我正在使用Tensorflow Object Detection API重新训练mobilenet v1 SSD，我在Windows和Ubuntu环境中都遇到了这个特殊错误。 我的环境是带有python 3.6的Windows 10和tensorflow-cpu 1.5。我用protobuf 3.4.0编译了protobuf。 我做了安装测试，没关系，所以现在我正在尝试使用自己的数据集并收到以下错误：
WARNING:tensorflow:From C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\object_detection\trainer.py:257: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.create_global_step
Traceback (most recent call last):
File "train.py", line 167, in <module>
tf.app.run()
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\platform\app.py", line 124, in run
_sys.exit(main(argv))
File "train.py", line 163, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\object_detection\trainer.py", line 275, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\slim\deployment\model_deploy.py", line 193, in create_clones
outputs = model_fn(*args, **kwargs)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\object_detection\trainer.py", line 198, in _create_losses
prediction_dict = detection_model.predict(images, true_image_shapes)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\object_detection\meta_architectures\ssd_meta_arch.py", line 384, in predict
preprocessed_inputs)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\object_detection\models\ssd_mobilenet_v1_feature_extractor.py", line 121, in extract_features
scope=scope)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\models\research\slim\nets\mobilenet_v1.py", line 267, in mobilenet_v1_base
scope=end_point)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\framework\python\ops\arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py", line 1066, in convolution
outputs = normalizer_fn(outputs, **normalizer_params)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\framework\python\ops\arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py", line 667, in batch_norm
outputs = layer.apply(inputs, training=is_training)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 762, in apply
return self.__call__(inputs, *args, **kwargs)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\base.py", line 652, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\normalization.py", line 544, in call
training_value = utils.constant_value(training)
File "C:\Users\Barak\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\layers\utils.py", line 234, in constant_value
**raise TypeError('`pred` must be a Tensor, a Variable, or a Python bool.')
TypeError: `pred` must be a Tensor, a Variable, or a Python bool.**
我的配置文件与here
几乎相同# 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: 6
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: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
fine_tune_checkpoint: "models/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: 1000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "data/training_train.record"
}
label_map_path: "data/barak_label_map.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/training_eval.record"
}
label_map_path: "data/barak_label_map.pbtxt"
shuffle: false
num_readers: 1
}
答案 0 :(得分：0)
我遇到了同样的问题。似乎已经报告了错误并且存在解决方法。有关详细信息，请参阅以下链接：