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e8160b2 | 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 | import torch.nn as nn
import torch
from .blocks.complexblock import CVConvNeXtDBlock, ComplexUpsampleUnet, ComplexUpConstantUNet, ComplexLinearLayer
from .blocks.complexmodule import ComplexConv1D
from .blocks.unetblock import UnetUpBlock
class CVDecoder(nn.Module):
def __init__(self, hidden_dims: list = None,
**kwargs) -> None:
super().__init__()
if hidden_dims is None:
hidden_dims = [64, 128, 256, 512, 512, 512, 512]
self.non_constant_depth = self.count_non_constant_hidden_dims(hidden_dims)
self.constant_depth = len(hidden_dims) - self.non_constant_depth
modules = []
latent_dim = hidden_dims[-1]
pre_h_dim = latent_dim
# Build Decoder in reverse
for i in reversed(range(len(hidden_dims))):
h_dim = hidden_dims[i]
if i >= self.non_constant_depth: # For constant part, use constant upsample blocks
dec_block = ComplexUpConstantUNet(latent_dim, dilation=(1, 1), padding=(1, 1), output_padding=(1, 1))
else:
# pre_h_dim = hidden_dims[i+1]
dec_block = ComplexUpsampleUnet(pre_h_dim, h_dim, dilation=(1, 1), padding=(1, 1), output_padding=(1, 1))
pre_h_dim = h_dim
modules.append(dec_block)
# Adjusting lateral dimension
self.lateral_projection = ComplexLinearLayer(hidden_dims[-1], hidden_dims[-1]//2)
self.complex_decoder = nn.ModuleList(modules)
def count_non_constant_hidden_dims(self, hidden_dims):
count = 1
for i in range(1, len(hidden_dims)):
if hidden_dims[i] == hidden_dims[i-1]:
break
count += 1
return count
def forward(self, x, laterals=None):
# tem_up = []
for i, layer in enumerate(self.complex_decoder):
if laterals is not None:
residual = laterals[-i -1]
if i == self.constant_depth:
residual = self.lateral_projection(residual)
else:
residual = None
x = layer(x, residual)
# tem_up.append(x)
return x
class ViTUnetDecoder(nn.Module):
def __init__(self, feature_size=[256, 256], patch_size=16, hidden_size=768, num_layers=4, kernel_size=3, stride=1, **kwargs):
super(ViTUnetDecoder, self).__init__()
H, W = feature_size
assert H == W, "Currently only supports square feature maps"
token_size = H // patch_size # e.g., 256 // 16 = 16 tokens per side
self.hidden_size = hidden_size
self.token_size = token_size
self.num_layers = num_layers
# Decoder
self.decoder5 = UnetUpBlock(in_channels=hidden_size, out_channels=self.token_size * 8, kernel_size=kernel_size, stride=stride) # x8 -> 128
self.decoder4 = UnetUpBlock(in_channels=self.token_size * 8, out_channels=self.token_size * 4, kernel_size=kernel_size, stride=stride) # x4 -> 64
self.decoder3 = UnetUpBlock(in_channels=self.token_size * 4, out_channels=self.token_size * 2, kernel_size=kernel_size, stride=stride) # x2 -> 32
self.decoder2 = UnetUpBlock(in_channels=self.token_size * 2, out_channels=self.token_size, kernel_size=kernel_size, stride=stride) # x1 -> 16
# def proj_feat(self, x, hidden_size, token_size):
# x = x.view(x.size(0), token_size, token_size, hidden_size)
# x = x.permute(0, 3, 1, 2).contiguous() # B C H W
# return x
def forward(self, x, residuals=None):
dec4 = x
if residuals is not None:
dec3 = self.decoder5(dec4, residuals[-1]) # enc4
dec2 = self.decoder4(dec3, residuals[-2]) # enc3
dec1 = self.decoder3(dec2, residuals[-3]) # enc2
out = self.decoder2(dec1, residuals[-4]) # enc1
else:
dec3 = self.decoder5(dec4)
dec2 = self.decoder4(dec3)
dec1 = self.decoder3(dec2)
out = self.decoder2(dec1)
return out
class CVConvNextDecoder(nn.Module):
def __init__(self,
input_dims=256,
hidden_dims=512,
intermediate_dim=1356,
num_layers=4,
complex_axis=1,
layer_scale_init_value=None,
**kwargs):
super(CVConvNextDecoder, self).__init__()
layer_scale_init_value = layer_scale_init_value or 1 / num_layers
self.blocks = nn.ModuleList(
[
CVConvNeXtDBlock(
dim=hidden_dims,
intermediate_dim=intermediate_dim,
layer_scale_init_value=layer_scale_init_value,
complex_axis=complex_axis,
)
for _ in range(num_layers)
]
)
self.final_layer_norm = nn.LayerNorm(hidden_dims, eps=1e-6)
self.complex_axis = complex_axis
self.enc1 = ComplexConv1D(in_channels=input_dims, out_channels=hidden_dims, kernel_size=3, padding=1, complex_axis=1)
self.num_layers = num_layers
def forward(self, x, x_in=None, laterals=None):
if x_in is not None:
# inputs: [B, 2, F, T]
B, C, F, T = x_in.shape # C = 2
# [B, 2, F, T] -> [B, C, T]
x_in = x_in.reshape(B, C * F, T)
if laterals is not None:
enc1 = self.enc1(x_in)
enc2 = laterals[self.num_layers // 4 * 1 -1]
enc3 = laterals[self.num_layers // 4 * 2 -1]
enc4 = laterals[self.num_layers // 4 * 3 -1]
residuals = [enc1, enc2, enc3, enc4]
for i, layer in enumerate(self.blocks):
if laterals is not None:
residual = residuals[-i-1]
else:
residual = None
x = layer(x, residual)
real, imag = torch.chunk(x, 2, dim=self.complex_axis) # Split real and imaginary parts
real = self.final_layer_norm(real.transpose(1, 2)).transpose(1, 2) # Apply LayerNorm to real part
imag = self.final_layer_norm(imag.transpose(1, 2)).transpose(1, 2) # Apply LayerNorm to imaginary part
x = torch.cat([real, imag], dim=self.complex_axis) # Concatenate real and imaginary parts back together
return x
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