不确定这是否是错误（文件报告），或者我做错了什么。
系统信息：
Linux 17.04 TensorFlow版本： 1.9.0 Python版本： 2.7.13
我使用的命令：
gcloud ml-engine jobs submit training object_detection_$(date +%Y%m%d_%H%M%S) \
--job-dir="gs://mybucket/train" \
--packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
--module-name object_detection.train \
--region us-central1 \
--config /home/me/Desktop/die_detection/config.yml \
-- \
--train_dir="gs://mybucket/train" \
--pipeline_config_path="gs://mybucket/data/pipeline_cloud.config"
尝试了以下示例，但使用了我自己的数据：https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_pets.md
可以在本地正常运行。在CloudML Engine上运行，我得到一个非零的退出状态。从日志中似乎找不到object_detection.train。
源代码/日志
E The replica ps 0 exited with a non-zero status of 1. Termination reason: Error. To find out more about why your job exited please check the logs: https://console.cloud.google.com/logs/viewer?project=730275006403&resource=ml_job%2Fjob_id%2Fobject_detection_20180725_090524&advancedFilter=resource.type%3D%22ml_job%22%0Aresource.labels.job_id%3D%22object_detection_20180725_090524%22
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E ps-replica-0 Command '['python', '-m', u'object_detection.train', u'--train_dir=gs://mybucket/train', u'--pipeline_config_path=gs://mybucket/data/pipeline_cloud.config', '--job-dir', u'gs://mybucket/train']' returned non-zero exit status 1 ps-replica-0
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E ps-replica-0 /usr/bin/python: No module named object_detection.train ps-replica-0
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我的pipeline.config：
# SSD with Mobilenet v1, configured for Oxford-IIIT Pets 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: 1
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: 24
optimizer {
rms_prop_optimizer: {
learning_rate: {
exponential_decay_learning_rate {
initial_learning_rate: 0.0004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
num_steps: 20000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "gs://mybucket/data/train.record"
}
label_map_path: "gs://mybucket/data/object-detection.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
num_examples: 32
}
eval_input_reader: {
tf_record_input_reader {
input_path: "gs://mybucket/data/val.record""
}
label_map_path: "gs://mybucket/data/object-detection.pbtxt"
shuffle: false
num_readers: 1
}
我的config.yml
trainingInput:
runtimeVersion: "1.0"
scaleTier: CUSTOM
masterType: standard_gpu
workerCount: 1
workerType: standard_gpu
parameterServerCount: 1
parameterServerType: standard
答案 0 :(得分：4)
我假设您正在使用未经修改的对象检测示例。根据{{3}}，--module-name应该是object_detection.model_main而不是object_detection.train。您能否再次检查dist / object_detection-0.1.tar.gz文件？
答案 1 :(得分：0)
从train.py目录复制models\research\object_detection\legacy并粘贴到models\research\object_detection，将cd粘贴到models\research并运行以下cmd：python setup.py sdist 。
这样会在您的object_detection-0.1.tar.gz中创建一个新的models-master\research\dist，然后您可以再次运行命令：
gcloud ml-engine jobs submit training object_detection_$(date +%Y%m%d_%H%M%S) \
--job-dir="gs://mybucket/train" \
--packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \
--module-name object_detection.train \
--region us-central1 \
--config /home/me/Desktop/die_detection/config.yml \
-- \
--train_dir="gs://mybucket/train" \
--pipeline_config_path="gs://mybucket/data/pipeline_cloud.config"