seed: 123 use_amp: False warmup_steps: 1000 checkpoint: "/workspace/tao-experiments/mask_rcnn/pretrained_resnet50/pretrained_instance_segmentation_vresnet50/resnet50.hdf5" learning_rate_steps: "[40000, 60000, 80000]" learning_rate_decay_levels: "[0.1, 0.02, 0.002]" #total steps = total images * total epochs / batch size / nGpus total_steps: 90000 train_batch_size: 4 eval_batch_size: 4 num_steps_per_eval: 10000 momentum: 0.9 l2_weight_decay: 0.0001 warmup_learning_rate: 0.0001 #0.02=for 8GPUs #for 1 GPUs = 0.02/8 * 1 init_learning_rate: 0.02 data_config{ image_size: "(832, 1344)" augment_input_data: True eval_samples: 500 training_file_pattern: "/workspace/tao-experiments/data/mask_rcnn/data/train*.tfrecord" validation_file_pattern: "/workspace/tao-experiments/data/mask_rcnn/data/val*.tfrecord" val_json_file: "/workspace/tao-experiments/data/mask_rcnn/instances_shape_validation2020.json" # dataset specific parameters num_classes: 125 skip_crowd_during_training: True } maskrcnn_config { nlayers: 50 arch: "resnet" freeze_bn: True freeze_blocks: "[0,1]" gt_mask_size: 112 # Region Proposal Network rpn_positive_overlap: 0.7 rpn_negative_overlap: 0.3 rpn_batch_size_per_im: 256 rpn_fg_fraction: 0.5 rpn_min_size: 0. # Proposal layer. batch_size_per_im: 512 fg_fraction: 0.25 fg_thresh: 0.5 bg_thresh_hi: 0.5 bg_thresh_lo: 0. # Faster-RCNN heads. fast_rcnn_mlp_head_dim: 1024 bbox_reg_weights: "(10., 10., 5., 5.)" # Mask-RCNN heads. include_mask: True mrcnn_resolution: 28 # training train_rpn_pre_nms_topn: 2000 train_rpn_post_nms_topn: 1000 train_rpn_nms_threshold: 0.7 # evaluation test_detections_per_image: 100 test_nms: 0.5 test_rpn_pre_nms_topn: 1000 test_rpn_post_nms_topn: 1000 test_rpn_nms_thresh: 0.7 # model architecture min_level: 2 max_level: 6 num_scales: 1 aspect_ratios: "[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]" anchor_scale: 8 # localization loss rpn_box_loss_weight: 1.0 fast_rcnn_box_loss_weight: 1.0 mrcnn_weight_loss_mask: 1.0 }