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clarm_main.py
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clarm_main.py
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''' About this code,
1) A pytorch code
2) CVAE is trained where module number acts as conditional to encoder
3) Temporal correlations are learned in the latent space with LSTM
5) Autoregressive loop help forecast projections in all modules
'''
import torch
import torch.optim as optim
import os
import sys
sys.path.insert(1, os.path.abspath("E:\MSR\codes\RFLA_VAE_LSTM_pyt"))
# APIs and inbuilt functions
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from lib.modifyimsize import MODIFYIMSIZE
# information on cuda
print("Number of CUDA devices",torch.cuda.device_count())
print("Name of CUDA device - 1 ==>",torch.cuda.get_device_name(0))
print("Name of CUDA device - 2 ==>",torch.cuda.get_device_name(1))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device1 = torch.device("cuda:0") if torch.cuda.is_available() else "cpu"
device2 = torch.device("cuda:1")
# path for the dataset
im_path = "E://MSR//data//RFLASimulationData//Alan_HPSim_11_13_23//images//"
labels_path = "E://MSR//data//RFLASimulationData//Alan_HPSim_11_13_23//labels//"
im_dir_list = os.listdir(im_path)
labels_dir_list = os.listdir(labels_path)
print("Files and directories in '", im_path, "' :")
print(im_dir_list)
print("Files and directories in '", labels_path, "' :")
print(labels_dir_list)
# loop to import dataset
imgs_all = []
labels_all = []
n = 0
for i in range(len(im_dir_list)):
n = n+1
load_imgs = np.load(im_path + im_dir_list[i]).astype('float32')
load_labels = np.load(labels_path + labels_dir_list[i]).astype('float32')
imgs_all.append(load_imgs)
labels_all.append(load_labels)
del load_imgs, load_labels
# concatentate the appended dataset
imgs_all_tr = np.concatenate((imgs_all[0],imgs_all[1]),axis=0)
# moveaxis: (p,mod,proj,L,W) -> (p,mod,L,W,proj)
# modify images and normalize require moveaxis
imgs_all_tr = np.moveaxis(imgs_all_tr,2,4)
imgs_all_te = np.moveaxis(imgs_all[2],2,4)
print('Size of the imported train dataset', imgs_all_tr.shape)
print('Size of the imported test dataset',imgs_all_te.shape)
#%% =====>>>> DATASET VISUALIZATION <<<<======
############################################################################################
######################### DATASET VISUALIZATION ###########################################
############################################################################################
#%% plot samples from dataset - across different runs/perturbations (axis=0)
projection = 12
plot_samples = 48*10
plt.figure(figsize=(15,45))
for i in range(plot_samples):
cols = 6
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.imshow(imgs_all_tr[i,0,:,:,projection], aspect='auto', origin='lower', cmap='hsv')
plt.tick_params(left = False, right = False , labelleft = False ,
labelbottom = False, bottom = False)
plt.tight_layout()
#plt.savefig('dataset_runs_proj11.png', dpi=300)
#%% plot samples from dataset - across different modules
n_mod = 1
run = 0
plot_samples = 15
plt.figure(figsize=(15,15))
for p in range(plot_samples):
cols = 5
plt.subplot(int(plot_samples/cols) + 1, cols, p + 1)
plt.imshow(np.log(1+imgs_all_tr[run,n_mod,:,:,p]), aspect='auto', origin='lower', cmap='plasma')
plt.tick_params(left = False, right = False , labelleft = False ,
labelbottom = False, bottom = False)
plt.tight_layout()
#plt.savefig('dataset_runs_allproj_mod.png', dpi=300)
#%% plot samples from dataset - different modules - different projections (E-phi)
E_ref = [5.35508654, 41.30085241, 72.59726239, 99.55659545, 113.30694285,
126.54444021, 139.06689037, 153.99711291, 167.78542056, 182.78942052,
196.36477222, 212.36085887, 225.5727351, 242.05917683, 257.87629262,
270.56978027, 287.61654992, 304.05749827, 317.68888699, 334.16846795,
351.99214741, 366.78784088, 381.9247197, 398.52202592, 415.99627685,
430.9371102, 446.40440063, 464.28822739, 482.19924841, 497.70466852,
512.78249953, 529.80507787, 548.09348445, 566.19867131, 582.82614372,
597.95315468, 614.21267512, 632.03148033, 650.15885225, 667.67175433,
683.26887754, 698.57431461, 715.5213991, 733.9661626, 751.98340543,
769.06200623, 784.84248942, 800.09776699]
phi_ref = [11280.83108553, 35524.52623656, 49838.59318148, 61305.903995,
288361.38037085, 319788.38387692, 351209.13837744, 381470.37516268,
