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add mobilenet
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‎CNNs/MobileNet.py

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"""
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2017/11/24 ref:https://github.com/Zehaos/MobileNet/blob/master/nets/mobilenet.py
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"""
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import tensorflow as tf
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from tensorflow.python.training import moving_averages
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UPDATE_OPS_COLLECTION = "_update_ops_"
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# create variable
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def create_variable(name, shape, initializer,
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dtype=tf.float32, trainable=True):
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return tf.get_variable(name, shape=shape, dtype=dtype,
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initializer=initializer, trainable=trainable)
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# batchnorm layer
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def bacthnorm(inputs, scope, epsilon=1e-05, momentum=0.99, is_training=True):
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inputs_shape = inputs.get_shape().as_list()
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params_shape = inputs_shape[-1:]
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axis = list(range(len(inputs_shape) - 1))
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with tf.variable_scope(scope):
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beta = create_variable("beta", params_shape,
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initializer=tf.zeros_initializer())
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gamma = create_variable("gamma", params_shape,
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initializer=tf.ones_initializer())
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# for inference
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moving_mean = create_variable("moving_mean", params_shape,
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initializer=tf.zeros_initializer(), trainable=False)
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moving_variance = create_variable("moving_variance", params_shape,
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initializer=tf.ones_initializer(), trainable=False)
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if is_training:
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mean, variance = tf.nn.moments(inputs, axes=axis)
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update_move_mean = moving_averages.assign_moving_average(moving_mean,
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mean, decay=momentum)
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update_move_variance = moving_averages.assign_moving_average(moving_variance,
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variance, decay=momentum)
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tf.add_to_collection(UPDATE_OPS_COLLECTION, update_move_mean)
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tf.add_to_collection(UPDATE_OPS_COLLECTION, update_move_variance)
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else:
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mean, variance = moving_mean, moving_variance
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return tf.nn.batch_normalization(inputs, mean, variance, beta, gamma, epsilon)
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# depthwise conv2d layer
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def depthwise_conv2d(inputs, scope, filter_size=3, channel_multiplier=1, strides=1):
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inputs_shape = inputs.get_shape().as_list()
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in_channels = inputs_shape[-1]
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with tf.variable_scope(scope):
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filter = create_variable("filter", shape=[filter_size, filter_size,
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in_channels, channel_multiplier],
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initializer=tf.truncated_normal_initializer(stddev=0.01))
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return tf.nn.depthwise_conv2d(inputs, filter, strides=[1, strides, strides, 1],
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padding="SAME", rate=[1, 1])
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# conv2d layer
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def conv2d(inputs, scope, num_filters, filter_size=1, strides=1):
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inputs_shape = inputs.get_shape().as_list()
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in_channels = inputs_shape[-1]
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with tf.variable_scope(scope):
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filter = create_variable("filter", shape=[filter_size, filter_size,
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in_channels, num_filters],
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initializer=tf.truncated_normal_initializer(stddev=0.01))
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return tf.nn.conv2d(inputs, filter, strides=[1, strides, strides, 1],
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padding="SAME")
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# avg pool layer
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def avg_pool(inputs, pool_size, scope):
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with tf.variable_scope(scope):
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return tf.nn.avg_pool(inputs, [1, pool_size, pool_size, 1],
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strides=[1, pool_size, pool_size, 1], padding="VALID")
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# fully connected layer
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def fc(inputs, n_out, scope, use_bias=True):
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inputs_shape = inputs.get_shape().as_list()
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n_in = inputs_shape[-1]
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with tf.variable_scope(scope):
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weight = create_variable("weight", shape=[n_in, n_out],
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initializer=tf.random_normal_initializer(stddev=0.01))
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if use_bias:
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bias = create_variable("bias", shape=[n_out,],
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initializer=tf.zeros_initializer())
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return tf.nn.xw_plus_b(inputs, weight, bias)
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return tf.matmul(inputs, weight)
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class MobileNet(object):
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def __init__(self, inputs, num_classes=1000, is_training=True,
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width_multiplier=1, scope="MobileNet"):
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"""
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The implement of MobileNet(ref:https://arxiv.org/abs/1704.04861)
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:param inputs: 4-D Tensor of [batch_size, height, width, channels]
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:param num_classes: number of classes
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:param is_training: Boolean, whether or not the model is training
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:param width_multiplier: float, controls the size of model
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:param scope: Optional scope for variables
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"""
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self.inputs = inputs
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self.num_classes = num_classes
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self.is_training = is_training
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self.width_multiplier = width_multiplier
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# construct model
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with tf.variable_scope(scope):
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# conv1
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net = conv2d(inputs, "conv_1", round(32 * width_multiplier), filter_size=3,
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strides=2) # ->[N, 112, 112, 32]
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net = tf.nn.relu(bacthnorm(net, "conv_1/bn", is_training=self.is_training))
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net = self._depthwise_separable_conv2d(net, 64, self.width_multiplier,
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"ds_conv_2") # ->[N, 112, 112, 64]
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net = self._depthwise_separable_conv2d(net, 128, self.width_multiplier,
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"ds_conv_3", downsample=True) # ->[N, 56, 56, 128]
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net = self._depthwise_separable_conv2d(net, 128, self.width_multiplier,
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"ds_conv_4") # ->[N, 56, 56, 128]
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net = self._depthwise_separable_conv2d(net, 256, self.width_multiplier,
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"ds_conv_5", downsample=True) # ->[N, 28, 28, 256]
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net = self._depthwise_separable_conv2d(net, 256, self.width_multiplier,
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"ds_conv_6") # ->[N, 28, 28, 256]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_7", downsample=True) # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_8") # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_9") # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_10") # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_11") # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 512, self.width_multiplier,
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"ds_conv_12") # ->[N, 14, 14, 512]
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net = self._depthwise_separable_conv2d(net, 1024, self.width_multiplier,
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"ds_conv_13", downsample=True) # ->[N, 7, 7, 1024]
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net = self._depthwise_separable_conv2d(net, 1024, self.width_multiplier,
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"ds_conv_14") # ->[N, 7, 7, 1024]
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net = avg_pool(net, 7, "avg_pool_15")
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net = tf.squeeze(net, [1, 2], name="SpatialSqueeze")
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self.logits = fc(net, self.num_classes, "fc_16")
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self.predictions = tf.nn.softmax(self.logits)
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def _depthwise_separable_conv2d(self, inputs, num_filters, width_multiplier,
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scope, downsample=False):
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"""depthwise separable convolution 2D function"""
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num_filters = round(num_filters * width_multiplier)
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strides = 2 if downsample else 1
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with tf.variable_scope(scope):
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# depthwise conv2d
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dw_conv = depthwise_conv2d(inputs, "depthwise_conv", strides=strides)
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# batchnorm
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bn = bacthnorm(dw_conv, "dw_bn", is_training=self.is_training)
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# relu
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relu = tf.nn.relu(bn)
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# pointwise conv2d (1x1)
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pw_conv = conv2d(relu, "pointwise_conv", num_filters)
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# bn
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bn = bacthnorm(pw_conv, "pw_bn", is_training=self.is_training)
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return tf.nn.relu(bn)
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if __name__ == "__main__":
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# test data
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inputs = tf.random_normal(shape=[4, 224, 224, 3])
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mobileNet = MobileNet(inputs)
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writer = tf.summary.FileWriter("./logs", graph=tf.get_default_graph())
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init = tf.global_variables_initializer()
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with tf.Session() as sess:
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sess.run(init)
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pred = sess.run(mobileNet.predictions)
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print(pred.shape)
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