From a7332f62309396bbc5212d3fc1db3d0e8f54ed79 Mon Sep 17 00:00:00 2001
From: Laurids Radtke <laurids.radtke@informatik.hu-berlin.de>
Date: Wed, 2 Jun 2021 21:50:33 +0000
Subject: [PATCH] Update Aufgabe 2: Spectrometer Alignment/Laurids
Radtke/spectrometer_alignment.py
---
.../Laurids Radtke/spectrometer_alignment.py | 97 +++++++++++++++++++
1 file changed, 97 insertions(+)
create mode 100644 Aufgabe 2: Spectrometer Alignment/Laurids Radtke/spectrometer_alignment.py
diff --git a/Aufgabe 2: Spectrometer Alignment/Laurids Radtke/spectrometer_alignment.py b/Aufgabe 2: Spectrometer Alignment/Laurids Radtke/spectrometer_alignment.py
new file mode 100644
index 0000000..2b1819b
--- /dev/null
+++ b/Aufgabe 2: Spectrometer Alignment/Laurids Radtke/spectrometer_alignment.py
@@ -0,0 +1,97 @@
+import h5py
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from sklearn.preprocessing import MinMaxScaler
+from torch.utils.data import Dataset, DataLoader
+from timeit import default_timer as timer
+
+class HDF5Dataset(Dataset):
+ def __init__(self, path, device, normalize):
+ sims = 50000 # only half the data
+ self.x = []
+ self.y = []
+ with h5py.File(path, 'r') as f:
+ for key in range(sims):
+ X = np.array(f['/' + str(key) + '/X'])
+ Y = np.array(f['/' + str(key) + '/Y'])
+ if(normalize):
+ X = MinMaxScaler().fit_transform(X) #normalise the data between 0 and 1
+ #Y = MinMaxScaler().fit_transform(Y)
+ #print(Y[:])
+ if np.amax(X) > 0: # ignore the blank images (no data)
+ self.x.append(X)
+ self.y.append(Y)
+
+ self.x = torch.tensor(self.x, dtype=torch.float32, device=device) # input
+ self.y = torch.tensor(self.y, dtype=torch.float32, device=device) # input
+
+ self.len = len(self.x)
+
+ # print('x.shape', np.shape(self.x))
+ # print('y.shape', np.shape(self.y))
+ # print(self.x.element_size() * self.x.nelement())
+
+ # number of rows in the dataset
+ def __len__(self):
+ return self.len
+
+ # get a row at an index
+ def __getitem__(self, index):
+ self.xc = self.x[index]
+ self.yc = self.y[index]
+ return self.xc, self.yc
+
+ #clips testdata from end of tensor
+ def get_splits(self, test_data_size):
+ train_x = self.x[:-test_data_size]
+ test_x = self.x[-test_data_size:]
+ train_y = self.y[:-test_data_size]
+ test_y = self.y[-test_data_size:]
+ train_data = [train_x, train_y]
+ test_data = [test_x, test_y]
+ return train_data, test_data
+
+def prepare_data(path, device, batch_size,test_data_size):
+ start = timer()
+ dataset = HDF5Dataset(path, device, normalize=True)
+ train_data, test_data = dataset.get_splits(test_data_size)
+ print("check")
+ end = timer()
+ print(end-start)
+ start = timer()
+ train_dl = DataLoader(train_data, batch_size=batch_size, shuffle=True, pin_memory=False)
+ test_dl = DataLoader(test_data, batch_size=batch_size, shuffle=False, pin_memory=False)
+ end = timer()
+ print(end-start)
+ return train_dl, test_dl, test_data
+
+#TODO anpassen an unsere Situation
+class Net(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(1,1,5) #1 channel input 1 channel output 5x5 kernel ??
+ self.pool = nn.MaxPool2d(5) #kernel size 5x5
+ self.conv2 = nn.Conv2d(1, 1, 5)
+ self.fc1 = nn.Linear(5 * 5, 120) #lin transformation
+ self.fc2 = nn.Linear(120, 84)
+ self.fc3 = nn.Linear(84, 3)
+
+ def forward(self, x):
+ x = self.pool(F.relu(self.conv1(x)))
+ x = self.pool(F.relu(self.conv2(x)))
+ x = torch.flatten(x, 1) # flatten all dimensions except batch
+ x = F.relu(self.fc1(x))
+ x = F.relu(self.fc2(x))
+ x = self.fc3(x)
+ return x
+
+
+path = "training_data_alignment.h5"
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+batch_size = 10
+test_data_size = 1000
+net = Net()
+#print(net)
+train_dl, test_dl, test_data = prepare_data(path, device, batch_size, test_data_size)
--
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