411729.70835502, 440524.90018308, 469362.37242238, 498138.63636995,
523347.67225599, 547484.83300091, 571555.07608093, 595678.48363986,
619799.63361058, 643884.756519, 667272.15623005, 690674.49256342,
714044.80546704, 737047.56147925, 760089.84080003, 782414.85757303,
804711.56299811, 827019.32420482, 849350.09611553, 871678.29433691,
893998.67106446, 915579.15614886, 937194.61074431, 958443.63612275,
979675.77897511, 1000909.73863817, 1022126.5531958, 1042646.95242532,
1063171.49187095, 1083695.10086393, 1104198.70898035, 1124705.97951749,
1145215.59676875, 1165731.03826898, 1186267.48596319, 1206785.63240674,
1226584.02594906, 1246374.38559172, 1266166.68815084, 1284803.08186162]
scale1 = 0.01
scale2 = 0.0001
E_ref2 = np.array(E_ref)*scale1
E_ref2 = np.round(E_ref2,1)
phi_ref2 = np.array(phi_ref)*scale2
phi_ref2 = np.round(phi_ref2,1)
phi_b = 60*scale1
E_b = 1.3*0.01*E_ref2*scale2
phi_lb, phi_ub = phi_ref2-60, phi_ref2+60
phi_lb, phi_ub = np.round(phi_lb,1),np.round(phi_ub,1)
E_lb, E_ub = E_ref2-E_b, E_ref2+E_b
E_lb, E_ub = np.round(E_lb,1),np.round(E_ub,1)
projection = 11 # E-phi
plot_samples = 48
plt.figure(figsize=(14,8))
dataplot = imgs_all_tr
for i in range(plot_samples):
cols = 8
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.imshow(dataplot[0,i,:,:,projection], aspect='auto', origin='lower', cmap='hsv')
#plt.tick_params(left = False, right = False , labelleft = False ,
#labelbottom = False, bottom = False)
plt.title('Mod.-'+str(i+1), fontsize = 12)
if (i == 0 or i == 8 or i == 16 or i == 24 or i == 32 or i == 40):
plt.ylabel('E (MeV*100)',fontsize=10)
if (i>40 and i<48):
plt.xlabel('$\phi (rad*10^4)$',fontsize=10)
xticks = [phi_lb[i], phi_ref2[i], phi_ub[i]]
yticks = [E_lb[i], E_ref2[i], E_ub[i]]
xticklabels = [str(xticks[0]), str(xticks[1]), str(xticks[2])]
yticklabels = [str(yticks[0]), str(yticks[1]), str(yticks[2])]
plt.xticks([0,128,256], xticklabels, fontsize=12)
plt.yticks([0,128,256], yticklabels, fontsize=12)
plt.tight_layout()
#%% plot samples from dataset - different modules - different projections (E-phi)
projection = 11 # E-phi
plot_samples = 48
plt.figure(figsize=(15,7))
dataplot = imgs_all_tr
for i in range(plot_samples):
cols = 12
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.suptitle('$E$ - $\phi$ projection',fontsize=20)
plt.imshow(dataplot[0,i,:,:,projection], aspect='auto', origin='lower', cmap='hsv')
#plt.tick_params(left = False, right = False , labelleft = False ,
#labelbottom = False, bottom = False)
plt.title('Mod.-'+str(i+1), fontsize = 12)
if (i == 0 or i == 12 or i == 24 or i == 36 or i == 48):
plt.ylabel('% $E_{r}$ (MeV)',fontsize=12)
yticks = [-1.3, 0, 1.3]
yticklabels = [str(yticks[0]), str(yticks[1]), str(yticks[2])]
plt.yticks([0,128,256], yticklabels, fontsize=10)
else:
plt.yticks([],fontsize=10)
if (i>35 and i<48):
plt.xlabel('$\Delta \phi (deg)$',fontsize=12)
xticks = [-60, 0, 60]
xticklabels = [str(xticks[0]), str(xticks[1]), str(xticks[2])]
plt.xticks([0,128,256], xticklabels, fontsize=10)
else:
plt.xticks([],fontsize=10)
plt.subplots_adjust(wspace=0.23, hspace=0.32)
plt.savefig('dataset_modules_4x12_proj_'+str(projection)+'.png', bbox_inches='tight',dpi=300)
#%% plot samples from dataset - different modules - 3 projections (E-phi) ==> 2 x 6
path = 'E:/MSR/codes/RFLA_VAE_LSTM_pyt/figures/fig_data/'
projection = 11 # E-phi
plot_samples = 12
pltfig = [0,1,2,3,10,15,20,25,30,35,40,47]
plt.figure(figsize=(15,7))
dataplot = imgs_all_tr
#dataplot = np.log(1+imgs_all_tr)
for i in range(plot_samples):
cols = 6
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.suptitle('$E$ - $\phi$ projection',fontsize=20)
plt.imshow(dataplot[0,pltfig[i],:,:,projection], aspect='auto', origin='lower', cmap='plasma')
#plt.tick_params(left = False, right = False , labelleft = False ,
#labelbottom = False, bottom = False)
plt.title('Mod.-'+str(pltfig[i]+1), fontsize = 16)
if (i == 0 or i == 6):
plt.ylabel('% $E_{r}$ (MeV)',fontsize=16)
yticks = [-1.3, 0, 1.3]
yticklabels = [str(yticks[0]), str(yticks[1]), str(yticks[2])]
plt.yticks([0,128,256], yticklabels, fontsize=16)
else:
plt.yticks([],fontsize=10)
if (i>5 and i<13):
plt.xlabel('$\Delta \phi (deg)$',fontsize=16)
xticks = [-60, 0, 60]
xticklabels = [str(xticks[0]), str(xticks[1]), str(xticks[2])]
plt.xticks([0,128,256], xticklabels, fontsize=16)
else:
plt.xticks([],fontsize=10)
