[Tensorflow] Object Detection API - mobileNet_v1.py
故事背景
一、课前感言
How to retrain your own mobileNet. Let's do it.
本篇重点在实践。
二、CNN 代码
- mobileNet_v1代码
Where is the code of mobileNet?
Link: https://github.com/tensorflow/models/tree/master/research/slim/nets
Let's start from in mobilenet_v1.py.
def mobilenet_v1(inputs,
num_classes=1000,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1'):
"""Mobilenet v1 model for classification.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
dropout_keep_prob: the percentage of activation values that are retained.
is_training: whether is training or not.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
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.
Raises:
ValueError: Input rank is invalid.
"""
input_shape = inputs.get_shape().as_list()
if len(input_shape) != 4:
raise ValueError('Invalid input tensor rank, expected 4, was: %d' %
len(input_shape))
with tf.variable_scope(scope, 'MobilenetV1', [inputs, num_classes],
reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = mobilenet_v1_base(inputs, scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier,
conv_defs=conv_defs) # main part of Graph. ----> 详见下文
with tf.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
scope='AvgPool_1a')
end_points['AvgPool_1a'] = net
# 1 x 1 x 1024
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
if prediction_fn:
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
- 网络定义
以下可见工程化的神经网络定义 in mobilenet_v1.py，谷歌工程能力是毫无疑问的强。
# Conv and DepthSepConv namedtuple define layers of the MobileNet architecture
# Conv defines 3x3 convolution layers
# DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution.
# stride is the stride of the convolution
# depth is the number of channels or filters in a layer
Conv = namedtuple('Conv', ['kernel', 'stride', 'depth'])
DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth'])
# _CONV_DEFS specifies the MobileNet body
_CONV_DEFS = [
Conv(kernel=[3, 3], stride=2, depth=32),
DepthSepConv(kernel=[3, 3], stride=1, depth=64),
DepthSepConv(kernel=[3, 3], stride=2, depth=128),
DepthSepConv(kernel=[3, 3], stride=1, depth=128),
DepthSepConv(kernel=[3, 3], stride=2, depth=256),
DepthSepConv(kernel=[3, 3], stride=1, depth=256),
DepthSepConv(kernel=[3, 3], stride=2, depth=512),
DepthSepConv(kernel=[3, 3], stride=1, depth=512),
DepthSepConv(kernel=[3, 3], stride=1, depth=512),
DepthSepConv(kernel=[3, 3], stride=1, depth=512),
DepthSepConv(kernel=[3, 3], stride=1, depth=512),
DepthSepConv(kernel=[3, 3], stride=1, depth=512),
DepthSepConv(kernel=[3, 3], stride=2, depth=1024),
DepthSepConv(kernel=[3, 3], stride=1, depth=1024)
]
- 卷积细节定义
以下是主体的网络结构，调用上述结构，通过for循环画网络。【网络每一层的更为细节的描述】
def mobilenet_v1_base(inputs,
final_endpoint='Conv2d_13_pointwise',
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
output_stride=None,
scope=None):
"""Mobilenet v1.
Constructs a Mobilenet v1 network from inputs to the given final endpoint.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of
['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise',
'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5_pointwise',
'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise',
'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise',
'Conv2d_12_pointwise', 'Conv2d_13_pointwise'
].
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
output_stride: An integer that specifies the requested ratio of input to
output spatial resolution. If not None, then we invoke atrous convolution
if necessary to prevent the network from reducing the spatial resolution
of the activation maps. Allowed values are 8 (accurate fully convolutional
mode), 16 (fast fully convolutional mode), 32 (classification mode).
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or depth_multiplier <= 0, or the target output_stride is not
allowed.
