python - 使用tensorflow的object detection训练我的模型,ckpt文件的时间戳超过4小时没有变化
问题描述
我在 Windows 10 上使用 anaconda(python3.6) 和 tensorflow(1.9.0) 来训练我的模型。
我用这个命令训练模型:</p>
python model_main.py --pipeline_config_path=training/ssd_mobilenet_v1_coco.config --model_dir=training/ --num_train_steps=500 --alsologtostderr
Anaconda 提示符输出以下信息。
ssd_mobilenet_v1_coco.config 中的内容是这样的:
# SSD with Mobilenet v1 configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.
model {
ssd {
num_classes: 2
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
}
}
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: 1
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
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v1'
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 {
}
}
localization_loss {
weighted_smooth_l1 {
}
}
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: 1
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: "PATH_TO_BE_CONFIGURED/model.ckpt"
#from_detection_checkpoint: true
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 100
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path:'data/train.record'
}
label_map_path:'data/side_vehicle.pbtxt'
}
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10
}
eval_input_reader: {
tf_record_input_reader {
input_path: 'data/test.record'
}
label_map_path: 'data/side_vehicle.pbtxt'
shuffle: false
num_readers: 1
}
为什么模型文件的时间戳不改变?哪里出错了?
当我使用此命令进行训练时:
python model_main.py --pipeline_config_path=training/ssd_mobilenet_v1_coco.config --model_dir=training/ --num_train_steps=10000
错误信息是:
回溯(最近一次调用最后):文件“E:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py”,第 1334 行,_do_call return fn(*args) 文件“E:\Anaconda3\ lib\site-packages\tensorflow\python\client\session.py”,第 1319 行,在 _run_fn 选项、feed_dict、fetch_list、target_list、run_metadata)文件“E:\Anaconda3\lib\site-packages\tensorflow\python\client \session.py",第 1407 行,在 _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InvalidArgumentError:断言失败:[最大框坐标值大于 1.100000:] [1.11401868] [[{{node ToAbsoluteCoordinates_1/Assert/AssertGuard /Assert}} = 断言[T=[DT_STRING, DT_FLOAT], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ToAbsoluteCoordinates_1/Assert/AssertGuard/Assert/Switch, ToAbsoluteCoordina tes_1/Assert/AssertGuard/Assert/data_0, ToAbsoluteCoordinates_1/Assert/AssertGuard/Assert/Switch_1)]]
解决方案
现在我知道哪里错了。我的 tensorflow 版本是 1.9.0。我把tensorflow的版本改成1.12.0,然后我修改了这个文件box_list_ops.py
,set check_range=False
。这样问题就解决了。
推荐阅读
- linux - bash - 获取正确处理新行的环境变量列表
- javascript - 将数据从父道具发送到子道具返回一个值,然后是未定义的功能组件
- javascript - Angular:无法读取 null 的属性(读取“cannotContainSpace”)
- typescript - 如何设置主 Vue 3 'app' 变量的 'type'
- node.js - 有人可以帮我做一个不和谐计数游戏吗
- python - 如何在 django 的某些视图中避免 THOUSAND SEPARATOR?
- python - `open` 不创建文件
- ado.net - 从 DataTable 到 DTO 的 AutoMapper
- c# - 从 ASP.NET MVC 操作方法返回文件 - 此请求已被阻止,因为敏感信息
- javascript - 如何在 JavaScript Annot3D PDF 中使 3D 对象移动?