feat: extend the SwinB config with training parameters

groundingdino/config/GroundingDINO_SwinB_cfg.py

@@ -1,11 +1,188 @@
+data_aug_scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
+data_aug_max_size = 1333
+data_aug_scales2_resize = [400, 500, 600]
+data_aug_scales2_crop = [384, 600]
+data_aug_scale_overlap = None
+batch_size = 8
+modelname = 'groundingdino'
+backbone = 'swin_B_384_22k'
+position_embedding = 'sine'
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = 'standard'
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = 'relu'
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 91
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True
+max_labels = 50                               # pos + neg
+lr = 0.0001                                   # base learning rate
+backbone_freeze_keywords = None               # only for gdino backbone
+freeze_keywords = ['bert']                    # for whole model, e.g. ['backbone.0', 'bert'] for freeze visual encoder and text encoder
+lr_backbone = 1e-05                           # specific learning rate
+lr_backbone_names = ['backbone.0', 'bert']
+lr_linear_proj_mult = 1e-05
+lr_linear_proj_names = ['ref_point_head', 'sampling_offsets']
+weight_decay = 0.0001
+param_dict_type = 'ddetr_in_mmdet'
+ddetr_lr_param = False
+epochs = 15
+lr_drop = 4
+save_checkpoint_interval = 1
+clip_max_norm = 0.1
+onecyclelr = False
+multi_step_lr = False
+lr_drop_list = [4, 8]
+frozen_weights = None
+dilation = False
+pdetr3_bbox_embed_diff_each_layer = False
+pdetr3_refHW = -1
+random_refpoints_xy = False
+fix_refpoints_hw = -1
+dabdetr_yolo_like_anchor_update = False
+dabdetr_deformable_encoder = False
+dabdetr_deformable_decoder = False
+use_deformable_box_attn = False
+box_attn_type = 'roi_align'
+dec_layer_number = None
+decoder_layer_noise = False
+dln_xy_noise = 0.2
+dln_hw_noise = 0.2
+add_channel_attention = False
+add_pos_value = False
+two_stage_pat_embed = 0
+two_stage_add_query_num = 0
+two_stage_learn_wh = False
+two_stage_default_hw = 0.05
+two_stage_keep_all_tokens = False
+num_select = 300
+batch_norm_type = 'FrozenBatchNorm2d'
+masks = False
+aux_loss = True
+set_cost_class = 1.0
+set_cost_bbox = 5.0
+set_cost_giou = 2.0
+cls_loss_coef = 2.0
+bbox_loss_coef = 5.0
+giou_loss_coef = 2.0
+enc_loss_coef = 1.0
+interm_loss_coef = 1.0
+no_interm_box_loss = False
+mask_loss_coef = 1.0
+dice_loss_coef = 1.0
+focal_alpha = 0.25
+focal_gamma = 2.0
+decoder_sa_type = 'sa'
+matcher_type = 'HungarianMatcher'
+decoder_module_seq = ['sa', 'ca', 'ffn']
+nms_iou_threshold = -1
+dec_pred_class_embed_share = True
+
+
+match_unstable_error = True
+use_ema = False
+ema_decay = 0.9997
+ema_epoch = 0
+use_detached_boxes_dec_out = False
+use_coco_eval = False
+dn_scalar = 100
+label_list = ["car", "pedestrian"]
+
+# batch_size = 1
+# modelname = "groundingdino"
+# backbone = "swin_B_384_22k"
+# position_embedding = "sine"
+# pe_temperatureH = 20
+# pe_temperatureW = 20
+# return_interm_indices = [1, 2, 3]
+# backbone_freeze_keywords = None
+# enc_layers = 6
+# dec_layers = 6
