import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
from LSImP import LoadSave
import os, os.path
loadsave = LoadSave()
DIRO = 'AnomTest/Original'
DIRP = 'AnomTest/Predicted'
DIRS = 'AnomTest/Test'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
print('GPU')
else:
print('CPU')
criterion = torch.nn.L1Loss()
def wholePictureLossAnom():
loss = criterion(imageO, imageP)
return loss
def oneKernelPictureLossAnom():
for i in range(640):
for j in range(480):
loss = criterion(imageO[0][j][i], imageP[0][j][i])
if loss > 0.05:
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
from LSImP import LoadSave
import os, os.path
import random
ls = LoadSave()
DIRO = 'CombinedData/1'
DIRA = 'CombinedData/Anom'
DIRNA = 'CombinedData/NonAnom'
# for i in range(3603):
# t = ls.load_image(DIRO+'/output%06d.jpg' %(i+1))
# r = random.randint(0,1)
# if r == 0:
# ls.save_image(t, DIRNA+'/output%06d.jpg' %(i+1),480)
# else:
# ls.save_image(t, DIRA+'/output%06d.jpg' %(a+1),480)
def blah(i):
t = ls.load_image(DIRO + '/output%06d.jpg' % (i + 1))
r = random.randint(0, 802)
if r > 40:
ls.save_image(t, DIRNA + '/output%06d.jpg' % (i + 1), 480)
else:
ls.save_image(t, DIRA + '/output%06d.jpg' % (i + 1), 480)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from LSImP import LoadSave
import os, os.path
DIRA = 'AnomTest/Anom'
DIRNA = 'AnomTest/NonAnom'
DIRP = 'AnomTest/Predicted'
ls = LoadSave()
class NeuralNetworkCalculator(nn.Module):
def __init__(self):
super(NeuralNetworkCalculator, self).__init__()
self.layer_1 = nn.Conv3d(1,
1,
kernel_size=(1, 15, 15),
stride=(1, 5, 5)) #kernel = 5 padding = 2
self.layer_2 = nn.Conv3d(1,
1,
kernel_size=(1, 10, 10),
stride=(1, 3, 3)) #kernel = 5 padding = 2
self.layer_3 = nn.Conv3d(1, 1, kernel_size=(1, 7, 7),
stride=(1, 1, 1)) #kernel = 5 padding = 2
self.layer_4 = nn.Conv3d(1, 1, kernel_size=(1, 5, 9),
stride=(1, 1, 1)) #kernel = 5 padding = 2
self.layer_5 = nn.Conv3d(1, 1, kernel_size=(2, 5, 9),
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
from LSImP import LoadSave
import os, os.path
import wandb
import numpy as np
loadsave = LoadSave()
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
print('GPU')
else:
print('CPU')
# Hyper-parameters
sequence_length = 480
input_size = 640
hidden_size = 100
num_layers = 4
num_classes = 307200
batch_size = 50
num_epochs = 1
learning_rate = 0.00001
DIRT = 'LSTMData/Train'
DIRV = 'LSTMData/Validate'
OUTNAME = 'LSTM2C'