在训练 Tensorflow2 自定义对象检测器期间，我无法评估我的训练过程。在阅读了与这个问题相关的几个问题后，我发现评估和培训应该被视为两个独立的过程，因此我应该使用新的 anaconda 提示符来开始评估工作。我正在使用 ssd_mobilenetv2 640x640 版本进行培训。我的管道配置：
model {
ssd {
num_classes: 6
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
feature_extractor {
type: "ssd_mobilenet_v2_fpn_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
use_depthwise: true
override_base_feature_extractor_hyperparams: true
fpn {
min_level: 3
max_level: 7
additional_layer_depth: 128
}
}
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
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
depth: 128
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.599999904632568
share_prediction_tower: true
use_depthwise: true
}
}
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
scales_per_octave: 2
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.6000000238418579
max_detections_per_class: 100
max_total_detections: 100
use_static_shapes: false
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.25
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 4
data_augmentation_options {
random_horizontal_flip {
}
}
#data_augmentation_options {
#random_crop_image {
#min_object_covered: 0.0
#min_aspect_ratio: 0.75
#max_aspect_ratio: 3.0
#min_area: 0.75
#max_area: 1.0
#overlap_thresh: 0.0
#}
#}
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.04999999821186066
total_steps: 50000
warmup_learning_rate: 0.0026666000485420227
warmup_steps: 600
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "pre-trained-models\ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8\checkpoint\ckpt-0"
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
fine_tune_checkpoint_version: V2
from_detection_checkpoint: true
}
train_input_reader {
label_map_path: "annotations/label_map.pbtxt"
tf_record_input_reader {
input_path: "data/train.record"
}
}
eval_config {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "annotations/label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "data/test.record"
}
}
我已经使用以下命令开始了培训：
python model_main_tf2.py --model_dir=models/my_ssd2_3/ --pipeline_config_path=models/my_ssd2_3/pipeline.config --sample_1_of_n_eval_examples 1 --logtostderr
我希望设置评估示例的数量会对开始评估工作产生影响。在任何情况下，我都尝试在不同的终端窗口中运行评估：python model_main_tf2.py --model_dir=models/my_ssd2_3 --pipeline_config_path=models/my_ssd2_3/pipeline.config --checkpoint_dir=models/my_ssd2_3/ --alsologtostderr
一旦开始评估，训练作业就会因以下错误而崩溃：错误
我认为我没有足够的硬件的问题：
- 8GB 内存
- 英伟达 GTX960M（2GB 内存）
我使用的所有输入图像都是 3000x3000 是否是个问题，因此预处理器必须加载太多信息？如果是这样，有没有办法解决它？我不想在生成 TF 记录文件之前调整所有图像的大小，因为我必须重新标记所有图像。我显然缺乏在培训过程开始期间如何分配内存的洞察力，因此非常感谢一些细节。
第二个问题是，在 tensorboard 上监视训练期间，图像以各种亮度显示，我尝试在 model_lib_v2.py 文件中将 627 行更改为：
data= (features[fields.InputDataFields.image]-np.min(features[fields.InputDataFields.image]))/(np.max(features[fields.InputDataFields.image])-np.min(features[fields.InputDataFields.image]))
,
根据这个解决方案：https ://github.com/tensorflow/models/issues/9115 没有任何运气。这个问题有解决方案吗？如果我可以在那里监视模型提出的边界框，那将是很好的。谢谢你。