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from typing import List, Tuple, Type
import torch
from torch import nn
from torch.nn import functional as F
from .common import LayerNorm2d
from .prompt_encoder import PositionEmbeddingRandom
from copy import deepcopy
class MaskDecoderMultiScale(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
image_feature_scale_num: int = 1,
) -> None:
"""
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
"""
super().__init__()
self.transformer_dim = transformer_dim
self.transformer = nn.ModuleList([deepcopy(transformer) for _ in range(image_feature_scale_num)])
self.num_multimask_outputs = num_multimask_outputs
self.iou_token = nn.Embedding(1, transformer_dim)
self.num_mask_tokens = num_multimask_outputs + 1
self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(
transformer_dim, transformer_dim // 8, kernel_size=2, stride=2
),
LayerNorm2d(transformer_dim // 8),
activation(),
)
self.upsample_2x = nn.Sequential(
nn.ConvTranspose2d(
transformer_dim, transformer_dim, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim),
activation(),)
self.pe1=PositionEmbeddingRandom(transformer_dim//2)
self.output_hypernetworks_mlps = nn.ModuleList(
[
MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
for i in range(self.num_mask_tokens)
]
)
self.iou_prediction_head = MLP(
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
)
self.image_feature_scale_num = image_feature_scale_num
self.level_embed = nn.Embedding(image_feature_scale_num, transformer_dim)
def forward(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
multimask_output: bool,
level_num: int,
previous_masks=None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
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.
Returns:
torch.Tensor: batched predicted masks
torch.Tensor: batched predictions of mask quality
"""
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
level_num=level_num,
previous_masks=previous_masks
)
if multimask_output:
mask_slice = slice(0, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, mask_slice, :, :]
iou_pred = iou_pred[:, mask_slice]
return masks, iou_pred
class Three_Level_Multi_Scale_Decoder(MaskDecoderMultiScale):
"""
Three-level multi-scale decoder.
修复了张量尺寸不匹配和硬编码通道数的问题
"""
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
image_feature_scale_num: int = 3,
) -> None:
if image_feature_scale_num != 3:
raise ValueError("Three_Level_Multi_Scale_Decoder 只支持恰好3个尺度")
super().__init__(
transformer_dim=transformer_dim,
transformer=transformer,
num_multimask_outputs=num_multimask_outputs,
activation=activation,
iou_head_depth=iou_head_depth,
iou_head_hidden_dim=iou_head_hidden_dim,
image_feature_scale_num=image_feature_scale_num,
)
def predict_masks(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
level_num: int,
previous_masks=None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Predicts masks. See 'forward' for more details."""
output_tokens = torch.cat(
[self.iou_token.weight, self.mask_tokens.weight], dim=0
)
output_tokens = output_tokens.unsqueeze(0).expand(
sparse_prompt_embeddings.size(0), -1, -1
)
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
level = torch.tensor([level_num, ], dtype=torch.long, device=tokens.device).expand((tokens.size(0), 1))
level_embed = self.level_embed(level)
tokens = tokens + level_embed
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
if level_num > 0:
src = self.upsample_2x(src)
b, c, h, w = src.shape
if previous_masks is not None:
previous_masks = torch.mean(previous_masks, dim=1)
if previous_masks.dim() == 3:
previous_masks = previous_masks.unsqueeze(1)
previous_masks = F.interpolate(
previous_masks.float(),
size=(h, w),
mode="bilinear",
align_corners=False
).to(previous_masks)
src = (torch.repeat_interleave(previous_masks, c, dim=1).sigmoid() + 1) * src
image_pe = self.pe1((h, w)).unsqueeze(0)
if dense_prompt_embeddings.shape[-2:] != (h, w):
dense_prompt_embeddings = F.interpolate(
dense_prompt_embeddings.float(),
size=(h, w),
mode="bilinear",
align_corners=False
).to(dense_prompt_embeddings)
src = src + dense_prompt_embeddings
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
b, c, h, w = src.shape
hs, src = self.transformer[level_num](src, pos_src, tokens)
iou_token_out = hs[:, 0, :]
mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
if src.dim() == 3:
src = src.transpose(1, 2)
N = src.shape[-1]
spatial_size = int(N**0.5)
if spatial_size * spatial_size != N:
raise ValueError(f"Cannot reshape {src.shape} to 4D spatial format")
src = src.view(b, c, spatial_size, spatial_size)
elif src.dim() == 4:
src = src.transpose(1, 2).view(b, c, h, w)
else:
raise ValueError(f"Unexpected src dimensions: {src.dim()}")
upscaled_embedding = self.output_upscaling(src)
hyper_in_list: List[torch.Tensor] = []
for i in range(self.num_mask_tokens):
hyper_in_list.append(
self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
)
hyper_in = torch.stack(hyper_in_list, dim=1)
b, c, h, w = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(
b, self.num_mask_tokens, h, w
)
iou_pred = self.iou_prediction_head(iou_token_out)
return masks, iou_pred
class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
)
self.sigmoid_output = sigmoid_output
def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
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