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fffea84 2a6b806 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 | 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 = torch.zeros([1,3,dim,dim])
img[0] = load_image_pil_accelerated(image_path, dim)
return img
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