File size: 10,249 Bytes
a8f4e0d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 |
#!/usr/bin/env python
# This model is part of the paper "ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data" (https://doi.org/10.1016/j.compbiomed.2022.105321)
# and has been published on GitHub: https://github.com/soumickmj/NCC1701/blob/main/Bridge/WarpDrives/ReconResNet/ReconResNet.py
import torch.nn as nn
from tricorder.torch.transforms import Interpolator
__author__ = "Soumick Chatterjee"
__copyright__ = "Copyright 2019, Soumick Chatterjee & OvGU:ESF:MEMoRIAL"
__credits__ = ["Soumick Chatterjee"]
__license__ = "apache-2.0"
__version__ = "1.0.0"
__email__ = "[email protected]"
__status__ = "Published"
class ResidualBlock(nn.Module):
def __init__(self, in_features, drop_prob=0.2):
super(ResidualBlock, self).__init__()
conv_block = [layer_pad(1),
layer_conv(in_features, in_features, 3),
layer_norm(in_features),
act_relu(),
layer_drop(p=drop_prob, inplace=True),
layer_pad(1),
layer_conv(in_features, in_features, 3),
layer_norm(in_features)]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class DownsamplingBlock(nn.Module):
def __init__(self, in_features, out_features):
super(DownsamplingBlock, self).__init__()
conv_block = [layer_conv(in_features, out_features, 3, stride=2, padding=1),
layer_norm(out_features),
act_relu()]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return self.conv_block(x)
class UpsamplingBlock(nn.Module):
def __init__(self, in_features, out_features, mode="convtrans", interpolator=None, post_interp_convtrans=False):
super(UpsamplingBlock, self).__init__()
self.interpolator = interpolator
self.mode = mode
self.post_interp_convtrans = post_interp_convtrans
if self.post_interp_convtrans:
self.post_conv = layer_conv(out_features, out_features, 1)
if mode == "convtrans":
conv_block = [layer_convtrans(
in_features, out_features, 3, stride=2, padding=1, output_padding=1), ]
else:
conv_block = [layer_pad(1),
layer_conv(in_features, out_features, 3), ]
conv_block += [layer_norm(out_features),
act_relu()]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x, out_shape=None):
if self.mode == "convtrans":
if self.post_interp_convtrans:
x = self.conv_block(x)
if x.shape[2:] != out_shape:
return self.post_conv(self.interpolator(x, out_shape))
else:
return x
else:
return self.conv_block(x)
else:
return self.conv_block(self.interpolator(x, out_shape))
class ReconResNetBase(nn.Module):
def __init__(self, in_channels=1, out_channels=1, res_blocks=14, starting_nfeatures=64, updown_blocks=2, is_relu_leaky=True, do_batchnorm=False, res_drop_prob=0.2,
is_replicatepad=0, out_act="sigmoid", forwardV=0, upinterp_algo='convtrans', post_interp_convtrans=False, is3D=False): # should use 14 as that gives number of trainable parameters close to number of possible pixel values in a image 256x256
super(ReconResNetBase, self).__init__()
layers = {}
if is3D:
layers["layer_conv"] = nn.Conv3d
layers["layer_convtrans"] = nn.ConvTranspose3d
if do_batchnorm:
layers["layer_norm"] = nn.BatchNorm3d
else:
layers["layer_norm"] = nn.InstanceNorm3d
layers["layer_drop"] = nn.Dropout3d
if is_replicatepad == 0:
layers["layer_pad"] = nn.ReflectionPad3d
elif is_replicatepad == 1:
layers["layer_pad"] = nn.ReplicationPad3d
layers["interp_mode"] = 'trilinear'
else:
layers["layer_conv"] = nn.Conv2d
layers["layer_convtrans"] = nn.ConvTranspose2d
if do_batchnorm:
layers["layer_norm"] = nn.BatchNorm2d
else:
layers["layer_norm"] = nn.InstanceNorm2d
layers["layer_drop"] = nn.Dropout2d
if is_replicatepad == 0:
layers["layer_pad"] = nn.ReflectionPad2d
elif is_replicatepad == 1:
layers["layer_pad"] = nn.ReplicationPad2d
layers["interp_mode"] = 'bilinear'
if is_relu_leaky:
layers["act_relu"] = nn.PReLU
else:
layers["act_relu"] = nn.ReLU
globals().update(layers)
self.forwardV = forwardV
self.upinterp_algo = upinterp_algo
interpolator = Interpolator(
