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MobileNetV2_old.py
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import tensorflow
regularizer = tensorflow.contrib.layers.l1_l2_regularizer(1e-7, 1e-7)
def MobileNetV2Block(x, stride, t, output_channel, layer, training):
with tensorflow.variable_scope('MobileNetV2Block%s' % layer):
with tensorflow.variable_scope('conv1'):
weight = tensorflow.get_variable(
name='weight',
shape=[1, 1, x.shape.as_list()[-1], int(t * x.shape.as_list()[-1])],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=int(t * x.shape.as_list()[-1]),
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.conv2d(
input=x,
filter=weight,
strides=[1, 1, 1, 1],
padding='SAME') + bias
y = tensorflow.layers.batch_normalization(
inputs=y,
beta_regularizer=None,
gamma_regularizer=None,
training=training,
fused=None)
y = tensorflow.nn.relu6(y)
with tensorflow.variable_scope('conv2'):
weight = tensorflow.get_variable(
name='weight',
shape=[3, 3, y.shape.as_list()[-1], 1],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=y.shape.as_list()[-1],
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.depthwise_conv2d(
input=y,
filter=weight,
strides=[1, stride, stride, 1],
padding='SAME') + bias
y = tensorflow.layers.batch_normalization(
inputs=y,
beta_regularizer=None,
gamma_regularizer=None,
training=training,
fused=None)
y = tensorflow.nn.relu6(y)
with tensorflow.variable_scope('conv3'):
weight = tensorflow.get_variable(
name='weight',
shape=[1, 1, y.shape.as_list()[-1], output_channel],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=output_channel,
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.conv2d(
input=y,
filter=weight,
strides=[1, 1, 1, 1],
padding='SAME') + bias
y = tensorflow.layers.batch_normalization(
inputs=y,
beta_regularizer=None,
gamma_regularizer=None,
training=training,
fused=None)
if x.shape.as_list() == y.shape.as_list():
y = x + y
return y
def MobileNetV2(x, width, num_classes, training):
with tensorflow.variable_scope('MobileNetV2', reuse=tensorflow.AUTO_REUSE):
with tensorflow.variable_scope('layer1'):
weight = tensorflow.get_variable(
name='weight',
shape=[3, 3, x.shape.as_list()[-1], int(32 * width)],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=int(32 * width),
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.conv2d(
input=x,
filter=weight,
strides=[1, 2, 2, 1],
padding='SAME') + bias
y = tensorflow.layers.batch_normalization(
inputs=y,
beta_regularizer=None,
gamma_regularizer=None,
training=training,
fused=None)
y = tensorflow.nn.relu6(y)
y = MobileNetV2Block(y, 1, 1, int(16 * width), 2, training)
y = MobileNetV2Block(y, 2, 6, int(24 * width), 3, training)
y = MobileNetV2Block(y, 1, 6, int(24 * width), 4, training)
y = MobileNetV2Block(y, 2, 6, int(32 * width), 5, training)
y = MobileNetV2Block(y, 1, 6, int(32 * width), 6, training)
y = MobileNetV2Block(y, 1, 6, int(32 * width), 7, training)
y = MobileNetV2Block(y, 2, 6, int(64 * width), 8, training)
y = MobileNetV2Block(y, 1, 6, int(64 * width), 9, training)
y = MobileNetV2Block(y, 1, 6, int(64 * width), 10, training)
y = MobileNetV2Block(y, 1, 6, int(64 * width), 11, training)
y = MobileNetV2Block(y, 1, 6, int(96 * width), 12, training)
y = MobileNetV2Block(y, 1, 6, int(96 * width), 13, training)
y = MobileNetV2Block(y, 1, 6, int(96 * width), 14, training)
y = MobileNetV2Block(y, 2, 6, int(160 * width), 15, training)
y = MobileNetV2Block(y, 1, 6, int(160 * width), 16, training)
y = MobileNetV2Block(y, 1, 6, int(160 * width), 17, training)
y = MobileNetV2Block(y, 1, 6, int(320 * width), 18, training)
with tensorflow.variable_scope('layer19'):
weight = tensorflow.get_variable(
name='weight',
shape=[1, 1, y.shape.as_list()[-1], int(1280 * width)],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=int(1280 * width),
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.conv2d(
input=y,
filter=weight,
strides=[1, 1, 1, 1],
padding='SAME') + bias
y = tensorflow.layers.batch_normalization(
inputs=y,
beta_regularizer=None,
gamma_regularizer=None,
training=training,
fused=None)
y = tensorflow.nn.relu6(y)
y = tensorflow.nn.avg_pool2d(y, 7, 1, 'VALID')
with tensorflow.variable_scope('layer20'):
weight = tensorflow.get_variable(
name='weight',
shape=[1, 1, y.shape.as_list()[-1], num_classes],
initializer=tensorflow.initializers.glorot_uniform(),
regularizer=regularizer)
bias = tensorflow.get_variable(
name='bias',
shape=num_classes,
initializer=tensorflow.initializers.zeros(),
regularizer=regularizer)
y = tensorflow.nn.conv2d(
input=y,
filter=weight,
strides=[1, 1, 1, 1],
padding='SAME') + bias
return y
if __name__ == '__main__':
tensorflow.enable_eager_execution(config=tensorflow.ConfigProto(allow_soft_placement=True, gpu_options=tensorflow.GPUOptions(allow_growth=True), inter_op_parallelism_threads=0, intra_op_parallelism_threads=0))
data = tensorflow.random.uniform([128, 224, 224, 3])
y = MobileNetV2(data, 1.4, 10, True)
print()