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test_recurrent.py
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/
test_recurrent.py
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import numpy as np
import matplotlib.pyplot as pl
import torch
import torch.nn as nn
import time
import model_recurrent as model
class deep_mfbd(object):
def __init__(self):
self.cuda = torch.cuda.is_available()
self.n_frames = 7
self.device = torch.device("cuda" if self.cuda else "cpu")
self.model = model.deconvolution_network(n_blocks=self.n_frames-1).to(self.device)
self.checkpoint = 'recurrent_network/2018-01-22-11:02.pth.tar'
print("=> loading checkpoint '{}'".format(self.checkpoint))
if (self.cuda):
checkpoint = torch.load(self.checkpoint)
else:
checkpoint = torch.load(self.checkpoint, map_location=lambda storage, loc: storage)
self.model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(self.checkpoint))
def test(self):
arcsec_per_px = 0.059
self.model.eval()
ims = np.expand_dims(np.load('data/ims.npy'), axis=0)
data = torch.from_numpy((ims / 1e3).astype('float32')).to(self.device)
with torch.no_grad():
start = time.time()
out = self.model(data)
print('Elapsed time : {0} s'.format(time.time()-start))
out = 1e3 * np.squeeze(out[-1].to("cpu").data.numpy())
pl.close('all')
fig, ax = pl.subplots(ncols=2, nrows=2, figsize=(10,10))
ax[0,0].imshow(ims[0,0,:,:], extent=(0,960*arcsec_per_px,0,960*arcsec_per_px))
ax[0,1].imshow(out, extent=(0,960*arcsec_per_px,0,960*arcsec_per_px))
ax[1,0].imshow(ims[0,0,100:500,100:500], extent=(0,400*arcsec_per_px,0,400*arcsec_per_px))
ax[1,1].imshow(out[100:500,100:500], extent=(0,400*arcsec_per_px,0,400*arcsec_per_px))
ax[0,0].set_title('Frame')
ax[0,1].set_title('NN')
pl.show()
if (__name__ == '__main__'):
deep_mfbd_network = deep_mfbd()
deep_mfbd_network.test()