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5d1d4d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """TIPSv2 DPT head configuration."""
from transformers import PretrainedConfig
_BACKBONE_REPO_BY_HIDDEN_SIZE = {
768: "google/tipsv2-b14",
1024: "google/tipsv2-l14",
1152: "google/tipsv2-so400m14",
1536: "google/tipsv2-g14",
}
class TIPSv2DPTConfig(PretrainedConfig):
"""Configuration for TIPSv2 DPT dense prediction heads."""
model_type = "tipsv2_dpt"
def __init__(
self,
backbone_config=None,
neck_hidden_sizes=(96, 192, 384, 768),
fusion_hidden_size=256,
reassemble_factors=(4, 2, 1, 0.5),
readout_type="project",
num_depth_bins=256,
min_depth=1e-3,
max_depth=10.0,
**kwargs,
):
super().__init__(**kwargs)
backbone_config = backbone_config or {}
hidden_size = backbone_config.get("hidden_size", 768)
out_indices = backbone_config.get("out_indices", (3, 6, 9, 12))
self.backbone_repo = _BACKBONE_REPO_BY_HIDDEN_SIZE[hidden_size]
self.embed_dim = hidden_size
self.channels = fusion_hidden_size
self.post_process_channels = list(neck_hidden_sizes)
self.block_indices = [out_index - 1 for out_index in out_indices]
self.readout_type = readout_type
self.num_depth_bins = num_depth_bins
self.min_depth = min_depth
self.max_depth = max_depth
self.num_seg_classes = self.num_labels
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