我目前正在尝试使用tf对象检测API训练迁移学习模型。
我从Tensorflow的Model Zoo中选择了ssd_mobilenet_v2_mnasfpn_coco。
我安装了所有依赖项，并使用文档中的python文件对其进行了测试。
过去，我训练了一个不同的模型，该模型可以工作，但是在最终运行一个模型时遇到了一些麻烦。我在某个时候成功了，并且使用了与现在相同的命令集。
但是当我通过以下方式为我的mnaspfn模型运行训练/图形创建（用于将其转换为tflite模型）时：
python object_detection/legacy/train.py
python object_detection/export_tflite_ssd_graph.py
我收到以下错误/警告：
> WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089:
> Layer.apply (from tensorflow.python.keras.engine.base_layer) is
> deprecated and will be removed in a future version. Instructions for
> updating: Please use `layer.__call__` method instead. W0628
> 11:19:43.432508 140522432427904 deprecation.py:323] From
> /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089:
> Layer.apply (from tensorflow.python.keras.engine.base_layer) is
> deprecated and will be removed in a future version. Instructions for
> updating: Please use `layer.__call__` method instead.
> ERROR:tensorflow:C6: Tensor("FeatureExtractor/MnasFPN/Identity:0",
> shape=(1, 5, 5, 48), dtype=float32) E0628 11:19:45.003035
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396] C6:
> Tensor("FeatureExtractor/MnasFPN/Identity:0", shape=(1, 5, 5, 48),
> dtype=float32) ERROR:tensorflow:layer_1:
> Tensor("FeatureExtractor/MobilenetV2/Conv/Relu6:0", shape=(1, 160,
> 160, 32), dtype=float32) E0628 11:19:45.003337 140522432427904
> ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396] layer_1:
> Tensor("FeatureExtractor/MobilenetV2/Conv/Relu6:0", shape=(1, 160,
> 160, 32), dtype=float32) ERROR:tensorflow:layer_10:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/output:0",
> shape=(1, 20, 20, 64), dtype=float32) E0628 11:19:45.003476
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_10:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/output:0",
> shape=(1, 20, 20, 64), dtype=float32)
> ERROR:tensorflow:layer_10/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.003618
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_10/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_10/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.003799
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_10/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_10/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/output:0",
> shape=(1, 20, 20, 64), dtype=float32) E0628 11:19:45.003912
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_10/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_8/output:0",
> shape=(1, 20, 20, 64), dtype=float32) ERROR:tensorflow:layer_11:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/output:0",
> shape=(1, 20, 20, 64), dtype=float32) E0628 11:19:45.004024
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_11:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/output:0",
> shape=(1, 20, 20, 64), dtype=float32)
> ERROR:tensorflow:layer_11/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.004132
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_11/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_11/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.004241
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_11/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_11/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/output:0",
> shape=(1, 20, 20, 64), dtype=float32) E0628 11:19:45.004343
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_11/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_9/output:0",
> shape=(1, 20, 20, 64), dtype=float32) ERROR:tensorflow:layer_12:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.004450
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_12:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/output:0",
> shape=(1, 20, 20, 96), dtype=float32)
> ERROR:tensorflow:layer_12/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.004577
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_12/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_12/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.004697
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_12/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_12/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.004810
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_12/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_10/output:0",
> shape=(1, 20, 20, 96), dtype=float32) ERROR:tensorflow:layer_13:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.004933
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_13:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/output:0",
> shape=(1, 20, 20, 96), dtype=float32)
> ERROR:tensorflow:layer_13/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise_output:0",
> shape=(1, 20, 20, 576), dtype=float32) E0628 11:19:45.005046
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_13/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise_output:0",
> shape=(1, 20, 20, 576), dtype=float32)
> ERROR:tensorflow:layer_13/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32) E0628 11:19:45.005153
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_13/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32)
> ERROR:tensorflow:layer_13/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.005253
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_13/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_11/output:0",
> shape=(1, 20, 20, 96), dtype=float32) ERROR:tensorflow:layer_14:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.005365
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_14:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/output:0",
> shape=(1, 20, 20, 96), dtype=float32)
> ERROR:tensorflow:layer_14/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise_output:0",
> shape=(1, 20, 20, 576), dtype=float32) E0628 11:19:45.005474
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_14/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise_output:0",
> shape=(1, 20, 20, 576), dtype=float32)
> ERROR:tensorflow:layer_14/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32) E0628 11:19:45.005624
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_14/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32)
> ERROR:tensorflow:layer_14/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/output:0",
> shape=(1, 20, 20, 96), dtype=float32) E0628 11:19:45.005737
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_14/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_12/output:0",
> shape=(1, 20, 20, 96), dtype=float32) ERROR:tensorflow:layer_15:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/output:0",
> shape=(1, 10, 10, 160), dtype=float32) E0628 11:19:45.005845
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_15:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/output:0",
> shape=(1, 10, 10, 160), dtype=float32)
> ERROR:tensorflow:layer_15/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise_output:0",
> shape=(1, 10, 10, 576), dtype=float32) E0628 11:19:45.005954
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_15/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise_output:0",
> shape=(1, 10, 10, 576), dtype=float32)
> ERROR:tensorflow:layer_15/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32) E0628 11:19:45.006059
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_15/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_13/expansion_output:0",
> shape=(1, 20, 20, 576), dtype=float32)
> [...]
