-
Notifications
You must be signed in to change notification settings - Fork 0
/
Sinc_interpolation_MNIST.py
355 lines (274 loc) · 10.9 KB
/
Sinc_interpolation_MNIST.py
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
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import CIFAR10
from torchvision.datasets import MNIST
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
import resnet
from torchvision.transforms import transforms
from torchsummary import summary
from torchvision import transforms
import matplotlib.pyplot as plt
import tensorflow.keras as K
from knockknock import telegram_sender
from torchvision import datasets
x=1
CHAT_ID: int = 43515446
@telegram_sender(token="1834099231:AAEHY1G5pAGDRXH20vyNuP-WrfUqbc4f-X8", chat_id=CHAT_ID)
def train_your_nicest_model(your_nicest_parameters):
return {'loss': 0.9} # Optional return value
#### Import data from Keras
#(train_x,train_y), (test_x,test_y)=K.datasets.cifar10.load_data()
(train_x,train_y), (test_x,test_y)=K.datasets.mnist.load_data()
train_x=train_x/255.0
test_x=test_x/255.0
#manual normalization
# mean=[0.5, 0.5, 0.5]
# std=[0.25, 0.25, 0.25]
# mean=[0.4914, 0.4822, 0.4465]
# std=[0.2471, 0.2435, 0.2616]
mean=0
std=1
test_x=(test_x-mean)/std
############ model and data selection ##########
model=resnet.ResNet18()
PATH='../base_model_trained_files/mnist/resnet18/model.t7'
model.load_state_dict(torch.load(PATH))
model.eval()
transform = transforms.Compose([
#transforms.Resize(256),
#transforms.RandomCrop(224),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.25, 0.25, 0.25))
#transforms.Normalize((0.5), (0.5))
#transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2471, 0.2435, 0.2616))
])
# dataset = CIFAR10(root='data/', download=True, transform=ToTensor())
# test_dataset = CIFAR10(root='data/', train=False, transform=ToTensor())
#dataset = CIFAR10(root='data/', download=True, transform=transform)
#test_dataset = CIFAR10(root='data/', train=False, transform=transform)
#test_dataset = MNIST(root='data/', train=False, transform=transform,download=True)
test_dataset = MNIST(root='data/', train=False, transform=transform,download=True)
batch_size=128
# train_loader = DataLoader(train_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True)
# val_loader = DataLoader(val_ds, batch_size, num_workers=0, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size, num_workers=0, pin_memory=True)
# classes = ('plane', 'car', 'bird', 'cat',
# 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
def model_accuracy_pytorch():
correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
for data in test_loader:
images, labels = data
# calculate outputs by running images through the network
outputs = model(images)
# the class with the highest energy is what we choose as prediction
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
correct/total ))
print(correct, total)
def model_accuracy():
n = len(test_x)
predictions = np.zeros([n, 1])
# trans = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2471, 0.2435, 0.2616])
for i in range(n):
data = test_x[i,]
torch_sample = torch.from_numpy(data).float()
# torch_sample = torch_sample.unsqueeze(0)
#torch_sample = torch_sample.permute(2, 0, 1)
# torch_sample = trans(torch_sample)
torch_sample = torch_sample.unsqueeze(0)
torch_sample = torch_sample.unsqueeze(0)
pred = model(torch_sample)
_, predicts = torch.max(pred, 1)
predictions[i] = predicts.numpy()[0]
#diff = test_y - predictions
test_y_reshaped = np.reshape(test_y, [n, 1])
diff=test_y_reshaped-predictions
print("Model accuracy is")
out = (n - np.count_nonzero(diff))/n
print(out)
print(n-np.count_nonzero(diff))
return out
shift=0
#Sinc Encoder
def encoder(X,N):
[K,H,W]=np.shape(X)
alpha=np.arange(0,K,1)
S=N-K
#all_z = np.arange(-np.floor(S / 2)+shift, K + np.ceil(S / 2) +shift, 1)
all_z = np.arange(0, K, K/N)-1+K/N
coded_X=np.zeros([N,H,W])
for n in range(N):
z=all_z[n]
for i in range(K):
coded_X[n,]=coded_X[n,]+np.sinc(z-alpha[i])*X[i,]
return coded_X
#Sinc Decoder
def decoder(Y,K,N,returned_points_indices):
F=len(returned_points_indices)
alpha = np.arange(0, K, 1)
S = N - K
#all_z = np.arange(-np.floor(S / 2)+shift, K + np.ceil(S / 2) + shift, 1)
all_z = np.arange(0, K, K/N)-1+K/N
# step=1
# all_z=np.arange(start=-np.floor(S / 2) + step/2,stop=-np.floor(S / 2) + (N+1)*step/2,step=step)
# print(len(all_z))
probs=np.zeros([K,10])
for digit in range(10):
for i in range(K):
z=alpha[i]
for l in range(F):
probs[i,digit] = probs[i,digit] + np.sinc(z - all_z[returned_points_indices[l]])*Y[returned_points_indices[l],digit]
return probs
def model_out(Y):
n=len(Y)
outputs=np.zeros([n,10])
#trans = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2471, 0.2435, 0.2616])
for i in range(n):
data=Y[i,]
torch_sample = torch.from_numpy(data).float()
