我們分析core文件夾下的 feature_extractor.py 模塊
導入相關的包
import functools
import tensorflow as tf
from deeplab.core import xception
from nets.mobilenet import mobilenet as mobilenet_lib
from nets.mobilenet import mobilenet_v2
slim = tf.contrib.slim
# 默認的截止層 特徵抽取層？ Default end point for MobileNetv2.
_MOBILENET_V2_FINAL_ENDPOINT = 'layer_18'函數 _mobilenet_v2 返回使用mobilenet v2 進行的特徵抽取層
def _mobilenet_v2(net,
depth_multiplier,
output_stride,
reuse=None,
scope=None,
final_endpoint=None):
"""Auxiliary function to add support for 'reuse' to mobilenet_v2.
Args:
net: Input tensor of shape [batch_size, height, width, channels].
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.
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).
reuse: Reuse model variables.
scope: Optional variable scope.
final_endpoint: The endpoint to construct the network up to.
Returns:
Features extracted by MobileNetv2.
"""
with tf.variable_scope(
scope, 'MobilenetV2', [net], reuse=reuse) as scope:
return mobilenet_lib.mobilenet_base(
net,
conv_defs=mobilenet_v2.V2_DEF,
multiplier=depth_multiplier,
final_endpoint=final_endpoint or _MOBILENET_V2_FINAL_ENDPOINT,
output_stride=output_stride,
scope=scope)
一些map定義
# A map from network name to network function.
networks_map = {
'mobilenet_v2': _mobilenet_v2,
'xception_65': xception.xception_65,
}
# A map from network name to network arg scope.
arg_scopes_map = {
'mobilenet_v2': mobilenet_v2.training_scope,
'xception_65': xception.xception_arg_scope,
}
# Names for end point features.
DECODER_END_POINTS = 'decoder_end_points'
# A dictionary from network name to a map of end point features.
networks_to_feature_maps = {
'mobilenet_v2': {
# The provided checkpoint does not include decoder module.
DECODER_END_POINTS: None,
},
'xception_65': {
DECODER_END_POINTS: [
'entry_flow/block2/unit_1/xception_module/'
'separable_conv2_pointwise',
],
}
}
# A map from feature extractor name to the network name scope used in the
# ImageNet pretrained versions of these models.
name_scope = {
'mobilenet_v2': 'MobilenetV2',
'xception_65': 'xception_65',
}函數 _preprocess_subtract_imagenet_mean
輸入減去均值
# Mean pixel value.
_MEAN_RGB = [123.15, 115.90, 103.06]
def _preprocess_subtract_imagenet_mean(inputs):
"""Subtract Imagenet mean RGB value."""
mean_rgb = tf.reshape(_MEAN_RGB, [1, 1, 1, 3])
return inputs - mean_rgb函數 _preprocess_zero_mean_unit_range
歸一化 從[0, 255] 到[-1, 1]
def _preprocess_zero_mean_unit_range(inputs):
"""Map image values from [0, 255] to [-1, 1]."""
return (2.0 / 255.0) * tf.to_float(inputs) - 1.0_PREPROCESS_FN
函數 mean_pixel
_PREPROCESS_FN = {
'mobilenet_v2': _preprocess_zero_mean_unit_range,
'xception_65': _preprocess_zero_mean_unit_range,
}
def mean_pixel(model_variant=None):
"""Gets mean pixel value.
This function returns different mean pixel value, depending on the input
model_variant which adopts different preprocessing functions. We currently
handle the following preprocessing functions:
(1) _preprocess_subtract_imagenet_mean. We simply return mean pixel value.
(2) _preprocess_zero_mean_unit_range. We return [127.5, 127.5, 127.5].
The return values are used in a way that the padded regions after
pre-processing will contain value 0.
Args:
model_variant: Model variant (string) for feature extraction. For
backwards compatibility, model_variant=None returns _MEAN_RGB.
Returns:
Mean pixel value.
