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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-06-18 01:24:59 +02:00
committed by GitHub
parent 21df296425
commit e78fb683f4
5 changed files with 118 additions and 123 deletions
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40
ultralytics/vit/sam/modules/decoders.py
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@ -22,20 +22,15 @@ class MaskDecoder(nn.Module):
iou_head_hidden_dim: int = 256,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
transformer architecture.
Predicts masks given an image and prompt embeddings, using a transformer architecture.
Arguments:
transformer_dim (int): the channel dimension of the transformer
transformer (nn.Module): the transformer used to predict masks
num_multimask_outputs (int): the number of masks to predict
when disambiguating masks
activation (nn.Module): the type of activation to use when
upscaling masks
iou_head_depth (int): the depth of the MLP used to predict
mask quality
iou_head_hidden_dim (int): the hidden dimension of the MLP
used to predict mask quality
transformer_dim (int): the channel dimension of the transformer module
transformer (nn.Module): the transformer used to predict masks
num_multimask_outputs (int): the number of masks to predict when disambiguating masks
activation (nn.Module): the type of activation to use when upscaling masks
iou_head_depth (int): the depth of the MLP used to predict mask quality
iou_head_hidden_dim (int): the hidden dimension of the MLP used to predict mask quality
"""
super().__init__()
self.transformer_dim = transformer_dim
@ -71,16 +66,15 @@ class MaskDecoder(nn.Module):
Predict masks given image and prompt embeddings.
Arguments:
image_embeddings (torch.Tensor): the embeddings from the image encoder
image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
multimask_output (bool): Whether to return multiple masks or a single
mask.
image_embeddings (torch.Tensor): the embeddings from the image encoder
image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
multimask_output (bool): Whether to return multiple masks or a single mask.
Returns:
torch.Tensor: batched predicted masks
torch.Tensor: batched predictions of mask quality
torch.Tensor: batched predicted masks
torch.Tensor: batched predictions of mask quality
"""
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
@ -136,9 +130,11 @@ class MaskDecoder(nn.Module):
return masks, iou_pred
# Lightly adapted from
# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
class MLP(nn.Module):
"""
Lightly adapted from
https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py
"""
def __init__(
self,

3
ultralytics/vit/utils/ops.py
View File

@ -249,7 +249,7 @@ def get_cdn_group(batch,
attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * 2 * i] = True
dn_meta = {
'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split([n for n in gt_groups], dim=1)],
'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
'dn_num_group': num_group,
'dn_num_split': [num_dn, num_queries]}
@ -258,5 +258,6 @@ def get_cdn_group(batch,
def inverse_sigmoid(x, eps=1e-6):
"""Inverse sigmoid function."""
x = x.clip(min=0., max=1.)
return torch.log(x / (1 - x + eps) + eps)