> ERROR:tensorflow:layer_9/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.103541
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_9/depthwise_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_9/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32) E0628 11:19:45.103683
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_9/expansion_output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/expansion_output:0",
> shape=(1, 20, 20, 384), dtype=float32)
> ERROR:tensorflow:layer_9/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/output:0",
> shape=(1, 20, 20, 64), dtype=float32) E0628 11:19:45.103810
> 140522432427904 ssd_mobilenet_v2_mnasfpn_feature_extractor.py:396]
> layer_9/output:
> Tensor("FeatureExtractor/MobilenetV2/expanded_conv_7/output:0",
> shape=(1, 20, 20, 64), dtype=float32) 2020-06-28 11:19:48.728455: I
> tensorflow/stream_executor/platform/default/dso_loader.cc:44]
> Successfully opened dynamic library libcuda.so.1 2020-06-28
> 11:19:48.732131: E tensorflow/stream_executor/cuda/cuda_driver.cc:318]
> failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is
> detected 2020-06-28 11:19:48.732181: I
> tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver
> does not appear to be running on this host (bf80455718a0):
> /proc/driver/nvidia/version does not exist 2020-06-28 11:19:48.732628:
> I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports
> instructions that this TensorFlow binary was not compiled to use: AVX2
> FMA 2020-06-28 11:19:48.740029: I
> tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:
> 2300000000 Hz 2020-06-28 11:19:48.740310: I
> tensorflow/compiler/xla/service/service.cc:168] XLA service 0x16e3640
> initialized for platform Host (this does not guarantee that XLA will
> be used). Devices: 2020-06-28 11:19:48.740347: I
> tensorflow/compiler/xla/service/service.cc:176] StreamExecutor
> device (0): Host, Default Version INFO:tensorflow:Found and fixed 12
> matches I0628 11:19:49.103435 140522432427904 exporter.py:92] Found
> and fixed 12 matches INFO:tensorflow:Found and fixed 0 matches I0628
> 11:19:49.138400 140522432427904 exporter.py:92] Found and fixed 0
> matches WARNING:tensorflow:From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127:
> checkpoint_exists (from
> tensorflow.python.training.checkpoint_management) is deprecated and
> will be removed in a future version. Instructions for updating: Use
> standard file APIs to check for files with this prefix. W0628
> 11:19:49.809565 140522432427904 deprecation.py:323] From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127:
> checkpoint_exists (from
> tensorflow.python.training.checkpoint_management) is deprecated and
> will be removed in a future version. Instructions for updating: Use
> standard file APIs to check for files with this prefix.