"""
if model_variant is None:
return _MEAN_RGB
else:
return [127.5, 127.5, 127.5]extract_features 函數
返回值
特徵層 features
end_points
def extract_features(images,
output_stride=8,
multi_grid=None,
depth_multiplier=1.0,
final_endpoint=None,
model_variant=None,
weight_decay=0.0001,
reuse=None,
is_training=False,
fine_tune_batch_norm=False,
regularize_depthwise=False,
preprocess_images=True,
num_classes=None,
global_pool=False):
"""Extracts features by the particular model_variant.
Args:
images: A tensor of size [batch, height, width, channels].
output_stride: The ratio of input to output spatial resolution.
multi_grid: Employ a hierarchy of different atrous rates within network.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops used in MobileNet.
final_endpoint: The MobileNet endpoint to construct the network up to.
model_variant: Model variant for feature extraction.
weight_decay: The weight decay for model variables.
reuse: Reuse the model variables or not.
is_training: Is training or not.
fine_tune_batch_norm: Fine-tune the batch norm parameters or not.
regularize_depthwise: Whether or not apply L2-norm regularization on the
depthwise convolution weights.
preprocess_images: Performs preprocessing on images or not. Defaults to
True. Set to False if preprocessing will be done by other functions. We
supprot two types of preprocessing: (1) Mean pixel substraction and (2)
Pixel values normalization to be [-1, 1].
num_classes: Number of classes for image classification task. Defaults
to None for dense prediction tasks.
global_pool: Global pooling for image classification task. Defaults to
False, since dense prediction tasks do not use this.
Returns:
features: A tensor of size [batch, feature_height, feature_width,
feature_channels], where feature_height/feature_width are determined
by the images height/width and output_stride.
end_points: A dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: Unrecognized model variant.
"""
if 'xception' in model_variant:
arg_scope = arg_scopes_map[model_variant](
weight_decay=weight_decay,
batch_norm_decay=0.9997,
batch_norm_epsilon=1e-3,
batch_norm_scale=True,
regularize_depthwise=regularize_depthwise)
features, end_points = get_network(
model_variant, preprocess_images, arg_scope)(
inputs=images,
num_classes=num_classes,
is_training=(is_training and fine_tune_batch_norm),
global_pool=global_pool,
output_stride=output_stride,
regularize_depthwise=regularize_depthwise,
multi_grid=multi_grid,
reuse=reuse,
scope=name_scope[model_variant])
elif 'mobilenet' in model_variant:
arg_scope = arg_scopes_map[model_variant](
is_training=(is_training and fine_tune_batch_norm),
weight_decay=weight_decay)
features, end_points = get_network(
model_variant, preprocess_images, arg_scope)(
inputs=images,
depth_multiplier=depth_multiplier,
output_stride=output_stride,
reuse=reuse,
scope=name_scope[model_variant],
final_endpoint=final_endpoint)
else:
raise ValueError('Unknown model variant %s.' % model_variant)
return features, end_pointsget_network 函數
返回特徵抽取網絡
def get_network(network_name, preprocess_images, arg_scope=None):
"""Gets the network.
Args:
network_name: Network name.
preprocess_images: Preprocesses the images or not.
arg_scope: Optional, arg_scope to build the network. If not provided the
default arg_scope of the network would be used.
Returns:
A network function that is used to extract features.
Raises:
ValueError: network is not supported.
"""
if network_name not in networks_map:
raise ValueError('Unsupported network %s.' % network_name)
arg_scope = arg_scope or arg_scopes_map[network_name]()
def _identity_function(inputs):
return inputs
if preprocess_images:
preprocess_function = _PREPROCESS_FN[network_name]
else:
preprocess_function = _identity_function
func = networks_map[network_name]
@functools.wraps(func)
def network_fn(inputs, *args, **kwargs):
with slim.arg_scope(arg_scope):
return func(preprocess_function(inputs), *args, **kwargs)
return network_fn