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vis_util.py
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vis_util.py
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import torchvision
import torchvision.datasets as dset
import torchvision.transforms as T
import torchvision.models as models
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import time
import os
import copy
import numpy as np
def check_accuracy_vis(prefix,loader, model, device, plot=True):
print('Checking accuracy on sequential validation set')
model.eval() # set model to evaluation mode
count = 0
score_array = np.empty((0,14))
gt_array = np.empty((0,14))
plt.figure()
with torch.no_grad():
for x, y in loader:
x = x.to(device=device, dtype=torch.float) # move to device, e.g. CPU
y = y.to(device=device, dtype=torch.float)
scores = model(x)
loss_fn = torch.nn.MSELoss(reduction='mean')
loss = loss_fn(scores,y)
scores = scores.to(device="cpu",dtype=torch.float)
y = y.to(device = "cpu", dtype = torch.float)
if plot:
plt.plot(range(count, len(scores) + count), scores.numpy()[:,0:3], 'b')
plt.plot(range(count, len(scores) + count), y.numpy()[:,0:3], 'r')
# append our results
score_array = np.vstack((score_array,scores.numpy()))
gt_array = np.vstack((gt_array,y.numpy()))
count = count + len(scores)
#save our results
print('saving our results...')
np.savetxt(prefix+'_vis_scores.dat', score_array, delimiter=',') # X is an array
np.savetxt(prefix+'_vis_gt.dat', gt_array, delimiter=',') # X is an array
print('MSE loss is: %f ' % loss)
plt.show()