Затем я пытаюсь тренировать модель с
python3 model_main.py —logtostderr —train_dir=training/ —pipelnie_config_path=training/ssd_mobilenet_v1_pets.config
Я получил следующую ошибку.Все конфигурации настроены.Сначала я попробовал это на Mac, и это сработало.Но учебный процесс занял так много времени на процессоре, что я решил перейти на облачные вычисления с использованием графического процессора (paperpace).Я сделал все точно так же и получил эту ошибку.Все файлы представлены.Что я мог сделать не так?Кажется, что-то не так с файлом конфигурации
Traceback (most recent call last):
File "model_main.py", line 109, in <module>
tf.app.run()
File "/home/paperspace/.local/lib/python3.6/site-
packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "model_main.py", line 71, in main
FLAGS.sample_1_of_n_eval_on_train_examples))
File "/home/paperspace/Desktop/models/research/object_detection/model_lib.py", line 589, in create_estimator_and_inputs
pipeline_config_path, config_override=config_override)
File "/home/paperspace/Desktop/models/research/object_detection/utils/config_util.py", line 97, in get_configs_from_pipeline_file
proto_str = f.read()
File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py", line 125, in read
self._preread_check()
File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py", line 85, in _preread_check
compat.as_bytes(self.__name), 1024 * 512, status)
File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/util/compat.py", line 61, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got None
Файл конфигурации:
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: 0.004
decay_steps: 800720
decay_factor: 0.95
}
}
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
}
}
fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
from_detection_checkpoint: true
load_all_detection_checkpoint_vars: 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: "data/train.record"
}
label_map_path: "data/label_map.pbtxt"
}
eval_config: {
metrics_set: "coco_detection_metrics"
num_examples: 1100
}
eval_input_reader: {
tf_record_input_reader {
input_path: "data/test.record"
}
label_map_path: "data/label_map.pbtxt"
shuffle: false
num_readers: 1
}