plt.subplots_adjust(wspace=0.23, hspace=0.32)
plt.savefig(path + 'dataset_modules_2x6_proj_'+str(projection) + '_unlog' +'.png', bbox_inches='tight',dpi=300)
#%% plot samples from dataset - different modules - 3 projections (x-y) ==> 2 x 6
projection = 1 # x-y
plot_samples = 12
pltfig = [0,1,2,3,10,15,20,25,30,35,40,47]
plt.figure(figsize=(15,7))
for i in range(plot_samples):
cols = 6
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.suptitle('$x - y$ projection',fontsize=20)
plt.imshow(dataplot[0,pltfig[i],:,:,projection], aspect='auto', origin='lower', cmap='plasma')
#plt.tick_params(left = False, right = False , labelleft = False ,
#labelbottom = False, bottom = False)
plt.title('Mod.-'+str(pltfig[i]+1), fontsize = 16)
if (i == 0 or i == 6):
plt.ylabel('x(cm)',fontsize=16)
yticks = [-3, 0, 3]
yticklabels = [str(yticks[0]), str(yticks[1]), str(yticks[2])]
plt.yticks([0,128,256], yticklabels, fontsize=16)
else:
plt.yticks([],fontsize=10)
if (i>5 and i<13):
plt.xlabel('$y(cm)$',fontsize=16)
xticks = [-3, 0, 3]
xticklabels = [str(xticks[0]), str(xticks[1]), str(xticks[2])]
plt.xticks([0,128,256], xticklabels, fontsize=16)
else:
plt.xticks([],fontsize=10)
plt.subplots_adjust(wspace=0.23, hspace=0.32)
plt.savefig(path + 'dataset_modules_2x6_proj_'+str(projection) + '_unlog' +'.png',bbox_inches='tight', dpi=300)
#%% plot samples from dataset - different modules - 3 projections (x'-y') ==> 2 x 6
projection = 12 # x'-y'
plot_samples = 12
pltfig = [0,1,2,3,10,15,20,25,30,35,40,47]
plt.figure(figsize=(15,7))
for i in range(plot_samples):
cols = 6
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.suptitle('$x^\prime$ - $y^\prime$ projection',fontsize=20)
plt.imshow(dataplot[0,pltfig[i],:,:,projection], aspect='auto', origin='lower', cmap='plasma')
#plt.tick_params(left = False, right = False , labelleft = False ,
#labelbottom = False, bottom = False)
plt.title('Mod.-'+str(pltfig[i]+1), fontsize = 16)
if (i == 0 or i == 6):
plt.ylabel('$x^\prime$ (mrad)',fontsize=16)
yticks = [-10, 0, 10]
yticklabels = [str(yticks[0]), str(yticks[1]), str(yticks[2])]
plt.yticks([0,128,256], yticklabels, fontsize=16)
else:
plt.yticks([],fontsize=10)
if (i>5 and i<13):
plt.xlabel('$y^\prime$ (mrad)',fontsize=16)
xticks = [-10, 0, 10]
xticklabels = [str(xticks[0]), str(xticks[1]), str(xticks[2])]
plt.xticks([0,128,256], xticklabels, fontsize=16)
else:
plt.xticks([],fontsize=10)
plt.subplots_adjust(wspace=0.23, hspace=0.32)
plt.savefig(path + 'dataset_modules_2x6_proj_'+str(projection) + '_unlog' +'.png',bbox_inches='tight', dpi=300)
#%% =====>>>> DATASET PROCESSING <<<<======
############################################################################################
######################### DATASET PRCOESSING #############################################
############################################################################################
#%% module numbers as conditional inputs
mod_num = np.arange(0,48,dtype='float32')
mod_num_nm = mod_num[:,np.newaxis]/np.max(mod_num)
mod_num_tr = np.tile(mod_num_nm, (imgs_all_tr.shape[0],1))
mod_num_te = np.tile(mod_num_nm, (imgs_all_te.shape[0],1))
print('module number input (conditional) shape for train', mod_num_tr.shape)
print('module number input (conditional) shape for test',mod_num_te.shape)
print('module number input (first 48)', mod_num_tr[0:48])
#%% Process the dataset (Reshape & Normalize)
# size of the image we want
imsize = 256
# reshape the dimensions of the dataset from 5D to 4D
imgs_all_tr_rs = imgs_all_tr.reshape(imgs_all_tr.shape[0]*imgs_all_tr.shape[1],
imgs_all_tr.shape[2],
imgs_all_tr.shape[3],
imgs_all_tr.shape[4])
imgs_all_te_rs = imgs_all_te.reshape(imgs_all_te.shape[0]*imgs_all_te.shape[1],
imgs_all_te.shape[2],
imgs_all_te.shape[3],
imgs_all_te.shape[4])
print('Size of reshaped dataset (train)', imgs_all_tr_rs.shape)
print('Size of reshaped dataset (test)',imgs_all_te_rs.shape)
#del imgs_all_tr, imgs_all_te
#%% modify the image size and normalize the data
if (imsize != 256):
imgs_all_tr_rs = MODIFYIMSIZE(imgs_all_tr_rs, imsize)
imgs_all_te_rs = MODIFYIMSIZE(imgs_all_te_rs, imsize)
# function for data normalization
def MinMaxNormalizeData(data):
# simplified to see the memory utilization at each step
# dividing an array with a number takes more time and multiple by (1/number)
minval = np.min(data)
maxval = np.max(data)
diff = maxval-minval
diffinv = 1/diff
data_nm_numer = data - minval
