configs/coco/panoptic_deeplab/resnet50_beta_os16.textproto

# proto-file: deeplab2/config.proto
# proto-message: ExperimentOptions
#
# Panoptic-DeepLab with ResNet-50-beta model variant and output stride 16.
#
############### PLEASE READ THIS BEFORE USING THIS CONFIG ###############
# Before using this config, you need to update the following fields:
# - experiment_name: Use a unique experiment name for each experiment.
# - initial_checkpoint: Update the path to the initial checkpoint.
# - train_dataset_options.file_pattern: Update the path to the
#   training set. e.g., your_dataset/train*.tfrecord
# - eval_dataset_options.file_pattern: Update the path to the
#   validation set, e.g., your_dataset/eval*.tfrecord
# - (optional) set merge_semantic_and_instance_with_tf_op: true, if you
#   could successfully compile the provided efficient merging operation
#   under the folder `tensorflow_ops`.
#########################################################################
#
# The `resnet50_beta` model variant replaces the first 7x7 convolutions in the
# original `resnet50` with three 3x3 convolutions, which is useful for dense
# prediction tasks.
#
# References:
# For resnet-50-beta, see
# https://github.com/tensorflow/models/blob/master/research/deeplab/core/resnet_v1_beta.py
# For Panoptic-DeepLab, see
# - Bowen Cheng, et al. "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline
#   for Bottom-Up Panoptic Segmentation." In CVPR, 2020.

# Use a unique experiment_name for each experiment.
experiment_name: "${EXPERIMENT_NAME}"
model_options {
  # Update the path to the initial checkpoint (e.g., ImageNet
  # pretrained checkpoint).
  initial_checkpoint: "${INIT_CHECKPOINT}"
  backbone {
    name: "resnet50_beta"
    output_stride: 16
  }
  decoder {
    feature_key: "res5"
    decoder_channels: 256
    aspp_channels: 256
    atrous_rates: 6
    atrous_rates: 12
    atrous_rates: 18
  }
  panoptic_deeplab {
    low_level {
      feature_key: "res3"
      channels_project: 64
    }
    low_level {
      feature_key: "res2"
      channels_project: 32
    }
    instance {
      low_level_override {
        feature_key: "res3"
        channels_project: 32
      }
      low_level_override {
        feature_key: "res2"
        channels_project: 16
      }
      instance_decoder_override {
        feature_key: "res5"
        decoder_channels: 128
        atrous_rates: 6
        atrous_rates: 12
        atrous_rates: 18
      }
      center_head {
        output_channels: 1
        head_channels: 32
      }
      regression_head {
        output_channels: 2
        head_channels: 32
      }
    }
    semantic_head {
      output_channels: 134
      head_channels: 256
    }
  }
}
trainer_options {
  save_checkpoints_steps: 1000
  save_summaries_steps: 100
  steps_per_loop: 100
  loss_options {
    semantic_loss {
      name: "softmax_cross_entropy"
      weight: 1.0
      top_k_percent: 0.2
    }
    center_loss {
      name: "mse"
      weight: 200
    }
    regression_loss {
      name: "l1"
      weight: 0.01
    }
  }
  solver_options {
    base_learning_rate: 0.0005
    training_number_of_steps: 200000
    warmup_steps: 2000
  }
}
train_dataset_options {
  dataset: "coco_panoptic"
  # Update the path to training set.
  file_pattern: "${TRAIN_SET}"
  # Adjust the batch_size accordingly to better fit your GPU/TPU memory.
  # Also see Q1 in g3doc/faq.md.
  batch_size: 64
  crop_size: 641
  crop_size: 641
  min_resize_value: 641
  max_resize_value: 641
  augmentations {
    min_scale_factor: 0.5
    max_scale_factor: 1.5
    scale_factor_step_size: 0.1
    autoaugment_policy_name: "simple_classification_policy_magnitude_scale_0.2"
  }
  increase_small_instance_weights: true
  small_instance_weight: 3.0
}
eval_dataset_options {
  dataset: "coco_panoptic"
  # Update the path to validation set.
  file_pattern: "${VAL_SET}"
  batch_size: 1
  crop_size: 641
  crop_size: 641
  min_resize_value: 641
  max_resize_value: 641
  # Add options to make the evaluation loss comparable to the training loss.
  increase_small_instance_weights: true
  small_instance_weight: 3.0
}
evaluator_options {
  continuous_eval_timeout: 43200
  stuff_area_limit: 4096
  center_score_threshold: 0.1
  nms_kernel: 41
  save_predictions: true
  save_raw_predictions: false
  # Use pure tf functions (i.e., no CUDA kernel) to merge semantic and
  # instance maps. For faster speed, compile TensorFlow with provided kernel
  # implementation under the folder `tensorflow_ops`, and set
  # merge_semantic_and_instance_with_tf_op to true.
  merge_semantic_and_instance_with_tf_op: false
}