'Tensorflow Object Detection Api dont restore checkpoint to fine-tuning
I am trying to re-train EfficientDet D4, coming from Tensorflow Model Zoo (http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d4_coco17_tpu-32.tar.gz) on my dataset.
The tutorial describes that it might see a log like this when running model_main_tf2 to fine tune the model:
W0716 05:24:19.108539 1364 util.py:151] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
INFO:tensorflow:Step 100 per-step time 1.153s loss=0.761
I0716 05:26:55.879558 1364 model_lib_v2.py:632] Step 100 per-step time 1.153s loss=0.761
But I don't see it.
Am I doing something wrong when loading the pre-training checkpoint?
The configuration file I am using is the following:
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 8
add_background_class: false
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: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 1024
max_dimension: 1024
pad_to_max_dimension: true
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 224
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 4
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b4_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 7
num_filters: 224
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
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: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "/home/models/efd4/checkpoint/ckpt-0"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "detection"
batch_size: 1
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 2000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 1024
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.002
total_steps: 2000
warmup_learning_rate: .0001
warmup_steps: 500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "/home/labels/label_map.txt"
tf_record_input_reader {
input_path: "/home/records/train.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "/home/labels/label_map.txt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "/home/records/validation.tfrecord"
}
}
When I make use of model_main_tf2 to start training, no error appears. However, when I check the model accuracy, it does not detect anything.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.021
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.010
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002
I try to modify parameters like learning rate, the number of epochs, etc but doesn't work
To Fine-Tuning this model, I have followed the steps established in the following guide (https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html).
python /home/drive/MyDrive/VISDRONE/model_main_tf2.py \
--pipeline_config_path={pipeline_file} \
--model_dir={model_dir} \
--alsologtostderr \
--num_train_steps={num_steps} \
--sample_1_of_n_eval_examples=1 \
--num_eval_steps={num_eval_steps}
2022-03-24 14:56:39.530945: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0324 14:56:39.539781 140467518502784 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: 2000
I0324 14:56:39.543960 140467518502784 config_util.py:552] Maybe overwriting train_steps: 2000
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0324 14:56:39.544119 140467518502784 config_util.py:552] Maybe overwriting use_bfloat16: False
I0324 14:56:39.553249 140467518502784 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b4
I0324 14:56:39.553378 140467518502784 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 224
I0324 14:56:39.553517 140467518502784 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7
I0324 14:56:39.558310 140467518502784 efficientnet_model.py:144] round_filter input=32 output=48
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.580137 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.582051 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.584519 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.585638 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.592988 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.597373 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.603657 140467518502784 efficientnet_model.py:144] round_filter input=32 output=48
I0324 14:56:39.603788 140467518502784 efficientnet_model.py:144] round_filter input=16 output=24
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.619617 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.620819 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.623020 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.624058 140467518502784 cross_device_ops.py:618] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0324 14:56:39.829434 140467518502784 efficientnet_model.py:144] round_filter input=16 output=24
I0324 14:56:39.829590 140467518502784 efficientnet_model.py:144] round_filter input=24 output=32
I0324 14:56:40.442389 140467518502784 efficientnet_model.py:144] round_filter input=24 output=32
I0324 14:56:40.442584 140467518502784 efficientnet_model.py:144] round_filter input=40 output=56
I0324 14:56:41.058132 140467518502784 efficientnet_model.py:144] round_filter input=40 output=56
I0324 14:56:41.058324 140467518502784 efficientnet_model.py:144] round_filter input=80 output=112
I0324 14:56:41.971299 140467518502784 efficientnet_model.py:144] round_filter input=80 output=112
I0324 14:56:41.971578 140467518502784 efficientnet_model.py:144] round_filter input=112 output=160
I0324 14:56:42.896141 140467518502784 efficientnet_model.py:144] round_filter input=112 output=160
I0324 14:56:42.896331 140467518502784 efficientnet_model.py:144] round_filter input=192 output=272
I0324 14:56:44.146403 140467518502784 efficientnet_model.py:144] round_filter input=192 output=272
I0324 14:56:44.146590 140467518502784 efficientnet_model.py:144] round_filter input=320 output=448
I0324 14:56:44.446191 140467518502784 efficientnet_model.py:144] round_filter input=1280 output=1792
I0324 14:56:44.504505 140467518502784 efficientnet_model.py:454] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0324 14:56:44.738715 140467518502784 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['/content/drive/MyDrive/VISDRONE/train.record']
I0324 14:56:44.751177 140467518502784 dataset_builder.py:163] Reading unweighted datasets: ['/content/drive/MyDrive/VISDRONE/train.record']
INFO:tensorflow:Reading record datasets for input file: ['/content/drive/MyDrive/VISDRONE/train.record']
I0324 14:56:44.751728 140467518502784 dataset_builder.py:80] Reading record datasets for input file: ['/content/drive/MyDrive/VISDRONE/train.record']
INFO:tensorflow:Number of filenames to read: 1
I0324 14:56:44.751873 140467518502784 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0324 14:56:44.752046 140467518502784 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0324 14:56:44.754448 140467518502784 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0324 14:56:44.776529 140467518502784 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0324 14:56:49.483746 140467518502784 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0324 14:56:52.317593 140467518502784 deprecation.py:343] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
/usr/local/lib/python3.7/dist-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0324 14:57:59.473496 140462682519296 deprecation.py:547] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:616: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
W0324 14:58:17.434093 140462682519296 utils.py:80] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
W0324 14:58:42.918556 140462682519296 utils.py:80] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
W0324 14:59:06.517044 140462682519296 utils.py:80] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
W0324 14:59:31.055212 140462682519296 utils.py:80] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?
INFO:tensorflow:Step 100 per-step time 4.057s
I0324 15:04:44.796877 140467518502784 model_lib_v2.py:707] Step 100 per-step time 4.057s
INFO:tensorflow:{'Loss/classification_loss': 1.0777053,
'Loss/localization_loss': 0.71329135,
'Loss/regularization_loss': 0.048915524,
'Loss/total_loss': 1.8399122,
'learning_rate': 0.0002}
I0324 15:04:44.797298 140467518502784 model_lib_v2.py:708] {'Loss/classification_loss': 1.0777053,
'Loss/localization_loss': 0.71329135,
'Loss/regularization_loss': 0.048915524,
'Loss/total_loss': 1.8399122,
'learning_rate': 0.0002}
Solution 1:[1]
Try changing this part:
fine_tune_checkpoint_type: "detection"
to:
fine_tune_checkpoint_type: "full"
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | GGberry |