data_nm = data_nm_numer*diffinv
return data_nm, minval, maxval
def UnNormalize(data, minval, maxval):
diff = maxval-minval
data_un = data*diff + minval
return data_un
# Normalize the data
imgs_all_tr_rs_nm, minval_tr, maxval_tr = MinMaxNormalizeData(imgs_all_tr_rs)
imgs_all_te_rs_nm, minval_te, maxval_te = MinMaxNormalizeData(imgs_all_te_rs)
print('Size of resized & normalized dataset (train)', imgs_all_tr_rs_nm.shape)
print('Size of resized & normalized dataset (test)',imgs_all_te_rs_nm.shape)
del imgs_all_tr_rs, imgs_all_te_rs
#%% plot samples from normalized dataset - different modules == E-z
projection = 11
plot_samples = 48
plt.figure(figsize=(14,5))
dataplot = imgs_all_tr_rs_nm
for i in range(plot_samples):
cols = 12
plt.subplot(int(plot_samples/cols) + 1, cols, i + 1)
plt.suptitle('Normalized $E$ - $\phi$ projection',fontsize=16)
plt.imshow(dataplot[i,:,:,projection], aspect='auto', origin='lower', cmap='hsv')
plt.tick_params(left = False, right = False , labelleft = False ,
labelbottom = False, bottom = False)
plt.title('Mod.-'+str(i+1),fontsize=10)
plt.tight_layout()
del dataplot
#%% Some data analysis = SSID and MSE across different perturbations
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import mean_squared_error
module = 40
ssim_da_all = []
mse_da_all=[]
for i in range(imgs_all_tr.shape[0]-1):
img_1 = imgs_all_tr[0,module,:,:,projection]
img_2 = imgs_all_tr[i+1,module,:,:,projection]
ssim_da = ssim(img_1, img_2, data_range=img_1.max() - img_2.min())
mse_da = mean_squared_error(img_1,img_2)
ssim_da_all.append(ssim_da)
mse_da_all.append(mse_da)
plt.figure(figsize = (30, 6))
plt.plot(ssim_da_all,'--or')
plt.ylabel("SSIM",fontsize=20)
plt.yticks(fontsize=20)
plt.figure(figsize = (30, 6))
plt.plot(mse_da_all,'--or')
plt.ylabel("MSE",fontsize=20)
plt.yticks(fontsize=20)
#%% Some data analysis = SSID and MSE across the modules
projection = 11
ssim_matrix = np.zeros((48,48))
mse_matrix = np.zeros((48,48))
data_matrix = imgs_all_tr_rs_nm[0:48,:,:,projection]
for i in range(48):
for j in range(48):
img_1 = data_matrix[i]
img_2 = data_matrix[j]
if j >= i:
maxval = img_2.max()
minval = img_2.min()
else:
maxval = img_1.max()
minval = img_1.min()
ssim_da = ssim(img_1, img_2, data_range=maxval - minval)
mse_da = mean_squared_error(img_1,img_2)
ssim_matrix[i,j] = ssim_da
mse_matrix[i,j] = mse_da
plt.figure(figsize=(20, 20))
plt.imshow(ssim_matrix,cmap='jet')
plt.colorbar()
plt.xticks(fontsize=20)
plt.xticks(np.arange(0,48),fontsize=12)
plt.yticks(np.arange(0,48),fontsize=12)
plt.figure(figsize=(20, 20))
plt.imshow(mse_matrix,cmap='jet')
plt.colorbar()
plt.xticks(np.arange(0,48),fontsize=12)
plt.yticks(np.arange(0,48),fontsize=12)
del data_matrix
#%% =====>>>> TRAINING CVAE <<<<======
############################################################################################
######################### CVAE TRAINING ##################################################
############################################################################################
#%% Transform the np dataset into tensor
imgs_all_tr_rs_nm = np.moveaxis(imgs_all_tr_rs_nm,3,1)
imgs_all_te_rs_nm = np.moveaxis(imgs_all_te_rs_nm,3,1)
imgs_all_tr_rs_tensor = torch.from_numpy(imgs_all_tr_rs_nm)
imgs_all_te_rs_tensor = torch.from_numpy(imgs_all_te_rs_nm)
mod_num_tr_tensor = torch.from_numpy(mod_num_tr)
mod_num_te_tensor = torch.from_numpy(mod_num_te)
print('Size of final train dataset', imgs_all_tr_rs_tensor.shape)
print('Size of final test dataset',imgs_all_te_rs_tensor.shape)
print('Conditional shape for train', mod_num_tr_tensor.shape)
print('Conditional shape for test',mod_num_te_tensor.shape)
del imgs_all_tr_rs_nm, imgs_all_te_rs_nm
#%% Tensor dataset & and use dataloader transforms into a tensor
BATCH_SIZE = 32
from torch.utils.data import Dataset
class DatasetforDataloader(Dataset):
def __init__(self,X,y):
self.X = X
self.y = y
def __len__(self):
return len(self.X)
def __getitem__(self,i):
# create a tuple
return self.X[i], self.y[i]
# Dataset of dataloader
train_dataset = DatasetforDataloader(imgs_all_tr_rs_tensor,mod_num_tr_tensor)
test_dataset = DatasetforDataloader(imgs_all_te_rs_tensor,mod_num_te_tensor)
# train-test split
train_size = int(0.85 * len(imgs_all_tr_rs_tensor))
val_size = len(imgs_all_tr_rs_tensor) - train_size
trainX, valX = torch.utils.data.random_split(train_dataset,[train_size, val_size])
