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fffea84 | 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 os, sys
import numpy as np
from PIL import Image
import itertools
import glob
import random
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.nn.functional import relu as RLU
registration_method = 'Additive_Recurence' #{'Rawblock', 'matching_points', 'Additive_Recurence', 'Multiplicative_Recurence'} #'recurrent_matrix',
imposed_point = 0
Arch = 'ResNet'
Fix_Torch_Wrap = False
BW_Position = False
dim = 128
dim0 =224
crop_ratio = dim/dim0
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class Build_IRmodel_Resnet(nn.Module):
def __init__(self, resnet_model, registration_method = 'Additive_Recurence', BW_Position=False):
super(Build_IRmodel_Resnet, self).__init__()
self.resnet_model = resnet_model
self.BW_Position = BW_Position
self.N_parameters = 6
self.registration_method = registration_method
self.fc1 =nn.Linear(6, 64)
self.fc2 =nn.Linear(64, 128*3)
self.fc3 =nn.Linear(512, self.N_parameters)
def forward(self, input_X_batch):
source = input_X_batch['source']
target = input_X_batch['target']
if 'Recurence' in self.registration_method:
M_i = input_X_batch['M_i'].view(-1, 6)
M_rep = F.relu(self.fc1(M_i))
M_rep = F.relu(self.fc2(M_rep)).view(-1,3,1,128)
concatenated_input = torch.cat((source,target,M_rep), dim=2)
else:
concatenated_input = torch.cat((source,target), dim=2)
resnet_output = self.resnet_model(concatenated_input)
predicted_line = self.fc3(resnet_output)
if 'Recurence' in self.registration_method:
predicted_part_mtrx = predicted_line.view(-1, 2, 3)
Prd_Affine_mtrx = predicted_part_mtrx + input_X_batch['M_i']
predction = {'predicted_part_mtrx':predicted_part_mtrx,
'Affine_mtrx': Prd_Affine_mtrx}
else:
Prd_Affine_mtrx = predicted_line.view(-1, 2, 3)
predction = {'Affine_mtrx': Prd_Affine_mtrx}
return predction
def pil_to_numpy(im):
im.load()
# Unpack data
e = Image._getencoder(im.mode, "raw", im.mode)
e.setimage(im.im)
# NumPy buffer for the result
shape, typestr = Image._conv_type_shape(im)
data = np.empty(shape, dtype=np.dtype(typestr))
mem = data.data.cast("B", (data.data.nbytes,))
bufsize, s, offset = 65536, 0, 0
while not s:
l, s, d = e.encode(bufsize)
mem[offset:offset + len(d)] = d
offset += len(d)
if s < 0:
raise RuntimeError("encoder error %d in tobytes" % s)
return data
def load_image_pil_accelerated(image_path, dim=128):
image = Image.open(image_path).convert("RGB")
array = pil_to_numpy(image)
tensor = torch.from_numpy(np.rollaxis(array,2,0)/255).to(torch.float32)
tensor = torchvision.transforms.Resize((dim,dim))(tensor)
return tensor
def preprocess_image(image_path, dim = 128):
img = load_image_pil_accelerated(image_path, dim)
return img.unsqueeze(0)
'''
def load_image_from_url(image_path, dim = 128):
img = Image.open(image_path).convert("RGB")
img = img.resize((dim, dim))
return img
def preprocess_image(image_path, dim = 128):
img = load_img(image_path, target_size=(dim, dim))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
return img
def create_model(dim = 128):
# configure unet input shape (concatenation of moving and fixed images)
volshape = (dim,dim,3)
unet_input_features = 2*volshape[:-1]
inshape = (*volshape[:-1],unet_input_features)
nb_conv_per_level=1
enc_nf = [dim, dim, dim, dim]
dec_nf = [dim, dim, dim, dim, dim, int(dim/2)]
nb_upsample_skips = 0
nb_dec_convs = len(enc_nf)
final_convs = dec_nf[nb_dec_convs:]
dec_nf = dec_nf[:nb_dec_convs]
nb_levels = int(nb_dec_convs / nb_conv_per_level) + 1
source = tf.keras.Input(shape=volshape, name='source_input')
target = tf.keras.Input(shape=volshape, name='target_input')
inputs = [source, target]
unet_input = concatenate(inputs, name='input_concat')
#Define lyers
ndims = len(unet_input.get_shape()) - 2
MaxPooling = getattr(tf.keras.layers, 'MaxPooling%dD' % ndims)
Conv = getattr(tf.keras.layers, 'Conv%dD' % ndims)
UpSampling = getattr(tf.keras.layers, 'UpSampling%dD' % ndims)
# Encoder
enc_layers = []
lyr = unet_input
for level in range(nb_levels - 1):
for conv in range(nb_conv_per_level):
nfeat = enc_nf[level * nb_conv_per_level + conv]
lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)
enc_layers.append(lyr)
lyr = MaxPooling(2)(lyr)
# Decoder
for level in range(nb_levels - 1):
real_level = nb_levels - level - 2
for conv in range(nb_conv_per_level):
nfeat = dec_nf[level * nb_conv_per_level + conv]
lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)
# upsample
if level < (nb_levels - 1 - nb_upsample_skips):
upsampled = UpSampling(size=(2,) * ndims)(lyr)
lyr = concatenate([upsampled, enc_layers.pop()])
# Final convolution
for num, nfeat in enumerate(final_convs):
lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)
unet = tf.keras.models.Model(inputs=inputs, outputs=lyr)
# transform the results into a flow field.
disp_tensor = Conv(ndims, kernel_size=3, padding='same', name='disp')(unet.output)
# using keras, we can easily form new models via tensor pointers
def_model = tf.keras.models.Model(inputs, disp_tensor)
# build transformer layer
spatial_transformer = SpatialTransformer()
# warp the moving image with the transformer
moved_image_tensor = spatial_transformer([source, disp_tensor])
outputs = [moved_image_tensor, disp_tensor]
vxm_model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
return vxm_model
'''
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