作为学习记录，记录一下复杂的心情。整个项目并没看完，很多流程其实已经忘了，但。。。。。。。。。。。
极其简单的修改奏效了
使用vgg.py的vgg_a作为整个模型的特征提取工具。
代码如下：
def vgg_a(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a',
fc_conv_padding='VALID',
global_pool=False,
stride=8):
with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = sc.original_name_scope + '_end_points'
# Collect outputs for conv2d, fully_connected and max_pool2d.
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
outputs_collections=end_points_collection):
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
end_points['pool1'] = net
net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
end_points['pool2'] = net
net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
end_points['pool3'] = net
if stride == 8:
return net, end_points
net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
end_points['pool4'] = net
if stride == 16:
return net, end_points
net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
end_points['pool5'] = net
# Use conv2d instead of fully_connected layers.
return net, end_points
这里的返回值就是最后一层的返回值和end_ponts的字典。
修改Resnet V1 Faster R-CNN implementation.
不敢在其他地方修改，照着Resnet V1 Faster R-CNN implementation的test文件简单修改了一下，其他地方是不敢动啊。
"""Resnet V1 Faster R-CNN implementation.
See "Deep Residual Learning for Image Recognition" by He et al., 2015.
https://arxiv.org/abs/1512.03385
Note: this implementation assumes that the classification checkpoint used
to finetune this model is trained using the same configuration as that of
the MSRA provided checkpoints
(see https://github.com/KaimingHe/deep-residual-networks), e.g., with
same preprocessing, batch norm scaling, etc.
"""
import tensorflow as tf
from object_detection.meta_architectures import faster_rcnn_meta_arch
from nets import resnet_utils
from nets import resnet_v1
from nets import vgg
slim = tf.contrib.slim
class FasterRCNNResnetV1FeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
"""Faster R-CNN Resnet V1 feature extractor implementation."""
def __init__(self,
architecture,
resnet_model,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0):
"""Constructor.
Args:
architecture: Architecture name of the Resnet V1 model.
resnet_model: Definition of the Resnet V1 model.
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
reuse_weights: See base class.
weight_decay: See base class.
Raises:
ValueError: If `first_stage_features_stride` is not 8 or 16.
"""
if first_stage_features_stride != 8 and first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 8 or 16.')
self._architecture = architecture
self._resnet_model = resnet_model
super(FasterRCNNResnetV1FeatureExtractor, self).__init__(
is_training, first_stage_features_stride, batch_norm_trainable,
reuse_weights, weight_decay)
def preprocess(self, resized_inputs):
"""Faster R-CNN Resnet V1 preprocessing.
VGG style channel mean subtraction as described here:
https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
Args:
resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
representing a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: A [batch, height_out, width_out, channels] float32
tensor representing a batch of images.
"""
channel_means = [123.68, 116.779, 103.939]
return resized_inputs - [[channel_means]]
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
Args:
preprocessed_inputs: A [batch, height, width, channels] float32 tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
activations: A dictionary mapping feature extractor tensor names to
tensors
Raises:
InvalidArgumentError: If the spatial size of `preprocessed_inputs`
(height or width) is less than 33.
ValueError: If the created network is missing the required activation.
"""
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
shape_assert = tf.Assert(
tf.logical_and(
tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
['image size must at least be 33 in both height and width.'])
with tf.control_dependencies([shape_assert]):
# Disables batchnorm for fine-tuning with smaller batch sizes.
# TODO(chensun): Figure out if it is needed when image
# batch size is bigger.
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
net, end_point = vgg.vgg_a(preprocessed_inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a',
fc_conv_padding='VALID',
global_pool=False,
stride=self._first_stage_features_stride)
# with slim.arg_scope(
# resnet_utils.resnet_arg_scope(
# batch_norm_epsilon=1e-5,
# batch_norm_scale=True,
# weight_decay=self._weight_decay)):
# with tf.variable_scope(
# self._architecture, reuse=self._reuse_weights) as var_scope:
# _, activations = self._resnet_model(
# preprocessed_inputs,
# num_classes=None,
# is_training=self._train_batch_norm,
# global_pool=False,
# output_stride=self._first_stage_features_stride,
# spatial_squeeze=False,
# scope=var_scope)
#
# handle = scope + '/%s/block1' % self._architecture
return net, end_point
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
proposal_classifier_features = slim.conv2d(proposal_feature_maps, num_outputs=2048, kernel_size=3,scope='conv8')
# with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
# with slim.arg_scope(
# resnet_utils.resnet_arg_scope(
# batch_norm_epsilon=1e-5,
# batch_norm_scale=True,
# weight_decay=self._weight_decay)):
# with slim.arg_scope([slim.batch_norm],
# is_training=self._train_batch_norm):
# blocks = [
# resnet_utils.Block('block2', resnet_v1.bottleneck, [{
# 'depth': 2048,
# 'depth_bottleneck': 512,
# 'stride': 1
# }] * 3)
# ]
# proposal_classifier_features = resnet_utils.stack_blocks_dense(
# proposal_feature_maps, blocks)
return proposal_classifier_features
class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
"""Faster R-CNN Resnet 101 feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
reuse_weights: See base class.
weight_decay: See base class.