mode=layers["interp_mode"] if self.upinterp_algo == "convtrans" else self.upinterp_algo)
# Initial convolution block
intialConv = [layer_pad(3),
layer_conv(in_channels, starting_nfeatures, 7),
layer_norm(starting_nfeatures),
act_relu()]
# Downsampling [need to save the shape for upsample]
downsam = []
in_features = starting_nfeatures
out_features = in_features*2
for _ in range(updown_blocks):
downsam.append(DownsamplingBlock(in_features, out_features))
in_features = out_features
out_features = in_features*2
# Residual blocks
resblocks = []
for _ in range(res_blocks):
resblocks += [ResidualBlock(in_features, res_drop_prob)]
# Upsampling
upsam = []
out_features = in_features//2
for _ in range(updown_blocks):
upsam.append(UpsamplingBlock(in_features, out_features,
self.upinterp_algo, interpolator, post_interp_convtrans))
in_features = out_features
out_features = in_features//2
# Output layer
finalconv = [layer_pad(3),
layer_conv(starting_nfeatures, out_channels, 7), ]
if out_act == "sigmoid":
finalconv += [nn.Sigmoid(), ]
elif out_act == "relu":
finalconv += [act_relu(), ]
elif out_act == "tanh":
finalconv += [nn.Tanh(), ]
self.intialConv = nn.Sequential(*intialConv)
self.downsam = nn.ModuleList(downsam)
self.resblocks = nn.Sequential(*resblocks)
self.upsam = nn.ModuleList(upsam)
self.finalconv = nn.Sequential(*finalconv)
if self.forwardV == 0:
self.forward = self.forwardV0
elif self.forwardV == 1:
self.forward = self.forwardV1
elif self.forwardV == 2:
self.forward = self.forwardV2
elif self.forwardV == 3:
self.forward = self.forwardV3
elif self.forwardV == 4:
self.forward = self.forwardV4
elif self.forwardV == 5:
self.forward = self.forwardV5
def forwardV0(self, x):
# v0: Original Version
x = self.intialConv(x)
shapes = []
for downblock in self.downsam:
shapes.append(x.shape[2:])
x = downblock(x)
x = self.resblocks(x)
for i, upblock in enumerate(self.upsam):
x = upblock(x, shapes[-1-i])
return self.finalconv(x)
def forwardV1(self, x):
# v1: input is added to the final output
out = self.intialConv(x)
shapes = []
for downblock in self.downsam:
shapes.append(out.shape[2:])
out = downblock(out)
out = self.resblocks(out)
for i, upblock in enumerate(self.upsam):
out = upblock(out, shapes[-1-i])
return x + self.finalconv(out)
def forwardV2(self, x):
# v2: residual of v1 + input to the residual blocks added back with the output
out = self.intialConv(x)
shapes = []
for downblock in self.downsam:
shapes.append(out.shape[2:])
out = downblock(out)
out = out + self.resblocks(out)
for i, upblock in enumerate(self.upsam):
out = upblock(out, shapes[-1-i])
return x + self.finalconv(out)
def forwardV3(self, x):
# v3: residual of v2 + input of the initial conv added back with the output
out = x + self.intialConv(x)
shapes = []
for downblock in self.downsam:
shapes.append(out.shape[2:])
out = downblock(out)
out = out + self.resblocks(out)
for i, upblock in enumerate(self.upsam):
out = upblock(out, shapes[-1-i])
return x + self.finalconv(out)
def forwardV4(self, x):
# v4: residual of v3 + output of the initial conv added back with the input of final conv
iniconv = x + self.intialConv(x)
shapes = []
if len(self.downsam) > 0:
for i, downblock in enumerate(self.downsam):
if i == 0:
shapes.append(iniconv.shape[2:])
out = downblock(iniconv)
else:
shapes.append(out.shape[2:])
out = downblock(out)
else:
out = iniconv
out = out + self.resblocks(out)
for i, upblock in enumerate(self.upsam):
out = upblock(out, shapes[-1-i])
out = iniconv + out
return x + self.finalconv(out)
def forwardV5(self, x):
# v5: residual of v4 + individual down blocks with individual up blocks
outs = [x + self.intialConv(x)]
shapes = []
for i, downblock in enumerate(self.downsam):
shapes.append(outs[-1].shape[2:])
outs.append(downblock(outs[-1]))
outs[-1] = outs[-1] + self.resblocks(outs[-1])
for i, upblock in enumerate(self.upsam):
outs[-1] = upblock(outs[-1], shapes[-1-i])
outs[-1] = outs[-2] + outs.pop()
return x + self.finalconv(outs.pop()) |