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| 1 | +from sacred import Experiment, Ingredient |
| 2 | + |
| 3 | +faster_rcnn = Ingredient('faster_rcnn') |
| 4 | +train_hp = Ingredient('train_hp') |
| 5 | +valid_hp = Ingredient('valid_hp') |
| 6 | + |
| 7 | + |
| 8 | +@faster_rcnn.config |
| 9 | +def faster_rcnn_default(): |
| 10 | + network = 'resnet50_v1b' # base feature network |
| 11 | + dataset = 'voc' # dataset |
| 12 | + nms_thresh = 0.5 |
| 13 | + nms_topk = -1 |
| 14 | + post_nms = -1 |
| 15 | + roi_mode = 'align' |
| 16 | + roi_size = (7, 7) |
| 17 | + strides = (4, 8, 16, 32, 64) |
| 18 | + clip = 4.14 |
| 19 | + rpn_channel = 256 |
| 20 | + anchor_base_size = 16 |
| 21 | + anchor_aspect_ratio = (0.5, 1, 2) |
| 22 | + anchor_scales = (2, 4, 8, 16, 32) |
| 23 | + anchor_alloc_size = (384, 384) |
| 24 | + rpn_nms_thresh = 0.7 |
| 25 | + max_num_gt = 100 |
| 26 | + gpus = (0, 1, 2, 3, 4, 5, 6, 7) |
| 27 | + norm_layer = None |
| 28 | + use_fpn = True |
| 29 | + custom_model = True |
| 30 | + num_fpn_filters = 256 |
| 31 | + num_box_head_conv = 4 |
| 32 | + num_box_head_conv_filters = 256 |
| 33 | + num_box_head_dense_filters = 1024 |
| 34 | + image_short = 800 |
| 35 | + image_max_size = 1333 |
| 36 | + amp = False |
| 37 | + static_alloc = False |
| 38 | + |
| 39 | + |
| 40 | +@train_hp.config |
| 41 | +def train_cfg(): |
| 42 | + pretrained_base = True # whether load the imagenet pre-trained base |
| 43 | + batch_size = 16 |
| 44 | + start_epoch = 0 |
| 45 | + epochs = 26 |
| 46 | + lr = 0.01 # learning rate |
| 47 | + lr_decay = 0.1 # decay rate of learning rate. |
| 48 | + lr_decay_epoch = (20, 24) # epochs at which learning rate decays |
| 49 | + lr_mode = 'step' # learning rate scheduler mode. options are step, poly and cosine |
| 50 | + lr_warmup = 500 # number of iterations for warmup. |
| 51 | + lr_warmup_factor = 1. / 3. # starging lr warmup factor. |
| 52 | + momentum = 0.9 # momentum |
| 53 | + wd = 1e-4 # weight decay |
| 54 | + log_interval = 100 # log interval |
| 55 | + seed = 233 |
| 56 | + verbose = False |
| 57 | + mixup = False |
| 58 | + no_mixup_epochs = 20 |
| 59 | + rpn_smoothl1_rho = 0.001 |
| 60 | + rcnn_smoothl1_rho = 0.001 |
| 61 | + horovod = False |
| 62 | + no_pretrained_base = False |
| 63 | + rpn_train_pre_nms = 12000 |
| 64 | + rpn_train_post_nms = 2000 |
| 65 | + rpn_min_size = 1 |
| 66 | + rcnn_num_samples = 512 |
| 67 | + rcnn_pos_iou_thresh = 0.5 |
| 68 | + rcnn_pos_ratio = 0.25 |
| 69 | + executor_threads = 4 |
| 70 | + |
| 71 | + |
| 72 | +@valid_hp.config |
| 73 | +def valid_cfg(): |
| 74 | + rpn_test_pre_nms = 6000 |
| 75 | + rpn_test_post_nms = 1000 |
| 76 | + val_interval = 1 # Epoch interval for validation |
| 77 | + |
| 78 | + |
| 79 | +ex = Experiment('faster_rcnn_default', ingredients=[train_hp, valid_hp, faster_rcnn]) |
| 80 | + |
| 81 | + |
| 82 | +@ex.config |
| 83 | +def default_configs(): |
| 84 | + dataset = 'coco' |
| 85 | + resume = '' |
| 86 | + save_prefix = '' |
| 87 | + save_interval = 1 # save interval in epoch |
| 88 | + horovod = False |
| 89 | + num_workers = 16 |
| 90 | + kv_store = 'nccl' |
| 91 | + disable_hybridization = False |
| 92 | + |
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