I am using TF2 for teeth classification using panoramic X-ray images. The images have been reduced to 640x640 and the annotations scaled accordingly.
The original image looks like this:
The test run went successful, confirming that the installation process is okay. Then the pre-built model was loaded to restore checkpoint as follows.
Then, the config file changed accordingly as follows:
When executing the training script, I get this error:
...
W0115 08:34:06.106962 139783846991616 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
Traceback (most recent call last):
File "/content/tensorflow-models/research/object_detection/model_main_tf2.py", line 114, in <module>
tf.compat.v1.app.run()
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/app.py", line 36, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 308, in run
_run_main(main, args)
File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 254, in _run_main
sys.exit(main(argv))
File "/content/tensorflow-models/research/object_detection/model_main_tf2.py", line 105, in main
model_lib_v2.train_loop(
File "/usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py", line 605, in train_loop
load_fine_tune_checkpoint(
File "/usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py", line 407, in load_fine_tune_checkpoint
ckpt.restore(
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/checkpoint/checkpoint.py", line 852, in assert_existing_objects_matched
raise AssertionError(
AssertionError: Found 265 Python objects that were not bound to checkpointed values, likely due to changes in the Python program. Showing 10 of 265 unmatched objects: [SyncOnReadVariable:{
0: <tf.Variable 'conv2_block1_3_bn/moving_variance:0' shape=(256,) dtype=float32, numpy=
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1.], dtype=float32)>
}, MirroredVariable:{
0: <tf.Variable 'conv4_block2_1_bn/gamma:0' shape=(256,) dtype=float32, numpy=
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
...
List of libraries and their versions: hdfs-2.7.0; keras-2.11.0; pyyaml-5.4.1; tensorboard-2.11.2; tensorflow-2.11.0
I tried this on Colab, the sequence of code looks something like:
!python object_detection/builders/model_builder_tf2_test.py
!python generate_tfrecord.py --csv_input=images/train/annotation.txt --image_dir=images/train --output_path=train.record
!python generate_tfrecord.py --csv_input=images/test/annotation.txt --image_dir=images/test --output_path=test.record
model_name = faster_rcnn_resnet50
!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/frcnn-resnet152-640
!tar -xf faster_rcnn_resnet50_v1_640x640_coco17_tpu-8.tar.tg
The config file:
num_classes = 6
batch_size = 8
num_steps = 100
num_eval_steps = 100
min_dimension, max_dimension = WIDTH, WIDTH
first_stage_nms_iou_threshold = 0.4
model_name = 'frcnn-resnet152-640'
base_pipeline_file = 'faster_rcnn_resnet50_v1_640x640_coco17_tpu-8.config'
pipeline_filename = '/content/tensorflow-models/research/object_detection/configs/tf2/' + base_pipeline_file
fine_tune_checkpoint = '/content/tensorflow-models/research/object_detection/' + model_name + '/checkpoint/ckpt-0'
# Read a sample config
with open(pipeline_filename) as f:
config = f.read()
with open('model_config.config', 'w') as f:
# Set labelmap path
config = re.sub('label_map_path: ".*?"',
'label_map_path: "labelmap.txt"', config)
# Set checkpoint path
config = re.sub('fine_tune_checkpoint: ".*?"',
'fine_tune_checkpoint: "{}"'.format(fine_tune_checkpoint), config)
# Set fine-tune checkpoint type to detection
#config = re.sub('fine_tune_checkpoint_type: "classification"',
# 'fine_tune_checkpoint_type: "{}"'.format('detection'), config)
config = re.sub('fine_tune_checkpoint_type: "detection"',
'fine_tune_checkpoint_type: "{}"'.format('classification'), config)
# Set train tf-record file path
config = re.sub('(input_path: ".*?)(PATH_TO_BE_CONFIGURED/train)(.*?")',
'input_path: "train.record"', config)
# Set test tf-record file path
config = re.sub('(input_path: ".*?)(PATH_TO_BE_CONFIGURED/val)(.*?")',
'input_path: "test.record"', config)
# Set number of classes.
config = re.sub('num_classes: [0-9]+',
'num_classes: {}'.format(num_classes), config)
# Set batch size
config = re.sub('batch_size: [0-9]+',
'batch_size: {}'.format(batch_size), config)
# Set training steps
config = re.sub('num_steps: [0-9]+',
'num_steps: {}'.format(num_steps), config)
# Set dimensions
config = re.sub('min_dimension: [0-9]+',
'min_dimension: {}'.format(max_dimension), config)
config = re.sub('max_dimension: [0-9]+',
'max_dimension: {}'.format(min_dimension), config)
# Set training steps
config = re.sub('first_stage_nms_iou_threshold: [0-9](.)[0-9]+',
'first_stage_nms_iou_threshold: {}'.format(first_stage_nms_iou_threshold), config)
f.write(config)
Training command:
model_dir = 'training/'
pipeline_config_path = 'model_config.config'
!python /content/tensorflow-models/research/object_detection/model_main_tf2.py \
--pipeline_config_path={pipeline_config_path} \
--model_dir={model_dir} \
--alsologtostderr \
--num_train_steps={num_steps} \
--sample_1_of_n_eval_examples=1 \
--num_eval_steps={num_eval_steps}
Can somebody help?