"""
depth = lambda d: max(int(d * depth_multiplier), min_depth)
end_points = {}
# Used to find thinned depths for each layer.
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
if conv_defs is None:
conv_defs = _CONV_DEFS # ---->
if output_stride is not None and output_stride not in [8, 16, 32]:
raise ValueError('Only allowed output_stride values are 8, 16, 32.')
with tf.variable_scope(scope, 'MobilenetV1', [inputs]):
with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding='SAME'):
# The current_stride variable keeps track of the output stride of the
# activations, i.e., the running product of convolution strides up to the
# current network layer. This allows us to invoke atrous convolution
# whenever applying the next convolution would result in the activations
# having output stride larger than the target output_stride.
current_stride = 1
# The atrous convolution rate parameter.
rate = 1
net = inputs
for i, conv_def in enumerate(conv_defs):
end_point_base = 'Conv2d_%d' % i
if output_stride is not None and current_stride == output_stride:
# If we have reached the target output_stride, then we need to employ
# atrous convolution with stride=1 and multiply the atrous rate by the
# current unit's stride for use in subsequent layers.
layer_stride = 1
layer_rate = rate
rate *= conv_def.stride
else:
layer_stride = conv_def.stride
layer_rate = 1
current_stride *= conv_def.stride
if isinstance(conv_def, Conv):
end_point = end_point_base
net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel,
stride=conv_def.stride,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
elif isinstance(conv_def, DepthSepConv):
end_point = end_point_base + '_depthwise'
# By passing filters=None
# separable_conv2d produces only a depthwise convolution layer
net = slim.separable_conv2d(net, None, conv_def.kernel,
depth_multiplier=1,
stride=layer_stride,
rate=layer_rate,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
end_point = end_point_base + '_pointwise'
net = slim.conv2d(net, depth(conv_def.depth), [1, 1],
stride=1,
normalizer_fn=slim.batch_norm,
scope=end_point)
end_points[end_point] = net
if end_point == final_endpoint:
return net, end_points
else:
raise ValueError('Unknown convolution type %s for layer %d'
% (conv_def.ltype, i))
raise ValueError('Unknown final endpoint %s' % final_endpoint)
三、空间计算
- 参数计算
注解中，查看下全连接层到底占用了多少空间。
1000*1024=1024000 params
3185088/1024000=3.11【占模型比重约24.4%】
1800144/1024000=1.76【占模型比重约36.3%】
若全连接层改为1024*100，则会有10M左右的压缩空间。
- 参数详情
"""MobileNet v1. MobileNet is a general architecture and can be used for multiple use cases. Depending on the use case, it can use different input layer size and different head (for example: embeddings, localization and classification). As described in https://arxiv.org/abs/1704.04861. