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deeplab2 / configs /coco /panoptic_deeplab /resnet50_os32.textproto

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	# proto-file: deeplab2/config.proto
	# proto-message: ExperimentOptions
	#
	# Panoptic-DeepLab with ResNet-50 and output stride 32.
	#
	############### 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`.
	#########################################################################
	#
	# References:
	# For ResNet, see
	# - Kaiming He, et al. "Deep Residual Learning for Image Recognition."
	# In CVPR, 2016.
	# 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"
	output_stride: 32
	}
	decoder {
	feature_key: "res5"
	decoder_channels: 256
	aspp_channels: 256
	atrous_rates: 3
	atrous_rates: 6
	atrous_rates: 9
	}
	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: 3
	atrous_rates: 6
	atrous_rates: 9
	}
	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
	}