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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | import torch.nn as nn
import torch.nn.functional as F
import torch
# from nets.autoencoders.cvViT import CVViT
from .blocks.complexblock import CVConvNeXtBlock, ComplexDConvBlock, ComplexConv1x1Block
from .blocks.unetblock import UnetBasicBlock, UnetPrUpBlock
from .vit import ViT
class CVEncoder(nn.Module):
def __init__(self, in_channels=2, hidden_dims=None, use_max_pool=True, **kwargs):
super(CVEncoder, self).__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 = []
previous_dim = in_channels
# Build Encoder
for i, h_dim in enumerate(hidden_dims):
# For Encoder Part 1, use dilated conv blocks
if i < self.non_constant_depth:
# enc_block = ComplexDConvBlock(previous_dim, h_dim, kernel_size=3, stride=1, dilation=2**(self.non_constant_depth-i))
enc_block = ComplexDConvBlock(previous_dim, h_dim, kernel_size=3, stride=1, dilation=1)
# For Encoder Part 2, Channel-wise pooling with constant feature maps.
else:
enc_block = ComplexConv1x1Block(h_dim, h_dim * 2, kernel_size=3, dilation=1)
modules.append(enc_block)
previous_dim = h_dim
# Build Encoder
self.complex_encoder = nn.ModuleList(modules)
self.use_max_pool = use_max_pool
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 = []
for i, layer in enumerate(self.complex_encoder):
x = layer(x)
laterals.append(x)
if self.use_max_pool: # and i < self.non_constant_depth - 1: # Apply max pooling only to the non-constant part
x = F.max_pool2d(x, 2)
return x, laterals
class ViTUnetEncoder(nn.Module):
def __init__(self, in_channels=2, feature_size=[256, 256], patch_size=16, hidden_size=768, num_layers=4, mlp_ratio=4, num_heads=8, kernel_size=3, stride=1, **kwargs):
super(ViTUnetEncoder, 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
self.visual_transformer = ViT(
feature_size=feature_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=hidden_size,
mlp_ratio=mlp_ratio,
num_layers=num_layers,
num_heads=num_heads,
)
# self.visual_transformer = CVViT(
# feature_size=feature_size,
# patch_size=patch_size,
# in_channels=in_channels,
# embed_dim=hidden_size,
# mlp_ratio=mlp_ratio,
# num_layers=num_layers,
# num_heads=num_heads,
# )
self.complex_proj = nn.Conv2d(
in_channels=in_channels,
out_channels=2,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1)
)
self.inchannels = in_channels
self.encoder1 = UnetBasicBlock(in_channels=2, out_channels=token_size, kernel_size=3, stride=1, residual=True)
self.encoder2 = UnetPrUpBlock(in_channels=hidden_size, out_channels=token_size * 2, num_layers=2, kernel_size=kernel_size, stride=stride) # x2 -> 32
self.encoder3 = UnetPrUpBlock(in_channels=hidden_size, out_channels=token_size * 4, num_layers=1, kernel_size=kernel_size, stride=stride) # x4 -> 64
self.encoder4 = UnetPrUpBlock(in_channels=hidden_size, out_channels=token_size * 8, num_layers=0, kernel_size=kernel_size, stride=stride) # x8 -> 128
def proj_feat(self, x, hidden_size, token_size):
x = x.view(x.size(0), token_size, token_size, hidden_size) # [B T C] -> [B, token_size, token_size, hidden_size]
x = x.permute(0, 3, 1, 2).contiguous() # B C H W
return x
def forward(self, x_in, skip_connections=False):
x, hidden_states = self.visual_transformer(x_in) # [B, T, C]
residual = None
if skip_connections:
if self.inchannels != 2:
x_in = self.complex_proj(x_in) # Assume input is mag, convert to complex by adding a imaginary part
enc1 = self.encoder1(x_in)
x2 = hidden_states[self.num_layers // 4 * 1 -1]
enc2 = self.encoder2(self.proj_feat(x2, self.hidden_size, self.token_size))
x3 = hidden_states[self.num_layers // 4 * 2 -1]
enc3 = self.encoder3(self.proj_feat(x3, self.hidden_size, self.token_size))
x4 = hidden_states[self.num_layers // 4 * 3 -1]
enc4 = self.encoder4(self.proj_feat(x4, self.hidden_size, self.token_size))
residual = [enc1, enc2, enc3, enc4]
x = self.proj_feat(x, self.hidden_size, self.token_size) # [B, T, C] -> [B, C, H, W]
return x, residual
class CVConvNextEncoder(nn.Module):
def __init__(self,
hidden_dims=512,
intermediate_dim=1356,
num_layers=4,
complex_axis=1,
layer_scale_init_value=None,
**kwargs):
super(CVConvNextEncoder, self).__init__()
layer_scale_init_value = layer_scale_init_value or 1 / num_layers
self.blocks = nn.ModuleList(
[
CVConvNeXtBlock(
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
def forward(self, x):
laterals = []
for layer in self.blocks:
x = layer(x)
res = x.transpose(1, 2) # [B, C, T] -> [B, T, C]
laterals.append(res[:, 1:]) # Remove CLS token
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
x = x.transpose(1, 2) # [B, C, T] -> [B, T, C]
return x, laterals
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