Spaces:
Configuration error
Configuration error
File size: 6,304 Bytes
c29babb | 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | import torch
from timm.layers import to_2tuple
from torch import nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
"""
A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
variance normalization over the channel dimension for inputs that have shape
(batch_size, channels, height, width).
https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa B950
"""
def __init__(self, normalized_shape, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.normalized_shape = (normalized_shape,)
def forward(self, x: 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] * x + self.bias[:, None, None]
return x
class MLP(nn.Module):
"""Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers, affine_func=nn.Linear):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(affine_func(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
def forward(self, x: torch.Tensor):
for i, layer in enumerate(self.layers):
# L R L R L
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
class Fusion(nn.Module):
def __init__(self, clip_dim, adapter_dim):
super().__init__()
self.clip_dim = clip_dim
self.adapter_dim = adapter_dim
self.proj = nn.Sequential(
LayerNorm(clip_dim),
nn.Conv2d(clip_dim, adapter_dim, kernel_size=1),
)
def forward(self, x, clip_x, spatial_shape):
h, w = spatial_shape
n, l, d = clip_x.shape
if l == h * w:
clip_x = clip_x.permute(0, 2, 1).view(n, d, h, w) # NLD->NDL->NDhw
else:
clip_x = clip_x.permute(0, 2, 1).view(n, d, 14, 14) # NLD->NDL->NDhw
clip_x = F.interpolate(
clip_x.contiguous(),
size=(16, 16),
mode="bilinear",
align_corners=False,
) # ND 14 14 => N D 16 16
clip_x = self.proj(clip_x).view(n, self.adapter_dim, h * w).permute(0, 2, 1)
x = x + clip_x # NLD
return x
class MaskPostXrayProcess(nn.Module):
def __init__(self, in_c):
super().__init__()
self.process = nn.Sequential(
nn.Conv2d(
in_channels=in_c, out_channels=in_c // 2, kernel_size=3, stride=1, padding=1
), # (N Q h,w)->(N 64 h,w))
nn.BatchNorm2d(in_c // 2),
nn.ReLU(),
nn.Conv2d(in_channels=in_c // 2, out_channels=in_c // 4, kernel_size=3, stride=1, padding=1), # (N 32 h,w)
nn.BatchNorm2d(in_c // 4),
nn.ReLU(),
nn.Conv2d(in_channels=in_c // 4, out_channels=1, kernel_size=1, stride=1, padding=0), # (N 16 h,w)
nn.ConvTranspose2d(in_channels=1, out_channels=1, kernel_size=16, stride=16), # (N 16 h,w)->(N 1 256 256)
# nn.Upsample(size=(256, 256), mode='bilinear', align_corners=True) # (N 1 256 256)
)
def forward(self, x, if_boundaries):
x = x.reshape(x.shape[0], x.shape[1], -1) # (N Q 256)
x = x.permute(0, 2, 1) # (N L Q)
if_boundaries = if_boundaries.unsqueeze(-1) # (NL1) 不是boundry的patch块置为0
x = x * if_boundaries # (N L Q) * (N L 1)
x = x.permute(0, 2, 1) # (N Q L)
x = x.reshape(x.shape[0], x.shape[1], 16, 16)
post_x = self.process(x) # (N 1 224 224)
return post_x
class PostClipProcess(nn.Module):
"""
NQD -> ND -> N2
"""
def __init__(self, num_quires, embed_dim):
super().__init__()
self.first_process = nn.Sequential(
nn.Conv1d(
in_channels=num_quires, out_channels=num_quires // 2, kernel_size=3, stride=1, padding=1
), # NQD->N1D
nn.BatchNorm1d(num_quires // 2),
nn.ReLU(),
nn.Conv1d(in_channels=num_quires // 2, out_channels=num_quires // 4, kernel_size=3, stride=1, padding=1),
nn.BatchNorm1d(num_quires // 4),
nn.ReLU(),
nn.Conv1d(in_channels=num_quires // 4, out_channels=1, kernel_size=3, stride=1, padding=1),
)
# self.norm = VT_LN(embed_dim)
self.second_process = nn.Sequential( # ND->N2
nn.Linear(in_features=embed_dim, out_features=embed_dim // 2),
nn.ReLU(),
nn.Linear(in_features=embed_dim // 2, out_features=embed_dim // 4),
nn.ReLU(),
# nn.Linear(in_features=embed_dim // 4, out_features=embed_dim // 8),
# nn.ReLU(),
nn.Linear(in_features=embed_dim // 4, out_features=2),
)
def forward(self, x):
x = self.first_process(x) # NQD->N1D
x = x.squeeze(1) # NQD->ND
x = self.second_process(x)
return x
class VT_LN(nn.LayerNorm):
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class PatchEmbed(nn.Module):
def __init__(self, img_size=256, patch_size=16, in_chans=3, embed_dim=192, norm_layer=None, bias=False, **kwargs):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = VT_LN(embed_dim)
def forward(self, x):
x = self.proj(x)
x = x.reshape(x.shape[0], x.shape[1], -1) # NDL
x = x.permute(0, 2, 1) # NDL->NLD
# x = self.norm(x)
return x
|