## Dataloader wraps an iterable around the Dataset for easy access to the samples.
dataloader_train = DataLoader(trainX, batch_size = BATCH_SIZE, shuffle=True)
dataloader_val = DataLoader(valX, batch_size = BATCH_SIZE, shuffle=False)
dataloader_test = DataLoader(test_dataset, batch_size = BATCH_SIZE, shuffle=False)
dataloader_trainval = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle=False)
print('Length of the training set',len(trainX))
print('Length of the validation set',len(valX))
#%% CVAE model
# Move the model to GPU
# Initialize the autoencoder
from lib.cvae_pyt import cVAE
from lib.elbo_loss import ELBO
imch = 15
f1,f2,f3,f4,f5 = 32,64,128,256,512
neurons = 256
branchneurons = 32
combinedneurons = neurons + branchneurons
latentdim = 8
strides = 2
nfilters = 5
imfinal1 = int(imsize/(strides**nfilters))
imfinal2 = int(imsize/(strides**nfilters))
cvae15 = cVAE(imch,f1,f2,f3,f4,f5,
neurons,branchneurons,combinedneurons,
latentdim,imfinal1,imfinal2,device1)
#cvae15 = torch.nn.DataParallel(cvae15)
cvae15 = cvae15.to(device1)
print("Num params encoder: ", sum(p.numel() for p in cvae15.parameters()))
print('Model architecture', cvae15)
# Define the loss function and optimizer
optimizer = optim.Adam(cvae15.parameters(), lr=0.001)
beta = 1
#%% Training
num_epochs = 1500
def train_epoch_cvae(model,device,dataloader_train,optimizer):
model.train() # Set train mode for model
train_loss = []
train_loss_bce = []
train_loss_kl = []
for step, batch in enumerate(dataloader_train):
x_tr, c_tr = batch
x_tr = x_tr.to(device)
c_tr = c_tr.to(device)
optimizer.zero_grad()
x_hat, mean, log_var, z = model(x_tr,c_tr)
loss, bce, kl = ELBO(x_tr, x_hat, mean, log_var, beta)
loss.backward()
optimizer.step()
train_loss.append(loss.item())
train_loss_bce.append(bce.item())
train_loss_kl.append(kl.item())
return (np.mean(train_loss),np.mean(train_loss_bce),np.mean(train_loss_kl))
def test_epoch_cvae(model, device, dataloader_test):
model.eval() # Set the eval mode for model
test_loss = []
test_loss_bce = []
test_loss_kl = []
with torch.no_grad(): # No need to track the gradients
for step, batch in enumerate(dataloader_test):
x_val, c_val = batch
x_val = x_val.to(device)
c_val = c_val.to(device)
x_hat, mean, log_var, z = model(x_val,c_val)
loss, bce, kl = ELBO(x_val, x_hat, mean, log_var, beta)
test_loss.append(loss.item())
test_loss_bce.append(bce.item())
test_loss_kl.append(kl.item())
return (np.mean(test_loss),np.mean(test_loss_bce),np.mean(test_loss_kl))
diz_loss = {'train_loss':[], 'train_loss_bce':[],'train_loss_kl':[],
'val_loss':[],'val_loss_bce':[],'val_loss_kl':[]}
for epoch in range(num_epochs):
train_loss, tr_bce, tr_kl = train_epoch_cvae(cvae15,device1,dataloader_train,optimizer)
val_loss, val_bce, val_kl = test_epoch_cvae(cvae15,device1,dataloader_val)
print('\n EPOCH {}/{} \t train loss {} \t val loss {} \t train recon {} \t val recon {} \ttrain kl {} \t val kl {}'
.format(epoch + 1, num_epochs,train_loss,val_loss,tr_bce,val_bce,tr_kl,val_kl))
diz_loss['train_loss'].append(train_loss)
diz_loss['train_loss_bce'].append(tr_bce)
diz_loss['train_loss_kl'].append(tr_kl)
diz_loss['val_loss'].append(val_loss)
diz_loss['val_loss_bce'].append(val_bce)
diz_loss['val_loss_kl'].append(val_kl)
# Save the model
torch.save(cvae15.state_dict(), 'cvae_15ch_model.pth')
#%% load the model
cvae15.load_state_dict(torch.load('cvae_15ch_model.pth'))
cvae15.eval()
#%% cVAE training and test loss
# Plot losses
plt.figure(figsize=(10,8))
plt.plot(diz_loss['train_loss'], '-ok', label='Train',)
plt.plot(diz_loss['val_loss'], '-^r', label='Valid')
plt.xlabel('Epoch',fontsize=20)
plt.ylabel('Average Loss',fontsize=20)
plt.legend(["tr_total", "val_total"])
plt.title('Training & Validation loss', fontsize = 20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.ylim(49000, 60000)
plt.show()
# Plot losses on semilog scale
plt.figure(figsize=(10,8))
plt.plot(diz_loss['train_loss_bce'],'-ob', label='Train bce')
plt.plot(diz_loss['val_loss_bce'], '-^r', label='Valid bce')
plt.xlabel('Epoch',fontsize=20)
plt.ylabel('Reconstruction loss',fontsize=20)
plt.legend(["tr_bce", "val_bce"],fontsize=20)
plt.title('Training & Validation loss', fontsize = 20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.ylim(49000, 60000)
plt.show()
# Plot losses on semilog scale
plt.figure(figsize=(10,8))
plt.plot(diz_loss['train_loss_kl'],'-ob', label='Train kl')
plt.plot(diz_loss['val_loss_kl'],'-^r', label='Valid kl')
plt.xlabel('Epoch',fontsize=20)
