Я хочу обучить мою модель обнаружения объектов тензорного потока, используя ssd_mobilenet_v2_coco_2018_03_29.when, когда я запускаю этап обучения:
python object_detection/train.py --logtostderr--train_dir=train
--pipeline_config_path=ssd_mobilenet_v2_coco_2018_03_29/pipeline.config
Он выдает
Traceback (most recent call last):
File "object_detection/train.py", line 198, in <module>
tf.app.run()
File "/home/user/tensor/lib/python3.5/site-
packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "object_detection/train.py", line 143, in main
model_config, train_config, input_config =
get_configs_from_pipeline_file()
File "object_detection/train.py", line 103, in
get_configs_from_pipeline_file
text_format.Merge(f.read(), pipeline_config)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 536, in Merge
descriptor_pool=descriptor_pool)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 590, in MergeLines
return parser.MergeLines(lines, message)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 623, in MergeLines
self._ParseOrMerge(lines, message)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 638, in _ParseOrMerge
self._MergeField(tokenizer, message)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 763, in _MergeField
merger(tokenizer, message, field)
File "/home/user/tensor/lib/python3.5/site-
packages/google/protobuf/text_format.py", line 837,in _
google.protobuf.text_format.ParseError: 80:7 : Message type
"object_detection.protos.SsdFeatureExtractor" has no field named
"use_depthwise".
Мой файл конфигурации:
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: 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
}
}
batch_norm {
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
}
}
}
}
feature_extractor {
type: 'ssd_mobilenet_v2'
min_depth: 16
depth_multiplier: 1.0
use_depthwise: true
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: 3
}
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: "ssd_mobilenet_v2_coco_2018_03_29/model.ckpt"
num_steps: 2000
fine_tune_checkpoint_type: "detection"
}
train_input_reader {
label_map_path: "ssd_mobilenet_v2_coco_2018_03_29/label_map.pbtxt"
tf_record_input_reader {
input_path: "data/train.record"
}
}
eval_config {
num_examples: 8000
max_evals: 10
use_moving_averages: false
}
eval_input_reader {
label_map_path: "ssd_mobilenet_v2_coco_2018_03_29/label_map.pbtxt"
shuffle: false
num_readers: 1
tf_record_input_reader {
input_path: "data/val.record"
}
}
Здесь я использую 2 класса.Я скопировал файл конфигурации из https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config моей версии Tensorflow: 1.12.0 protobuf: 3.6.1