Raises:
ValueError: If `first_stage_features_stride` is not 8 or 16,
or if `architecture` is not supported.
"""
super(FasterRCNNResnet101FeatureExtractor, self).__init__(
'resnet_v1_101', resnet_v1.resnet_v1_101, is_training,
first_stage_features_stride, batch_norm_trainable,
reuse_weights, weight_decay)
配置文件使用的是１０１的配置文件，所以其他的可以干掉了。
修改奏效了
模型
loss
pb文件
检测图片
这里只是简单修改，因为在ｔｅｓｔ文件中发现Resnet V1 Faster R-CNN implementation的第二阶段竟然没有改变输入的维度。照着ｔｅｓｔ文件进行修改了一下。
更新
今天又重新进行了改动，
创建faster_rcnn_vgg_feature_extractor.py
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
import tensorflow as tf
from object_detection.meta_architectures import faster_rcnn_meta_arch
from nets import vgg
slim = tf.contrib.slim
class FasterRCNNVGGFeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
"""Faster R-CNN Resnet V1 feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0):
print('first_stage_features_stride',first_stage_features_stride)
if first_stage_features_stride != 8 and first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 8 or 16.')
super(FasterRCNNVGGFeatureExtractor, self).__init__(
is_training, first_stage_features_stride, batch_norm_trainable,
reuse_weights, weight_decay)
def preprocess(self, resized_inputs):
channel_means = [123.68, 116.779, 103.939]
return resized_inputs - [[channel_means]]
def _extract_proposal_features(self, preprocessed_inputs, scope):
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
shape_assert = tf.Assert(
tf.logical_and(
tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
['image size must at least be 33 in both height and width.'])
with tf.control_dependencies([shape_assert]):
# Disables batchnorm for fine-tuning with smaller batch sizes.
# TODO(chensun): Figure out if it is needed when image
# batch size is bigger.
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
net, end_point = vgg.vgg_a(preprocessed_inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.5,
spatial_squeeze=True,
scope='vgg_a',
fc_conv_padding='VALID',
global_pool=False,
stride=self._first_stage_features_stride)
return net, end_point
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
net = slim.repeat(proposal_feature_maps, 2, slim.conv2d, 2048, [3, 3], scope='conv8')
net = slim.max_pool2d(net, [2, 2], scope='pool8')
proposal_classifier_features = slim.repeat(proposal_feature_maps, 3, slim.conv2d, 64, [3, 3], scope='conv9')
return proposal_classifier_features
在model_builder.py中增加faster_vgg
from object_detection.models import faster_rcnn_vgg_feature_extractor as frcnn_vgg
# 进注册
FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP = {
'faster_rcnn_nas':
frcnn_nas.FasterRCNNNASFeatureExtractor,
'faster_rcnn_vgg':
frcnn_vgg.FasterRCNNVGGFeatureExtractor,
'faster_rcnn_pnas':
frcnn_pnas.FasterRCNNPNASFeatureExtractor,
'faster_rcnn_inception_resnet_v2':
frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor,
'faster_rcnn_inception_v2':
frcnn_inc_v2.FasterRCNNInceptionV2FeatureExtractor,
'faster_rcnn_resnet50':
frcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
'faster_rcnn_resnet101':
frcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
'faster_rcnn_resnet152':
frcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor,
}
修改config文件
model {
faster_rcnn {
num_classes: 20
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_vgg'
first_stage_features_stride: 16
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0003
schedule {
step: 900000
learning_rate: .00003
}
schedule {
step: 1200000
learning_rate: .000003
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
#fine_tune_checkpoint: "faster_rcnn_vgg_coco_11_06_2017/model.ckpt"
from_detection_checkpoint: false
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "data/voc2012_trian.record"
}
label_map_path: "data/pascal_label_map.pbtxt"
}
eval_config: {
num_examples: 100
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 100
eval_interval_secs: 5
#metrics_set:"pascal_voc_detection_metrics"
metrics_set: "coco_detection_metrics"
}
eval_input_reader: {
tf_record_input_reader {
input_path: "data/voc2012_val.record"
}
label_map_path: "data/pascal_label_map.pbtxt"
shuffle: false
num_readers: 1
num_epochs: 1
}
怎么调参需要慢慢弄。。。。。。。