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pythontensorflow
sneeze_shiny
sneeze_shiny

Reputation: 328

TensorFlow - ValueError: Checkpoint version should be V2

I am training a Mask R-CNN Inception ResNet V2 1024x1024 algorithm (on my computer's GPU), as downloaded from the TensorFlow 2 Detection Model Zoo. I am training this algorithm on my custom dataset, which I have labeled using Label-img . When I train the model using the Anaconda command python model_main_tf2.py --model_dir=models/my_faster_rcnn --pipeline_config_path=models/my_faster_rcnn/pipeline.config, I get the following error:

Traceback (most recent call last):
  File "model_main_tf2.py", line 113, in <module>
    tf.compat.v1.app.run()
  File "C:\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "C:\user\anaconda3\envs\object_detection_api\lib\site-packages\absl\app.py", line 303, in run
    _run_main(main, args)
  File "C:\user\anaconda3\envs\object_detection_api\lib\site-packages\absl\app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "model_main_tf2.py", line 104, in main
    model_lib_v2.train_loop(
  File "C:\user\anaconda3\envs\object_detection_api\lib\site-packages\object_detection\model_lib_v2.py", line 564, in train_loop
    load_fine_tune_checkpoint(detection_model,
  File "C:\user\anaconda3\envs\object_detection_api\lib\site-packages\object_detection\model_lib_v2.py", line 348, in load_fine_tune_checkpoint
    raise ValueError('Checkpoint version should be V2')
ValueError: Checkpoint version should be V2

What is the code needed to resolve this error? (Below are some scripts referenced in the error):

model_main_tf2.py:

# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

r"""Creates and runs TF2 object detection models.

For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
NUM_TRAIN_STEPS=10000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
python model_main_tf2.py -- \
  --model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS \
  --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
  --pipeline_config_path=$PIPELINE_CONFIG_PATH \
  --alsologtostderr
"""
from absl import flags
import tensorflow.compat.v2 as tf
from object_detection import model_lib_v2

flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config '
                    'file.')
flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.')
flags.DEFINE_bool('eval_on_train_data', False, 'Enable evaluating on train '
                  'data (only supported in distributed training).')
flags.DEFINE_integer('sample_1_of_n_eval_examples', None, 'Will sample one of '
                     'every n eval input examples, where n is provided.')
flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample '
                     'one of every n train input examples for evaluation, '
                     'where n is provided. This is only used if '
                     '`eval_training_data` is True.')
flags.DEFINE_string(
    'model_dir', None, 'Path to output model directory '
                       'where event and checkpoint files will be written.')
flags.DEFINE_string(
    'checkpoint_dir', None, 'Path to directory holding a checkpoint.  If '
    '`checkpoint_dir` is provided, this binary operates in eval-only mode, '
    'writing resulting metrics to `model_dir`.')

flags.DEFINE_integer('eval_timeout', 3600, 'Number of seconds to wait for an'
                     'evaluation checkpoint before exiting.')

flags.DEFINE_bool('use_tpu', False, 'Whether the job is executing on a TPU.')
flags.DEFINE_string(
    'tpu_name',
    default=None,
    help='Name of the Cloud TPU for Cluster Resolvers.')
flags.DEFINE_integer(
    'num_workers', 1, 'When num_workers > 1, training uses '
    'MultiWorkerMirroredStrategy. When num_workers = 1 it uses '
    'MirroredStrategy.')
flags.DEFINE_integer(
    'checkpoint_every_n', 1000, 'Integer defining how often we checkpoint.')
flags.DEFINE_boolean('record_summaries', True,
                     ('Whether or not to record summaries during'
                      ' training.'))

FLAGS = flags.FLAGS


def main(unused_argv):
  flags.mark_flag_as_required('model_dir')
  flags.mark_flag_as_required('pipeline_config_path')
  tf.config.set_soft_device_placement(True)

  if FLAGS.checkpoint_dir:
    model_lib_v2.eval_continuously(
        pipeline_config_path=FLAGS.pipeline_config_path,
        model_dir=FLAGS.model_dir,
        train_steps=FLAGS.num_train_steps,
        sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
        sample_1_of_n_eval_on_train_examples=(
            FLAGS.sample_1_of_n_eval_on_train_examples),
        checkpoint_dir=FLAGS.checkpoint_dir,
        wait_interval=300, timeout=FLAGS.eval_timeout)
  else:
    if FLAGS.use_tpu:
      # TPU is automatically inferred if tpu_name is None and
      # we are running under cloud ai-platform.
      resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
          FLAGS.tpu_name)
      tf.config.experimental_connect_to_cluster(resolver)
      tf.tpu.experimental.initialize_tpu_system(resolver)
      strategy = tf.distribute.experimental.TPUStrategy(resolver)
    elif FLAGS.num_workers > 1:
      strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
    else:
      strategy = tf.compat.v2.distribute.MirroredStrategy()

    with strategy.scope():
      model_lib_v2.train_loop(
          pipeline_config_path=FLAGS.pipeline_config_path,
          model_dir=FLAGS.model_dir,
          train_steps=FLAGS.num_train_steps,
          use_tpu=FLAGS.use_tpu,
          checkpoint_every_n=FLAGS.checkpoint_every_n,
          record_summaries=FLAGS.record_summaries)

if __name__ == '__main__':
  tf.compat.v1.app.run()

pipeline.config file:

# Mask R-CNN with Inception Resnet v2 (no atrous)
# Sync-trained on COCO (with 8 GPUs) with batch size 16 (1024x1024 resolution)
# Initialized from Imagenet classification checkpoint
# TF2-Compatible, *Not* TPU-Compatible
#
# Achieves XXX mAP on COCO

model {
  faster_rcnn {
    number_of_stages: 3
    num_classes: 1
    image_resizer {
      fixed_shape_resizer {
        height: 1024
        width: 1024
        # pad_to_max_dimension: true
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_resnet_v2_keras'
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 16
        width_stride: 16
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 17
    maxpool_kernel_size: 1
    maxpool_stride: 1
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
        mask_height: 33
        mask_width: 33
        mask_prediction_conv_depth: 0
        mask_prediction_num_conv_layers: 4
        conv_hyperparams {
          op: CONV
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            truncated_normal_initializer {
              stddev: 0.01
            }
          }
        }
        predict_instance_masks: true
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
    second_stage_mask_prediction_loss_weight: 4.0
    resize_masks: false
  }
}

train_config: {
  batch_size: 1
  num_steps: 200000
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: 0.008
          total_steps: 200000
          warmup_learning_rate: 0.0
          warmup_steps: 5000
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "pre-trained-models/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8/checkpoint/ckpt-0"
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "annotations/train.record"
  }
  load_instance_masks: true
  mask_type: PNG_MASKS
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  metrics_set: "coco_mask_metrics"
  eval_instance_masks: true
  use_moving_averages: false
  batch_size: 1
  include_metrics_per_category: true
}

eval_input_reader: {
  label_map_path: "annotations/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "annotations/test.record"
  }
  load_instance_masks: true
  mask_type: PNG_MASKS
}

The rest of the python scripts referenced in the error can be found here, as they would not fit in a single StackOverflow post.

Upvotes: 2

Views: 3394

Answers (1)

B200011011
B200011011

Reputation: 4258

You may be missing fine_tune_checkpoint_version: V2 in train_config{}. Try custom modifications with this config below,

https://github.com/tensorflow/models/blob/6d6a78a259d4929b7f00d97aa5bbee7588463abd/research/object_detection/configs/tf2/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config#L124

Upvotes: 9

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