#torch_sample = torch_sample.unsqueeze(0)
#torch_sample = torch_sample.permute( 2, 0, 1)
#torch_sample = trans(torch_sample)
torch_sample = torch_sample.unsqueeze(0)
torch_sample = torch_sample.unsqueeze(0)
outputs[i,]= model(torch_sample).detach().numpy()[0]
# _, predicts = torch.max(pred, 1)
# predictions[i]=predicts.numpy()[0]
return outputs
def Determine_accuracy(input_batch_ids):
input_batch=test_x[input_batch_ids]
n=len(input_batch)
predictions=np.zeros([n,1])
#trans = transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2471, 0.2435, 0.2616])
for i in range(n):
data=input_batch[i,]
torch_sample = torch.from_numpy(data).float()
#torch_sample = torch_sample.unsqueeze(0)
#torch_sample = torch_sample.permute( 2, 0, 1)
#torch_sample = trans(torch_sample)
torch_sample = torch_sample.unsqueeze(0)
torch_sample = torch_sample.unsqueeze(0)
pred = model(torch_sample)
_, predicts = torch.max(pred, 1)
predictions[i]=predicts.numpy()[0]
test_y_samples=test_y[input_batch_ids]
test_y_samples_reshaped=np.reshape(test_y_samples, [n, 1])
diff=test_y_samples_reshaped-predictions
# print("Accuracy is")
out=(n-np.count_nonzero(diff))
return out
def Acc_Comparison(K, N, S,T, iterations):
Berrut_Accuracy = 0
Centralized_accuracy = 0
for i in range(iterations):
# Random data
shuffled_indices=np.random.permutation(test_x.shape[0])
random_indices = shuffled_indices[0:K]
random_indices=np.sort(random_indices)
test_sample_x = test_x[random_indices]
True_labels = test_y[random_indices]
#dataset sweep
# Centralized Accuracy
# centralized_test_sample_x = tf.expand_dims(test_sample_x, 3)
# probs_centralized = new_model.predict(centralized_test_sample_x)
# centralized_predictions = np.argmax(probs_centralized, axis=1)
Single_Centralized_Accuracy=Determine_accuracy(random_indices)
Centralized_accuracy = Centralized_accuracy +Single_Centralized_Accuracy / K
# Distributed Inference
# encoding test data
coded_test_sample_x = encoder(test_sample_x, N)
# train_x=tf.expand_dims(train_x,3)
# test_x=tf.expand_dims(test_x,3)
model_outputs=model_out(coded_test_sample_x)
## Determining stragglers' indices ####
returned_points_indices = np.random.permutation(N)
returned_points_indices = returned_points_indices[0:N - S]
returned_points_indices = np.sort(returned_points_indices)
# returned_points_indices=range(N-S)
# returned_points_indices=range(N)
test_sample_out_value = decoder(model_outputs, K, N, returned_points_indices)
#print(test_sample_out_value.shape)
Berrut_predictions = np.argmax(test_sample_out_value, axis=1)
# Perfomance Evaluation
#True_labels = test_sample_y
Berrut_predictions=Berrut_predictions.reshape(K, 1)
True_labels_reshaped=np.reshape(True_labels,[len(True_labels),1])
Berrut_Accuracy = Berrut_Accuracy + np.count_nonzero(Berrut_predictions - True_labels_reshaped) / K
print("%" + str((i + 1) * 100 / iterations) + "completed")
### for degraded mode systematic
# Berrut_predictions = Berrut_predictions.reshape(S, 1)
# True_labels_reshaped=np.reshape(True_labels,[len(True_labels),1])
# Berrut_Accuracy = Berrut_Accuracy + np.count_nonzero(Berrut_predictions - True_labels_reshaped[0:S]) / S
# print("%" + str((i + 1) * 100 / iterations) + "completed")
return 1 - Berrut_Accuracy / iterations, Centralized_accuracy / iterations
def Plot_N():
K=10
S=1
#N=np.arange(43,81,2)
N=[11,13,14,15,17]
#N=[5,7,9]
Berrut_acc=np.zeros(len(N))
Center_acc=np.zeros(len(N))
num_of_iterations=1
for i in range(len(N)):
print("N="+str(N[i]))
a,b=Acc_Comparison(K,N[i],S,num_of_iterations)
Berrut_acc[i]=a
Center_acc[i]=b
np.savetxt("Berrut_acc.txt", Berrut_acc)
np.savetxt("Center_acc.txt", Center_acc)
np.savetxt("N.txt", N)
plt.plot(N, Berrut_acc, label="Berrut")
plt.plot(N, Center_acc, label="Centralized")
plt.legend(["Berrut", "Centralized"])
plt.xlabel("N")
plt.ylabel("Accuracy")
plt.title('K=,'+str(K)+' S=' + str(S) + ', Num_of_Iterations=' + str(num_of_iterations))
plt.show()
# Plot vs K
def Plot_K():
K=np.arange(10,14,2)
S=1
T=0
Berrut_acc=np.zeros(len(K))
Center_acc=np.zeros(len(K))
num_of_iterations=100
for i in range(len(K)):
print("K="+str(K[i]))
a,b=Acc_Comparison(K[i],K[i]+S+T,S,T,num_of_iterations)
Berrut_acc[i]=a
Center_acc[i]=b
# train_your_nicest_model(1)
np.savetxt("Berrut_acc.txt",Berrut_acc)
np.savetxt("Center_acc.txt",Center_acc)
np.savetxt("K.txt",K)
plt.plot(K,Berrut_acc,label="Berrut")
plt.plot(K,Center_acc,label="Centralized")
plt.legend(["Berrut","Centralized"])
plt.xlabel("K")
plt.ylabel("Accuracy")
plt.title('N=K+'+str(S)+', S=' +str(S)+', Num_of_Iterations='+ str(num_of_iterations))
plt.show()
############### START HERE ##############
#model_accuracy_pytorch()
#model_accuracy()
Plot_K()
# rain_your_nicest_model(1)
# a=np.array([11,12,13,14,15,16])
# b=1-3/a
# plt.plot(a, b)
# plt.show()