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import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import List, Tuple, Type, Union
from .unet import Unet_decoder, Conv, TwoConv
from monai.networks.nets import UNet
class LayerNorm3d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None, None] * x + self.bias[:, None, None, None]
return x
class MaskDecoder3D(nn.Module):
def __init__(
self,
args,
*,
transformer_dim: int = 384,
multiple_outputs: bool = False,
num_multiple_outputs: int = 3,
) -> None:
super().__init__()
self.args = args
self.multiple_outputs = multiple_outputs
self.num_multiple_outputs = num_multiple_outputs
# if self.args.use_sam3d_turbo:
# self.output_hypernetworks_mlps = nn.ModuleList([MLP(transformer_dim, transformer_dim, 48, 3) for i in range(num_multiple_outputs + 1)])
# else:
self.output_hypernetworks_mlps = nn.ModuleList([MLP(transformer_dim, transformer_dim, 32, 3) for i in range(num_multiple_outputs + 1)])
self.iou_prediction_head = MLP(transformer_dim, 256, num_multiple_outputs + 1, 3, sigmoid_output=True)
self.decoder = Unet_decoder(spatial_dims=3, features=(32, 32, 64, 128, transformer_dim, 32))
if self.args.refine:
self.refine = Refine(self.args)
def forward(
self,
prompt_embeddings: torch.Tensor, # prompt_embedding --> [b, self.num_mask_tokens, c]
image_embeddings, # image_embedding --> [b, c, low_res / 4, low_res / 4, low_res / 4]
feature_list: List[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
upscaled_embedding = self.decoder(image_embeddings, feature_list)
masks, iou_pred = self._predict_mask(upscaled_embedding, prompt_embeddings)
return masks, iou_pred
def _predict_mask(self, upscaled_embedding, prompt_embeddings):
b, c, x, y, z = upscaled_embedding.shape
iou_token_out = prompt_embeddings[:, 0, :]
mask_tokens_out = prompt_embeddings[:, 1: (self.num_multiple_outputs + 1 + 1), :] # multiple masks + iou
hyper_in_list: List[torch.Tensor] = []
for i in range(self.num_multiple_outputs + 1):
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
hyper_in = torch.stack(hyper_in_list, dim=1)
masks = (hyper_in @ upscaled_embedding.view(b, c, x * y * z)).view(b, -1, x, y, z)
iou_pred = self.iou_prediction_head(iou_token_out)
if self.multiple_outputs:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, mask_slice, :, :]
iou_pred = iou_pred[:, mask_slice]
return masks, iou_pred
class Refine_unet(nn.Module):
def __init__(self):
super(Refine_unet, self).__init__()
self.refine = UNet(spatial_dims=3, in_channels=4, out_channels=1,
channels=(32, 64, 64), strides=(2, 2), num_res_units=2)
def forward(self, x):
return self.refine(x)
class Refine(nn.Module):
def __init__(self,
args,
spatial_dims: int = 3,
in_channel: int = 4,
out_channel: int = 32,
act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
norm: Union[str, tuple] = ("instance", {"affine": True}),
bias: bool = True,
dropout: Union[float, tuple] = 0.0,
):
super().__init__()
self.args = args
self.first_conv = Conv["conv", 3](in_channels=in_channel, out_channels=out_channel, kernel_size=1)
self.conv1 = TwoConv(spatial_dims, out_channel, out_channel, act, norm, bias, dropout)
self.conv2 = TwoConv(spatial_dims, out_channel, out_channel, act, norm, bias, dropout)
self.conv_error_map = Conv["conv", 3](in_channels=out_channel, out_channels=1, kernel_size=1)
self.conv_correction = Conv["conv", 3](in_channels=out_channel, out_channels=1, kernel_size=1)
def forward(self, image, mask_best, points, mask):
x = self._get_refine_input(image, mask_best, points)
mask = F.interpolate(mask, scale_factor=0.5, mode='trilinear', align_corners=False)
x = self.first_conv(x)
residual = x
x = self.conv1(x)
x = residual + x
residual = x
x = self.conv2(x)
x = residual + x
error_map = self.conv_error_map(x)
correction = self.conv_correction(x)
outputs = (error_map * correction + mask)
outputs = F.interpolate(outputs, scale_factor=2, mode='trilinear', align_corners=False)
error_map = F.interpolate(error_map, scale_factor=2, mode='trilinear', align_corners=False)
return outputs, error_map
def _get_refine_input(self, image, mask, points):
mask = torch.sigmoid(mask)
mask = (mask > 0.5)
coors, labels = points[0], points[1]
positive_map, negative_map = torch.zeros_like(image), torch.zeros_like(image)
for click_iters in range(len(coors)):
coors_click, labels_click = coors[click_iters], labels[click_iters]
for batch in range(image.size(0)):
point_label = labels_click[batch]
coor = coors_click[batch]
# sepehre_coor = [coor[:, 0], coor[:, 1], coor[:, 2]]
# Create boolean masks
negative_mask = point_label == 0
positive_mask = point_label != 0
# Update negative_map
if negative_mask.any(): # Check if there's at least one True in negative_mask
negative_indices = coor[negative_mask]
for idx in negative_indices:
negative_map[batch, 0, idx[0], idx[1], idx[2]] = 1
# Update positive_map
if positive_mask.any(): # Check if there's at least one True in negative_mask
positive_indices = coor[positive_mask]
for idx in positive_indices:
positive_map[batch, 0, idx[0], idx[1], idx[2]] = 1
refine_input = F.interpolate(torch.cat([image, mask, positive_map, negative_map], dim=1), scale_factor=0.5, mode='trilinear')
return refine_input
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 = torch.sigmoid(x)
return x
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