plt.ylabel('KL divergence loss',fontsize=20)
plt.legend(["tr_kl" , "val_kl"],fontsize=20)
plt.title('Training & Validation loss', fontsize = 20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.ylim(200,500)
plt.show()
#%% =====>>>> RECONSTRUCTION RESULTS <<<<======
############################################################################################
######################### RECONSTRUCTION PART #############################################
############################################################################################
#%% Collect all reconstruction images for training and testing data
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import mean_squared_error
# calculate the reconstruction error
def reconstruction_error(samples, pred_samples):
errors = []
for (image, recon) in zip(samples, pred_samples):
mse = np.mean((image - recon)**2)
errors.append(mse)
return errors
# calculate mse and ssim projection wise
def mse_ssim_projwise(realdata,gendata):
ssim_app = []
mse_app = []
for p in range(realdata.shape[0]):
real = realdata[p]
gen = gendata[p]
sim = ssim(real, gen, data_range=real.max() - real.min())
mse = mean_squared_error(real, gen)
ssim_app.append(sim)
mse_app.append(mse)
return np.array(mse_app), np.array(ssim_app)
# calculate mse and ssim sample wise
def mse_ssim_samplewise(data,recondata):
mse2_app, ss2_app = [], []
for s in range(data.shape[0]):
org = data[s,:,:,:]
recon = recondata[s,:,:,:]
mse, ss = mse_ssim_projwise(org, recon)
mse2_app.append(mse)
ss2_app.append(ss)
return np.array(mse2_app), np.array(ss2_app)
# reconstruction images, mse and ssim calculations
def recon_cVAE(dataloader):
mse3_app, ss3_app = [], []
data_app, recon_app = [], []
with torch.no_grad():
for step, batch in enumerate(dataloader):
print('Step = ', step)
data,mod = batch
data = data.to(device1)
mod = mod.to(device1)
recon,_,_,_ = cvae15(data,mod)
data = data.detach().cpu().numpy()
recon = recon.detach().cpu().numpy()
mse, ss = mse_ssim_samplewise(data,recon)
mse3_app.append(mse)
ss3_app.append(ss)
# save data and recon for plotting
if (step == 0) or (step == 1) or (step == 2):
data_app.append(data)
recon_app.append(recon)
return np.array(data_app), np.array(recon_app), np.array(mse3_app), np.array(ss3_app)
org_tr, recon_tr, mse_tr, ssim_tr = recon_cVAE(dataloader_trainval)
org_te, recon_te, mse_te, ssim_te = recon_cVAE(dataloader_test)
print("Original training shape",org_tr.shape)
print("Original test shape",org_te.shape)
print("ssim training shape",ssim_tr.shape)
print("ssim test shape",ssim_te.shape)
# change the shape of ssim and mse
ssim_tr = ssim_tr.reshape(ssim_tr.shape[0]*ssim_tr.shape[1],ssim_tr.shape[2])
ssim_te = ssim_te.reshape(ssim_te.shape[0]*ssim_te.shape[1],ssim_te.shape[2])
mse_tr = mse_tr.reshape(mse_tr.shape[0]*mse_tr.shape[1],mse_tr.shape[2])
mse_te = mse_te.reshape(mse_te.shape[0]*mse_te.shape[1],mse_te.shape[2])
# change the shape again
ssim_tr, ssim_te = ssim_tr.reshape(1400,48,15), ssim_te.reshape(100,48,15)
mse_tr, mse_te = mse_tr.reshape(1400,48,15), mse_te.reshape(100,48,15)
# reshape org_tr and recon_tr
org_tr = org_tr.reshape(org_tr.shape[0]*org_tr.shape[1],15,256,256)
org_te = org_te.reshape(org_te.shape[0]*org_te.shape[1],15,256,256)
recon_tr = recon_tr.reshape(recon_tr.shape[0]*recon_tr.shape[1],15,256,256)
recon_te = recon_te.reshape(recon_te.shape[0]*recon_te.shape[1],15,256,256)
# change the shape again
org_tr, org_te = org_tr.reshape(2,48,15,256,256), org_te.reshape(2,48,15,256,256)
recon_tr, recon_te = recon_tr.reshape(2,48,15,256,256), recon_te.reshape(2,48,15,256,256)
print("Original training shape",org_tr.shape)
print("Original test shape",org_te.shape)
print("ssim tr shape",ssim_tr.shape)
print("ssim te shape",ssim_te.shape)
#%% plot original and reconstruction images - 3 x 5 pictures
allprojnames = ['$x-p_x$','$x-y$','$x-p_y$','$x-z$','$x-p_z$',
'$y-p_x$','$y-p_y$','$y-z$','$y-p_z$','$z-p_x$',
'$z-p_y$','$z-p_z$','$p_x-p_y$','$p_x-p_z$','$p_z-p_y$']
org_te_un = UnNormalize(org_te, minval_te, maxval_te)
recon_te_un = UnNormalize(recon_te, minval_te, maxval_te)
org_te_un_log = np.log(1 + org_te_un)
recon_te_un_log = np.log(1 + recon_te_un)
randrun = 0
n_mod_recon = 39 # 0,23,47 for paper # 1,2,3,4,9,19,29,39 for supp. info
plot_samples = 15
plt.figure(figsize=(15,12))
# original images as 3 x 5
for p in range(15):
cols = 5
plt.subplot(int(plot_samples/cols) + 1, cols, p + 1)
plt.suptitle('Original projections in module ' + str(n_mod_recon+1) , fontsize = 32, y = 0.99)