> INFO:tensorflow:Restoring parameters from /content/drive/My
> Drive/DeepSpace/models/mnasfpn/train/model.ckpt-100 I0628
> 11:19:50.725562 140522432427904 saver.py:1284] Restoring parameters
> from /content/drive/My
> Drive/DeepSpace/models/mnasfpn/train/model.ckpt-100
> WARNING:tensorflow:From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233:
> convert_variables_to_constants (from
> tensorflow.python.framework.graph_util_impl) is deprecated and will be
> removed in a future version. Instructions for updating: Use
> `tf.compat.v1.graph_util.convert_variables_to_constants` W0628
> 11:19:52.092961 140522432427904 deprecation.py:323] From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233:
> convert_variables_to_constants (from
> tensorflow.python.framework.graph_util_impl) is deprecated and will be
> removed in a future version. Instructions for updating: Use
> `tf.compat.v1.graph_util.convert_variables_to_constants`
> WARNING:tensorflow:From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277:
> extract_sub_graph (from tensorflow.python.framework.graph_util_impl)
> is deprecated and will be removed in a future version. Instructions
> for updating: Use `tf.compat.v1.graph_util.extract_sub_graph` W0628
> 11:19:52.093257 140522432427904 deprecation.py:323] From
> /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277:
> extract_sub_graph (from tensorflow.python.framework.graph_util_impl)
> is deprecated and will be removed in a future version. Instructions
> for updating: Use `tf.compat.v1.graph_util.extract_sub_graph`
> INFO:tensorflow:Froze 658 variables. I0628 11:19:52.769557
> 140522432427904 graph_util_impl.py:334] Froze 658 variables.
> INFO:tensorflow:Converted 658 variables to const ops. I0628
> 11:19:52.859092 140522432427904 graph_util_impl.py:394] Converted 658
> variables to const ops. 2020-06-28 11:19:53.012933: I
> tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying
> strip_unused_nodes
它不会终止我的跑步，并且该模型似乎仍然可以正常工作。但是结果是完全可怕的。我在导入模型时发现了一些错误，但是由于该错误并未告诉我实际上是什么错误，因此很难弄清。 （我删除了错误消息的某些行，因为字符数限制不允许更多，但仍应足够，因为错误似乎在整个过程中都是相同的）
我正在使用samples/config中的ssd_mobilenet_v2_mnasfpn_shared_box_predictor_320x320_coco_sync.config配置。我对其进行了修改以适合我的数据集并更改了标签路径等。
# SSD with MnasFPN feature extractor, shared box predictor
# See Chen et al, https://arxiv.org/abs/1912.01106
# Trained on COCO, initialized from scratch.
#
# 0.92B MulAdds, 2.5M Parameters. Latency is 193ms on Pixel 1.
# Achieves 26.6 mAP on COCO14 minival dataset.
# This config is TPU compatible
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 4
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 {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 6
anchor_scale: 3.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
fixed_shape_resizer {
height: 320
width: 320
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 64
class_prediction_bias_init: -4.6
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.997,
epsilon: 0.001,
}
}
num_layers_before_predictor: 4
share_prediction_tower: true
use_depthwise: true
kernel_size: 3
}
}
feature_extractor {
type: 'ssd_mobilenet_v2_mnasfpn'
fpn {
min_level: 3
max_level: 6
additional_layer_depth: 48
}
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.97,
epsilon: 0.001,
}
}
override_base_feature_extractor_hyperparams: true
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 2.0
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: 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: 1024
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 32
num_steps: 100 # TODO edit number steps here (maybe)
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: 4.
total_steps: 100 # TODO and here as well
warmup_learning_rate: .026666
warmup_steps: 50 # TODO and here asle
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint: "{PATH}"
from_detection_checkpoint: true
}
train_input_reader: {
tf_record_input_reader {
input_path: "{PATH}"
}
label_map_path: "{PATH}"
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
num_examples: 182
}
eval_input_reader: {
tf_record_input_reader {
input_path: "{PATH}"
}
label_map_path: "{PATH}"
shuffle: false
num_readers: 1
}
主要是我添加的：
fine_tune_checkpoint: "{PATH}"
from_detection_checkpoint: true
对于配置，我不确定这是否是您的操作方式，因为它可能会随着版本的变化而变化。 但是我在没有这些参数的情况下对其进行了测试，仍然收到了相同的错误消息，并且看到训练过程中的损失发生了巨大变化，因此从检查点加载的内容似乎在这里起作用，而这似乎不是问题。
我当前正在tf版本1.15上运行，因为API尚不支持2.2。这可能会引起问题，因为mnaspfn头是相当新的（约3个月），可能打算用于2.2。 但是，由于它仍然可以正常运行而没有任何导入问题，因此这似乎也不是问题。
我期待有关此问题的任何建议。