_1.get_shape())
conv5_2 = slim.convolution2d(conv5_1,
384, [1, 1],
stride=1,
scope='conv5_2/expand')
print(conv5_2.name, conv5_2.get_shape())
conv5_2 = slim.separable_convolution2d(conv5_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_2/dwise')
print(conv5_2.name, conv5_2.get_shape())
conv5_2 = slim.convolution2d(conv5_2,
64, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_2/linear')
print(conv5_2.name, conv5_2.get_shape())
block_5_2 = conv5_1 + conv5_2
print(block_5_2.name, block_5_2.get_shape())
conv5_3 = slim.convolution2d(block_5_2,
384, [1, 1],
stride=1,
scope='conv5_3/expand')
print(conv5_3.name, conv5_3.get_shape())
conv5_3 = slim.separable_convolution2d(conv5_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_3/dwise')
print(conv5_3.name, conv5_3.get_shape())
conv5_3 = slim.convolution2d(conv5_3,
64, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_3/linear')
print(conv5_3.name, conv5_3.get_shape())
block_5_3 = block_5_2 + conv5_3
print(block_5_3.name, block_5_3.get_shape())
conv5_4 = slim.convolution2d(block_5_3,
384, [1, 1],
stride=1,
scope='conv5_4/expand')
print(conv5_4.name, conv5_4.get_shape())
conv5_4 = slim.separable_convolution2d(conv5_4,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv5_4/dwise')
print(conv5_4.name, conv5_4.get_shape())
conv5_4 = slim.convolution2d(conv5_4,
64, [1, 1],
stride=1,
activation_fn=None,
scope='conv5_4/linear')
print(conv5_4.name, conv5_4.get_shape())
block_5_4 = block_5_3 + conv5_4
print(block_5_4.name, block_5_4.get_shape())
# 6*6*64 7*7*64
conv6_1 = slim.convolution2d(block_5_4,
384, [1, 1],
stride=1,
scope='conv6_1/expand')
print(conv6_1.name, conv6_1.get_shape())
conv6_1 = slim.separable_convolution2d(conv6_1,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv6_1/dwise')
print(conv6_1.name, conv6_1.get_shape())
conv6_1 = slim.convolution2d(conv6_1,
96, [1, 1],
stride=1,
activation_fn=None,
scope='conv6_1/linear')
print(conv6_1.name, conv6_1.get_shape())
conv6_2 = slim.convolution2d(conv6_1,
576, [1, 1],
stride=1,
scope='conv6_2/expand')
print(conv6_2.name, conv6_2.get_shape())
conv6_2 = slim.separable_convolution2d(conv6_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv6_2/dwise')
print(conv6_2.name, conv6_2.get_shape())
conv6_2 = slim.convolution2d(conv6_2,
96, [1, 1],
stride=1,
activation_fn=None,
scope='conv6_2/linear')
print(conv6_2.name, conv6_2.get_shape())
block_6_2 = conv6_1 + conv6_2
print(block_6_2.name, block_6_2.get_shape())
conv6_3 = slim.convolution2d(block_6_2,
576, [1, 1],
stride=1,
scope='conv6_3/expand')
print(conv6_3.name, conv6_3.get_shape())
conv6_3 = slim.separable_convolution2d(conv6_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv6_3/dwise')
print(conv6_3.name, conv6_3.get_shape())
conv6_3 = slim.convolution2d(conv6_3,
96, [1, 1],
stride=1,
activation_fn=None,
scope='conv6_3/linear')
print(conv6_3.name, conv6_3.get_shape())
block_6_3 = block_6_2 + conv6_3
print(block_6_3.name, block_6_3.get_shape())
features['feature5'] = block_6_3
# 6*6*96 7*7*96
conv7_1 = slim.convolution2d(block_6_3,
576, [1, 1],
stride=1,
scope='conv7_1/expand')
print(conv7_1.name, conv7_1.get_shape())
conv7_1 = slim.separable_convolution2d(conv7_1,
num_outputs=None,
stride=2,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv7_1/dwise')
print(conv7_1.name, conv7_1.get_shape())
conv7_1 = slim.convolution2d(conv7_1,
160, [1, 1],
stride=1,
activation_fn=None,
scope='conv7_1/linear')
print(conv7_1.name, conv7_1.get_shape())
conv7_2 = slim.convolution2d(conv7_1,
960, [1, 1],
stride=1,
scope='conv7_2/expand')
print(conv7_2.name, conv7_2.get_shape())
conv7_2 = slim.separable_convolution2d(conv7_2,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv7_2/dwise')
print(conv7_2.name, conv7_2.get_shape())
conv7_2 = slim.convolution2d(conv7_2,
160, [1, 1],
stride=1,
activation_fn=None,
scope='conv7_2/linear')
print(conv7_2.name, conv7_2.get_shape())
block_7_2 = conv7_1 + conv7_2
print(block_7_2.name, block_7_2.get_shape())
conv7_3 = slim.convolution2d(block_7_2,
960, [1, 1],
stride=1,
scope='conv7_3/expand')
print(conv7_3.name, conv7_3.get_shape())
conv7_3 = slim.separable_convolution2d(conv7_3,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv7_3/dwise')
print(conv7_3.name, conv7_3.get_shape())
conv7_3 = slim.convolution2d(conv7_3,
160, [1, 1],
stride=1,
activation_fn=None,
scope='conv7_3/linear')
print(conv7_3.name, conv7_3.get_shape())
block_7_3 = block_7_2 + conv7_3
print(block_7_3.name, block_7_3.get_shape())
conv7_4 = slim.convolution2d(block_7_3,
