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config_.py
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config_.py
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# Tempformer_model/pred_bias_matrix_vidvrd.npy
model_config = dict(
num_enti_cats = 36,
num_pred_cats = 133,
dim_ffn = 512,
dim_enti = 512,
dim_pred = 512,
dim_att = 512,
dim_feat = 1024, # dimension of each bbox's RoI feature, depend on the detector
dim_clsme = 300,
enco_pool_len = 4,
n_enco_layers = 2,
n_deco_layers = 6,
n_att_head = 8,
num_querys = 192,
neg_weight = 0.1,
positive_vIoU_th= 0.5,
EntiNameEmb_path= "prepared_data/vidvrd_EntiNameEmb.npy",
bias_matrix_path= "prepared_data/pred_bias_matrix_vidvrd.npy",
cost_coeff_dict = dict(
classification = 1.0,
adj_matrix = 30.0,
),
loss_coeff_dict = dict( # loss coefficient dictionary
classification = 1.0,
adj_matrix = 30.0,
)
)
train_dataset_config = dict(
split = "train",
ann_dir = "/home/gkf/project/VideoGraph/datasets/vidvrd-dataset",
proposal_dir = "/home/gkf/project/VideoGraph/proposals/vidvrd-dataset/miss30_minscore0p3/VidVRD_train_every1frames",
dim_boxfeature = 1024,
min_frames_th = 5,
max_proposal = 50,
max_preds = 100,
cache_tag = "MEGA"
)
test_dataset_config = dict(
split = "test",
ann_dir = "/home/gkf/project/VideoGraph/datasets/vidvrd-dataset",
proposal_dir = "/home/gkf/project/VideoGraph/proposals/vidvrd-dataset/miss30_minscore0p3/VidVRD_test_every1frames",
dim_boxfeature = 1024,
min_frames_th = 5,
max_proposal = 150,
max_preds = 100,
cache_tag = "MEGA"
)
train_config = dict(
batch_size = 8,
total_epoch = 120,
initial_lr = 1e-4,
lr_decay = 0.2,
epoch_lr_milestones = [80],
)
inference_config = dict(
topk = 10,
)
if __name__ == "__main__":
xx1 = "/home/gkf/VidSGG/tools/vidvrd_EntiNameEmb.npy"
xx2 = "tools/vidvrd_EntiNameEmb.npy"