+# pre_norm = False
+# dim_feedforward = 2048
+# hidden_dim = 256
+# dropout = 0.0
+# nheads = 8
+# num_queries = 900
+# query_dim = 4
+# num_patterns = 0
+# num_feature_levels = 4
+# enc_n_points = 4
+# dec_n_points = 4
+# two_stage_type = "standard"
+# two_stage_bbox_embed_share = False
+# two_stage_class_embed_share = False
+# transformer_activation = "relu"
+# dec_pred_bbox_embed_share = True
+# dn_box_noise_scale = 1.0
+# dn_label_noise_ratio = 0.5
+# dn_label_coef = 1.0
+# dn_bbox_coef = 1.0
+# embed_init_tgt = True
+# dn_labelbook_size = 2000
+# max_text_len = 256
+# text_encoder_type = "bert-base-uncased"
+# use_text_enhancer = True
+# use_fusion_layer = True
+# use_checkpoint = True
+# use_transformer_ckpt = True
+# use_text_cross_attention = True
+# text_dropout = 0.0
+# fusion_dropout = 0.0
+# fusion_droppath = 0.1
+# sub_sentence_present = True
+
+
+# new
+'''
+data_aug_scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
+data_aug_max_size = 1333
+data_aug_scales2_resize = [400, 500, 600]
+data_aug_scales2_crop = [384, 600]
+data_aug_scale_overlap = None
 batch_size = 1
+
 modelname = "groundingdino"
-backbone = "swin_B_384_22k"
+backbone = "swin_B_384_22k"         # <-- your Swin-B backbone
 position_embedding = "sine"
 pe_temperatureH = 20
 pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
+
+# Use the actual feature count of your backbone (try [0,1,2,3]; fall back to [0,1] if it crashes)
+return_interm_indices = [0, 1, 2, 3]
+num_feature_levels = 4
 backbone_freeze_keywords = None
+
 enc_layers = 6
 dec_layers = 6
 pre_norm = False
@@ -16,14 +193,15 @@
 num_queries = 900
 query_dim = 4
 num_patterns = 0
-num_feature_levels = 4
 enc_n_points = 4
 dec_n_points = 4
+
 two_stage_type = "standard"
 two_stage_bbox_embed_share = False
 two_stage_class_embed_share = False
 transformer_activation = "relu"
 dec_pred_bbox_embed_share = True
+
 dn_box_noise_scale = 1.0
 dn_label_noise_ratio = 0.5
 dn_label_coef = 1.0
@@ -41,3 +219,53 @@
 fusion_dropout = 0.0
 fusion_droppath = 0.1
 sub_sentence_present = True
+
+# optimization / training
+max_labels = 50
+lr = 0.0001
+lr_backbone = 1e-5
+lr_linear_proj_mult = 1e-5
+weight_decay = 0.0001
+param_dict_type = "ddetr_in_mmdet"
+ddetr_lr_param = False
+epochs = 15
+lr_drop = 4
+lr_drop_list = [4, 8]
+clip_max_norm = 0.1
+save_checkpoint_interval = 1
+onecyclelr = False
+multi_step_lr = False
+frozen_weights = None
+
+# losses / matcher
+matcher_type = "HungarianMatcher"
+set_cost_class = 1.0
+set_cost_bbox = 5.0
+set_cost_giou = 2.0
+cls_loss_coef = 2.0
+bbox_loss_coef = 5.0
+giou_loss_coef = 2.0
+enc_loss_coef = 1.0
+interm_loss_coef = 1.0
+focal_alpha = 0.25
+focal_gamma = 2.0
+aux_loss = True
+
+# postprocess / selection
+num_select = 300
+nms_iou_threshold = -1
+batch_norm_type = "FrozenBatchNorm2d"
+
+# extras
+masks = False
+no_interm_box_loss = False
+mask_loss_coef = 1.0
+dice_loss_coef = 1.0
+match_unstable_error = True
+use_ema = False
+ema_decay = 0.9997
+ema_epoch = 0
+use_detached_boxes_dec_out = False
+use_coco_eval = True
+dn_scalar = 100
+'''