960, [1, 1],
stride=1,
scope='conv7_4/expand')
print(conv7_4.name, conv7_4.get_shape())
conv7_4 = slim.separable_convolution2d(conv7_4,
num_outputs=None,
stride=1,
depth_multiplier=1,
kernel_size=[3, 3],
scope='conv7_4/dwise')
print(conv7_4.name, conv7_4.get_shape())
conv7_4 = slim.convolution2d(conv7_4,
320, [1, 1],
stride=1,
activation_fn=None,
scope='conv7_4/linear')
print(conv7_4.name, conv7_4.get_shape())
features['feature6'] = conv7_4
return features
def create_model(input, landmark, phase_train, args):
batch_norm_params = {
'decay': 0.995,
'epsilon': 0.001,
'updates_collections': None, #tf.GraphKeys.UPDATE_OPS,
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
'is_training': phase_train
}
landmark_dim = int(landmark.get_shape()[-1])
features, landmarks_pre = pfld_inference(input, args.weight_decay,
batch_norm_params)
# loss
landmarks_loss = tf.reduce_sum(tf.square(landmarks_pre - landmark), axis=1)
landmarks_loss = tf.reduce_mean(landmarks_loss)
# add the auxiliary net
# : finish the loss function
print('\nauxiliary net')
with slim.arg_scope([slim.convolution2d, slim.fully_connected], \
activation_fn=tf.nn.relu,\
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
biases_initializer=tf.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
pfld_input = features['auxiliary_input']
net_aux = slim.convolution2d(pfld_input,
128, [3, 3],
stride=2,
scope='pfld_conv1')
print(net_aux.name, net_aux.get_shape())
# net = slim.max_pool2d(net, kernel_size=[3, 3], stride=1, scope='pool1', padding='SAME')
net_aux = slim.convolution2d(net_aux,
128, [3, 3],
stride=1,
scope='pfld_conv2')
print(net_aux.name, net_aux.get_shape())
net_aux = slim.convolution2d(net_aux,
32, [3, 3],
stride=2,
scope='pfld_conv3')
print(net_aux.name, net_aux.get_shape())
net_aux = slim.convolution2d(net_aux,
128, [7, 7],
stride=1,
scope='pfld_conv4')
print(net_aux.name, net_aux.get_shape())
net_aux = slim.max_pool2d(net_aux,
kernel_size=[3, 3],
stride=1,
scope='pool1',
padding='SAME')
print(net_aux.name, net_aux.get_shape())
net_aux = slim.flatten(net_aux)
print(net_aux.name, net_aux.get_shape())
fc1 = slim.fully_connected(net_aux,
num_outputs=32,
activation_fn=None,
scope='pfld_fc1')
print(fc1.name, fc1.get_shape())
euler_angles_pre = slim.fully_connected(fc1,
num_outputs=3,
activation_fn=None,
scope='pfld_fc2')
print(euler_angles_pre.name, euler_angles_pre.get_shape())
# return landmarks_loss, landmarks, heatmap_loss, HeatMaps
return landmarks_pre, landmarks_loss, euler_angles_pre
def mobilenet(inputs,
num_classes=1000,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
""" MobileNet
More detail, please refer to Google's paper(https://arxiv.org/abs/1704.04861).
Args:
inputs: a tensor of size [batch_size, height, width, channels].
num_classes: number of predicted classes.
is_training: whether or not the model is being trained.
scope: Optional scope for the variables.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, `num_classes`]
end_points: a dictionary from components of the network to the corresponding
activation.
"""
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu,
fused=True):
net = slim.convolution2d(inputs, round(32 * width_multiplier), [3, 3], stride=2, padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net, 64, width_multiplier, sc='conv_ds_2')
net = _depthwise_separable_conv(net, 128, width_multiplier, downsample=True, sc='conv_ds_3')
net = _depthwise_separable_conv(net, 128, width_multiplier, sc='conv_ds_4')
net = _depthwise_separable_conv(net, 256, width_multiplier, downsample=True, sc='conv_ds_5')
net = _depthwise_separable_conv(net, 256, width_multiplier, sc='conv_ds_6')
net = _depthwise_separable_conv(net, 512, width_multiplier, downsample=True, sc='conv_ds_7')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_8')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_9')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_10')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_11')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_12')
net = _depthwise_separable_conv(net, 1024, width_multiplier, downsample=True, sc='conv_ds_13')
net = _depthwise_separable_conv(net, 1024, width_multiplier, sc='conv_ds_14')
net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
end_points['squeeze'] = net
logits = slim.fully_connected(net, num_classes, activation_fn=None, scope='fc_16')
predictions = slim.softmax(logits, scope='Predictions')
end_points['Logits'] = logits
end_points['Predictions'] = predictions
return logits, end_points