plt.title(str(allprojnames[p]), fontsize = 28)
plt.imshow(org_te_un_log[randrun,n_mod_recon,p,:,:], aspect='auto', origin='lower', cmap='plasma')
plt.axis('OFF')
plt.tight_layout()
path = 'E:/MSR/codes/RFLA_VAE_LSTM_pyt/figures/fig_reconstruction/'
#plt.savefig(path + 'reconablity_orgimgs_log_mod_'+str(n_mod_recon+1) + '.png', bbox_inches="tight", dpi=300)
# reconstructed images as 3 x 5
plt.figure(figsize=(15,12))
for p in range(15):
cols = 5
plt.subplot(int(plot_samples/cols) + 1, cols, p + 1)
plt.suptitle('Reconstruction projections in module ' + str(n_mod_recon+1) , fontsize = 32, y = 0.99)
plt.title(str(allprojnames[p]), fontsize = 28)
plt.imshow(recon_te_un_log[randrun,n_mod_recon,p,:,:], aspect='auto', origin='lower', cmap='plasma')
plt.axis('OFF')
plt.tight_layout()
path = 'E:/MSR/codes/RFLA_VAE_LSTM_pyt/figures/fig_reconstruction/'
#plt.savefig(path + 'reconablity_reconimgs_log_mod_'+str(n_mod_recon+1) + '.png', bbox_inches="tight", dpi=300)
#%% plots for MSE and SSIM for the entire training and test with error bounds
def mu_sigma(data):
mu = np.mean(data,axis=0)
sigma = np.std(data,axis=0)
return mu,sigma
mse_recon_avg_tr = np.mean(mse_tr, axis = 0)
ssim_recon_avg_tr = np.mean(ssim_tr, axis = 0)
mse_recon_avg_te = np.mean(mse_te, axis = 0)
ssim_recon_avg_te = np.mean(ssim_te, axis = 0)
print('Avg. MSE Recon te shape', mse_recon_avg_te.shape)
print('Avg. SSIM Recon te shape', ssim_recon_avg_te.shape)
mu_mse_recon_avg_tr, sigma_mse_recon_avg_tr = mu_sigma(mse_recon_avg_tr)
mu_ssim_recon_avg_tr, sigma_ssim_recon_avg_tr = mu_sigma(ssim_recon_avg_tr)
mu_mse_recon_avg_te, sigma_mse_recon_avg_te = mu_sigma(mse_recon_avg_te)
mu_ssim_recon_avg_te, sigma_ssim_recon_avg_te = mu_sigma(ssim_recon_avg_te)
print('Avg. MSE Recon mu te shape', mu_mse_recon_avg_te.shape)
print('Avg. SSIM Recon mu te shape', mu_ssim_recon_avg_te.shape)
lb_mse_tr = mu_mse_recon_avg_tr - sigma_mse_recon_avg_tr
ub_mse_tr = mu_mse_recon_avg_tr + sigma_mse_recon_avg_tr
lb_mse_te = mu_mse_recon_avg_te - sigma_mse_recon_avg_te
ub_mse_te = mu_mse_recon_avg_te + sigma_mse_recon_avg_te
lb_ssim_tr = mu_ssim_recon_avg_tr - sigma_ssim_recon_avg_tr
ub_ssim_tr = mu_ssim_recon_avg_tr + sigma_ssim_recon_avg_tr
lb_ssim_te = mu_ssim_recon_avg_te - sigma_ssim_recon_avg_te
ub_ssim_te = mu_ssim_recon_avg_te + sigma_ssim_recon_avg_te
plt.figure()
xaxis = np.arange(1,16)
fig, ax = plt.subplots(1, 2, figsize=(18, 3))
fig.suptitle('Average reconstruction MSE and SSIM', fontsize = 18)
ax[0].plot(np.arange(1,16),mu_mse_recon_avg_tr,'o-',c ='tab:blue')
ax[0].plot(np.arange(1,16),mu_mse_recon_avg_te,'^--',c='tab:blue')
ax[1].plot(np.arange(1,16),mu_ssim_recon_avg_tr,'o-',c = 'tab:orange')
ax[1].plot(np.arange(1,16),mu_ssim_recon_avg_te,'^--',c='tab:orange')
ax[0].set_ylabel('Avg. recon. MSE', fontsize = 18)
ax[1].set_ylabel('Avg. recon. SSIM', fontsize = 18)
ax[0].set_ylim([0.1e-7,10e-7])
ax[1].set_ylim([0.98,1.0])
ax[0].set_yticks([2e-7,4e-7,6e-7,8e-7,10e-7])
ax[1].set_yticks([0.98,0.985,0.99,0.995,1.00])
ax[0].set_yticklabels((['2','4','6','8','10e-7']),fontsize=15)
ax[1].set_yticklabels((['0.980','0.985','0.990','0.995','1.0']),fontsize=15)
ax[0].set_xticks(range(1,16))
ax[1].set_xticks(range(1,16))
ax[0].set_xticklabels((str(i) for i in range(1,16)),fontsize=15)
ax[1].set_xticklabels((str(i) for i in range(1,16)),fontsize=15)
ax[0].set_xlabel('6d LPS projections', fontsize = 18)
ax[1].set_xlabel('6d LPS projections', fontsize = 18)
ax[0].legend(['train','test'], fontsize=15)
ax[1].legend(['train','test'], fontsize=15)
plt.savefig('mse_ssim_recon.png', bbox_inches='tight' ,dpi=300)
#%% =====>>>> LATENT SPACE RESULTS <<<<======
############################################################################################
################## LATENT SPACE VISUALIZATION PART ########################################
############################################################################################
#%% plot multidimensional latent space as different 2d projections
from lib.colorplots2d import COLORPLOTS_2D
def latentspace(dataloader,model):
model.eval()
latent_app = []
with torch.no_grad():
for step, batch in enumerate(dataloader):
data, mod = batch
data = data.to(device1)
mod = mod.to(device1)
_,_,_,z = model(data,mod)
latent_app.append(z.cpu().detach())
latent_app = torch.cat(latent_app)
return latent_app
latent_train = latentspace(dataloader_train,cvae15).numpy()
latent_val = latentspace(dataloader_val,cvae15).numpy()
