Описание: я пытаюсь запустить этот код, который предназначен для обнаружения модели оружия с помощью машинного обучения. я выполнил базовую настройку c, используя шаги из этой ссылки https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#configuring -a-training-pipe , и я поражен настройкой части обучающего конвейера.
and I faced the error when i run the below code. following is the error and later is the code
(labelimg) C:\Users\reddy\Tensorflow\workspace\training_demo>python model_main.py --alsologtostderr --model_dir=training/ --pipeline_config_path=training/ssd_inception_v2_coco.config
Traceback (most recent call last):
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\imp.py", line 242, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\imp.py", line 342, in load_dynamic
return _load(spec)
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: The specified module could not be found.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "model_main.py", line 23, in <module>
import tensorflow as tf
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\__init__.py", line 41, in <module>
from tensorflow.python.tools import module_util as _module_util
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\__init__.py", line 50, in <module>
from tensorflow.python import pywrap_tensorflow
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 69, in <module>
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\imp.py", line 242, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\reddy\Anaconda3\envs\labelimg\lib\imp.py", line 342, in load_dynamic
return _load(spec)
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: The specified module could not be found.
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
Ниже приведен код, который я пытаюсь запустить в командной строке anaconda # code:
# SSD with Inception v2 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
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,
}
}
override_base_feature_extractor_hyperparams: true
}
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: "C:\Users\reddy\Tensorflow\workspace\training_demo\pre-trained-model\ssd_inception_v2_coco_2018_01_28/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: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
}
train_input_reader: {
tf_record_input_reader {
input_path: "C:\Users\reddy\Tensorflow\workspace\training_demo\annotations\train.record"
}
label_map_path: "C:\Users\reddy\Tensorflow\workspace\training_demo\annotations\label_map.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
num_examples: 860
# 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: "C:\Users\reddy\Tensorflow\workspace\training_demo\annotations\train.record"
}
label_map_path: "C:\Users\reddy\Tensorflow\workspace\training_demo\annotations\label_map.pbtxt"
shuffle: false
num_readers: 1
}