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Sinc_interpolation_CIFAR.py
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Sinc_interpolation_CIFAR.py
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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 CIFAR100
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
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
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), (0.25))
#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)
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')
############ model and data selection ##########
model=resnet.ResNet18()
PATH='../base_model_trained_files/cifar10/resnet18/model.t7'
model.load_state_dict(torch.load(PATH))
model.eval()
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 %%' % (
100*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.permute(2, 0, 1)
# torch_sample = trans(torch_sample)
torch_sample = torch_sample.unsqueeze(0)
pred = model(torch_sample)
_, predicts = torch.max(pred, 1)
predictions[i] = predicts.numpy()[0]
#diff = test_y - predictions
diff=test_y-predictions
print("Model accuracy is")
out = (n - np.count_nonzero(diff))/n
print(out)
print(n-np.count_nonzero(diff))
return out
shift=.25
#Sinc Encoder
def encoder(X,N):
[K,H,W,C]=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,C])
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)
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.permute( 2, 0, 1)
torch_sample = torch_sample.unsqueeze(0)
pred = model(torch_sample)
_, predicts = torch.max(pred, 1)
predictions[i]=predicts.numpy()[0]
diff=test_y[input_batch_ids]-predictions
# print("Accuracy is")
out=(n-np.count_nonzero(diff))
return out
def Acc_Comparison(K, N, S, 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)
Berrut_Accuracy = Berrut_Accuracy + np.count_nonzero(Berrut_predictions - True_labels) / K
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(3,15,2)
K = np.arange(2, 20, 1)
S=1
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,S,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("N.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+1, S=' +str(S)+', Num_of_Iterations='+ str(num_of_iterations))
plt.show()
#### 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.cifar10.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]
test_x=(test_x-mean)/std
#model_accuracy_pytorch()
#model_accuracy()
Plot_K()