latent_combine = latentspace(dataloader_trainval,cvae15).numpy()
latent_test = latentspace(dataloader_test,cvae15).numpy()
print("Shape of the latent train", latent_train.shape)
print("Shape of the latent validation", latent_val.shape)
print("Shape of the latent combine", latent_combine.shape)
print("Shape of the latent test", latent_test.shape)
# color plots in latent
for i in range(1,8):
COLORPLOTS_2D(latent_combine,imgs_all_tr.shape[0],0,i,'2d proj. of 8d latent space')
#plt.savefig(path +'latent_parallelcoords_' + str(0) + '_' + str(i), dpi=300)
#%% parallel cords visualization of the 8d latent space
from lib.parallelcords1_plot import parallelcords1
# plot few training and validation samples in parralelcords
nlines1 = np.random.randint(0, latent_train.shape[0], 2000)
nlines2 = np.random.randint(0, latent_val.shape[0], 300)
data_train_pc = latent_train[nlines1]
data_val_pc = latent_val[nlines2]
# plotting train and test sets
N1 = np.ones(data_train_pc.shape[0],dtype='int64')
N2 = np.ones(data_val_pc.shape[0],dtype='int64')*2
coloring = np.concatenate((N1,N2),axis=0)
data_pc = np.concatenate((data_train_pc,data_val_pc),axis=0)
color1 = "tab:blue"
color2 = "tab:orange"
parallelcords1(data_pc,coloring,color1,color2)
#plt.savefig('latent_parallelcords.png', dpi=300)
#%% parallel cords visualization of the 8d latent space with different modules separated
from lib.parallelcords_modwise import PC_modwise
latent_combine_rs = latent_combine.reshape(1400,48,8)
n_lines = np.random.randint(0, latent_combine_rs.shape[0], 25) # 5 random lines for each module
coloring = []
data_pc = []
for i in range(48):
data_mod = latent_combine_rs[n_lines, i, :]
N = np.ones(data_mod.shape[0],dtype='int64')*(i+1)
coloring.append(N)
data_pc.append(data_mod)
data_pc = np.concatenate(data_pc, axis = 0)
PC_modwise(data_pc, n_lines.shape[0])
path = 'E:/MSR/codes/RFLA_VAE_LSTM_pyt/figures/fig_latentspace/'
plt.savefig(path + 'latent_parallelcords_modwise.png', dpi=300)
#%% latent space via PCA
from lib.PCA import PCAmodel
from lib.trajectors import TRAJECTORIESinLATENT
from lib.colorplotsinlatent import COLORPLOTSinLATENT
pca = PCAmodel()
nd = 2
(Xtr_pca, evr_tr_pca, recon_tr_pca) = pca.pcabuild(latent_train, nd)
(Xval_pca, evr_val_pca, recon_val_pca) = pca.pcabuild(latent_val, nd)
(Xc_pca, evr_tc_pca, recon_tc_pca) = pca.pcabuild(latent_combine, nd)
plt.figure(figsize=(6, 6))
plt.title('PCA of the latent space', fontsize = 20)
plt.scatter(Xtr_pca[:, 0], Xtr_pca[:, 1], c='tab:blue', marker=".")
plt.scatter(Xval_pca[:, 0], Xval_pca[:, 1], c='tab:orange', marker=".")
plt.legend(['Train','Test'], fontsize=18)
plt.xlabel('pca-1', fontsize=20)
plt.ylabel('pca-2', fontsize=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.show()
#plt.savefig('pca1_of_latent.png', dpi=300)
# color plots in latent
COLORPLOTSinLATENT(Xc_pca,imgs_all_tr.shape[0], 'pca of 8d latent space', 'pca')
#plt.savefig(path + 'pca2_of_latent.png', dpi=300)
modidx, prt = 4, 3
title = 'Trajectories in the latent manifold'
TRAJECTORIESinLATENT(Xc_pca,modidx,prt,title,'pca')
#plt.savefig('traj_pca2_of_latent.png', dpi=300)
#%% t-sne of the latent space
from sklearn.manifold import TSNE
size_train = latent_train.shape[0]
tsne = TSNE(n_components=2, learning_rate='auto',
init ='pca').fit_transform(latent_combine)
X_train_tsne = tsne[0:size_train,:]
X_test_tsne = tsne[size_train:,:]
plt.figure(figsize=(6, 6))
plt.title('t-SNE of the latent space', fontsize = 20)
plt.scatter(X_train_tsne[:, 0], X_train_tsne[:, 1], c='tab:blue', marker=".")
plt.scatter(X_test_tsne[:, 0], X_test_tsne[:, 1], c='tab:orange', marker=".")
plt.legend(['Train', 'Test'], loc='best', fontsize=18)
plt.xlabel('tsne1', fontsize=20)
plt.ylabel('tsne2', fontsize=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.savefig('tsne1_of_latent.png', dpi=300)
plt.show()
# color plots in latent
COLORPLOTSinLATENT(tsne,imgs_all_tr.shape[0],'t-sne of 8d latent space', 'tsne')
plt.savefig(path + 'tsne2_of_latent.png', dpi=300)
modidx, prt = 4, 3
title = 'Trajectories in the latent manifold'
TRAJECTORIESinLATENT(tsne,modidx,prt,title,'tsne')
#plt.savefig('traj_tsne2_of_latent.png', dpi=300)
#%% UMAP visualization
import umap
reducer = umap.UMAP(n_components=2)
mapper = reducer.fit(latent_combine)
COLORPLOTSinLATENT(mapper.embedding_,imgs_all_tr.shape[0], 'umap of 8d latent space', 'umap')
plt.savefig(path + 'umap_of_latent.png', dpi=150)
modidx, prt = 4, 3
title = 'Trajectories in the latent manifold'