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam 100% Mobilenet V1 (base) with input size 224x224: See mobilenet_v1() Layer params macs -------------------------------------------------------------------------------- MobilenetV1/Conv2d_0/Conv2D: 864 10,838,016 MobilenetV1/Conv2d_1_depthwise/depthwise: 288 3,612,672 MobilenetV1/Conv2d_1_pointwise/Conv2D: 2,048 25,690,112 MobilenetV1/Conv2d_2_depthwise/depthwise: 576 1,806,336 MobilenetV1/Conv2d_2_pointwise/Conv2D: 8,192 25,690,112 MobilenetV1/Conv2d_3_depthwise/depthwise: 1,152 3,612,672 MobilenetV1/Conv2d_3_pointwise/Conv2D: 16,384 51,380,224 MobilenetV1/Conv2d_4_depthwise/depthwise: 1,152 903,168 MobilenetV1/Conv2d_4_pointwise/Conv2D: 32,768 25,690,112 MobilenetV1/Conv2d_5_depthwise/depthwise: 2,304 1,806,336 MobilenetV1/Conv2d_5_pointwise/Conv2D: 65,536 51,380,224 MobilenetV1/Conv2d_6_depthwise/depthwise: 2,304 451,584 MobilenetV1/Conv2d_6_pointwise/Conv2D: 131,072 25,690,112 MobilenetV1/Conv2d_7_depthwise/depthwise: 4,608 903,168 MobilenetV1/Conv2d_7_pointwise/Conv2D: 262,144 51,380,224 MobilenetV1/Conv2d_8_depthwise/depthwise: 4,608 903,168 MobilenetV1/Conv2d_8_pointwise/Conv2D: 262,144 51,380,224 MobilenetV1/Conv2d_9_depthwise/depthwise: 4,608 903,168 MobilenetV1/Conv2d_9_pointwise/Conv2D: 262,144 51,380,224 MobilenetV1/Conv2d_10_depthwise/depthwise: 4,608 903,168 MobilenetV1/Conv2d_10_pointwise/Conv2D: 262,144 51,380,224 MobilenetV1/Conv2d_11_depthwise/depthwise: 4,608 903,168 MobilenetV1/Conv2d_11_pointwise/Conv2D: 262,144 51,380,224 MobilenetV1/Conv2d_12_depthwise/depthwise: 4,608 225,792 MobilenetV1/Conv2d_12_pointwise/Conv2D: 524,288 25,690,112 MobilenetV1/Conv2d_13_depthwise/depthwise: 9,216 451,584 MobilenetV1/Conv2d_13_pointwise/Conv2D: 1,048,576 51,380,224 -------------------------------------------------------------------------------- Total: 3,185,088 567,716,352 75% Mobilenet V1 (base) with input size 128x128: See mobilenet_v1_075() Layer params macs -------------------------------------------------------------------------------- MobilenetV1/Conv2d_0/Conv2D: 648 2,654,208 MobilenetV1/Conv2d_1_depthwise/depthwise: 216 884,736 MobilenetV1/Conv2d_1_pointwise/Conv2D: 1,152 4,718,592 MobilenetV1/Conv2d_2_depthwise/depthwise: 432 442,368 MobilenetV1/Conv2d_2_pointwise/Conv2D: 4,608 4,718,592 MobilenetV1/Conv2d_3_depthwise/depthwise: 864 884,736 MobilenetV1/Conv2d_3_pointwise/Conv2D: 9,216 9,437,184 MobilenetV1/Conv2d_4_depthwise/depthwise: 864 221,184 MobilenetV1/Conv2d_4_pointwise/Conv2D: 18,432 4,718,592 MobilenetV1/Conv2d_5_depthwise/depthwise: 1,728 442,368 MobilenetV1/Conv2d_5_pointwise/Conv2D: 36,864 9,437,184 MobilenetV1/Conv2d_6_depthwise/depthwise: 1,728 110,592 MobilenetV1/Conv2d_6_pointwise/Conv2D: 73,728 4,718,592 MobilenetV1/Conv2d_7_depthwise/depthwise: 3,456 221,184 MobilenetV1/Conv2d_7_pointwise/Conv2D: 147,456 9,437,184 MobilenetV1/Conv2d_8_depthwise/depthwise: 3,456 221,184 MobilenetV1/Conv2d_8_pointwise/Conv2D: 147,456 9,437,184 MobilenetV1/Conv2d_9_depthwise/depthwise: 3,456 221,184 MobilenetV1/Conv2d_9_pointwise/Conv2D: 147,456 9,437,184 MobilenetV1/Conv2d_10_depthwise/depthwise: 3,456 221,184 MobilenetV1/Conv2d_10_pointwise/Conv2D: 147,456 9,437,184 MobilenetV1/Conv2d_11_depthwise/depthwise: 3,456 221,184 MobilenetV1/Conv2d_11_pointwise/Conv2D: 147,456 9,437,184 MobilenetV1/Conv2d_12_depthwise/depthwise: 3,456 55,296 MobilenetV1/Conv2d_12_pointwise/Conv2D: 294,912 4,718,592 MobilenetV1/Conv2d_13_depthwise/depthwise: 6,912 110,592 MobilenetV1/Conv2d_13_pointwise/Conv2D: 589,824 9,437,184 -------------------------------------------------------------------------------- Total: 1,800,144 106,002,432 """
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