【发布时间】：2017-08-18 09:15:05
【问题描述】：
我最初在 tensorflow github 上发布了这个，但我在这里复制它，因为它更合适。
我需要您的宝贵帮助，因为我遇到了一些奇怪的配置文件解析错误。我正在尝试在宠物数据集上本地运行 train.py 脚本。我已经按照教程中的说明完成了准备数据和配置文件的所有工作，这是我的目录结构：
+pets_example
+data
-pet_label_map.pbtxt
-pet_train.record
-pet_val.record
+models
+ssd_inception_v2_coco_11_06_2017
-frozen_inference_graph.pb
-graph.pbtxt
-model.ckpt.data-00000-of-00001
-model.ckpt.index
-model.ckpt.meta
-pipeline.proto
这是我的管道配置文件的内容：
syntax = "proto2";
package object_detection.protos;
import "object_detection/protos/eval.proto";
import "object_detection/protos/input_reader.proto";
import "object_detection/protos/model.proto";
import "object_detection/protos/train.proto";
// Convenience message for configuring a training and eval pipeline. Allows all
// of the pipeline parameters to be configured from one file.
message TrainEvalPipelineConfig {
optional DetectionModel model = 1;
optional TrainConfig train_config = 2;
optional InputReader train_input_reader = 3;
optional EvalConfig eval_config = 4;
optional InputReader eval_input_reader = 5;
}
model {
ssd {
num_classes: 37
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
}
}
similarity_calculator {
iou_similarity {
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
reduce_boxes_in_lowest_layer: true
}
}
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
box_predictor {
convolutional_box_predictor {
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 3
box_code_size: 4
apply_sigmoid_to_scores: false
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
}
}
}
feature_extractor {
type: 'ssd_inception_v2'
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid {
anchorwise_output: true
}
}
localization_loss {
weighted_smooth_l1 {
anchorwise_output: true
}
}
hard_example_miner {
num_hard_examples: 3000
iou_threshold: 0.99
loss_type: CLASSIFICATION
max_negatives_per_positive: 3
min_negatives_per_image: 0
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
batch_size: 24
optimizer {
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
fine_tune_checkpoint: "pets_example/models/ssd_inception_v2_coco_11_06_2017/model.ckpt"
from_detection_checkpoint: true
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "pets_example/data/pet_train.record"
}
label_map_path: "pets_example/data/pet_label_map.pbtxt"
}
eval_config: {
num_examples: 2000
}
eval_input_reader: {
tf_record_input_reader {
input_path: "pets_example/data/pet_val.record"
}
label_map_path: "pets_example/data/pet_label_map.pbtxt"
}
我正在使用 Linux Ubuntu 16.04，并且我有一个 tensorflow 1.1.0 的 pip 安装版本（没有 GPU 支持，我在 CPU 上运行所有东西）。
所以当我尝试命令时：
python3 object_detection/train.py --logtostderr --pipeline_config_path='pets_example/models/ssd_inception_v2_coco_11_06_2017/pipeline.proto' --train_dir='pets_example/models/train_events/'
我收到以下错误：
Traceback (most recent call last):
File "object_detection/train.py", line 201, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "object_detection/train.py", line 146, in main
model_config, train_config, input_config = get_configs_from_pipeline_file()
File "object_detection/train.py", line 106, in get_configs_from_pipeline_file
text_format.Merge(a, pipeline_config)
File "/usr/local/lib/python3.5/dist-packages/google/protobuf/text_format.py", line 481, in Merge
descriptor_pool=descriptor_pool)
File "/usr/local/lib/python3.5/dist-packages/google/protobuf/text_format.py", line 535, in MergeLines
return parser.MergeLines(lines, message)
File "/usr/local/lib/python3.5/dist-packages/google/protobuf/text_format.py", line 568, in MergeLines
self._ParseOrMerge(lines, message)
File "/usr/local/lib/python3.5/dist-packages/google/protobuf/text_format.py", line 583, in _ParseOrMerge
self._MergeField(tokenizer, message)
File "/usr/local/lib/python3.5/dist-packages/google/protobuf/text_format.py", line 657, in _MergeField
(message_descriptor.full_name, name))
google.protobuf.text_format.ParseError: 1:1 : Message type "object_detection.protos.TrainEvalPipelineConfig" has no field named "syntax".
我通过不同的文件跟踪错误，当我查看描述符的东西时很快就用完了技能……如果有人能提供任何帮助，那就太棒了！ :) 提前感谢您的帮助！
【问题讨论】：
-
您可以通过编辑上一个问题来添加标签“tensorflow”。
-
你可以去掉这行
syntax = "proto2"; -
您好，感谢您的回答。我确实删除了它，但随后我在“包”字段中遇到了同样的错误。我是否要从 pipeline.proto 文件中删除所有导致问题的字段？
-
我试了一下只是为了看看，这让我删除了包字段和导入行（没想到它会起作用，但还是尝试了），我得到了一个不同的错误：
google.protobuf.text_format.ParseError: 2:1 : Expected identifier or number.这在 text_format.py 脚本中比前一个脚本更早发生。
标签： python parsing tensorflow object-detection