def test_nlm3_fast():
from imgtools import test_images, calcPSNR, read3dTiff
dev = OCLDevice(useDevice=1)
# data = test_images.blobs64()
data = read3dTiff("/Users/mweigert/Data/synthetics/blobs64.tif")
np.random.seed(0)
data = 1000.*data/np.amax(data)
y = np.maximum(0,data + np.random.normal(0,200,data.shape))
# y = y.astype(np.uint16)
y = y.astype(np.float32)
t = time()
out = nlm3_fast(dev,y,2,3,200)
print "time:", time()-t
print "PSNR: ",utils.calcPSNR(data,out)
# sigs = np.linspace(3,70,30)
# bests = []
# for s0 in np.linspace(2,20,10):
# y = data + np.random.normal(0,s0,data.shape)
# y = y.astype(np.float32)
# sigs = s0*np.linspace(.1,5 ,20)
# ind=np.argmax([calcPSNR(data,nlm3_fast(dev,y,2,3,s)) for s in sigs])
# print s0,sigs[ind]
# bests.append([s0,sigs[ind]])
return out
def test_nlm3_fast():
from imgtools import test_images, calcPSNR, read3dTiff
dev = OCLDevice(useDevice=1)
# data = test_images.blobs64()
data = read3dTiff("/Users/mweigert/Data/synthetics/blobs64.tif")
np.random.seed(0)
data = 1000.*data/np.amax(data)
y = np.maximum(0,data + np.random.normal(0,200,data.shape))
# y = y.astype(np.uint16)
y = y.astype(np.float32)
t = time()
out = nlm3_fast(dev,y,2,3,200)
print "time:", time()-t
print "PSNR: ",utils.calcPSNR(data,out)
# sigs = np.linspace(3,70,30)
# bests = []
# for s0 in np.linspace(2,20,10):
# y = data + np.random.normal(0,s0,data.shape)
# y = y.astype(np.float32)
# sigs = s0*np.linspace(.1,5 ,20)
# ind=np.argmax([calcPSNR(data,nlm3_fast(dev,y,2,3,s)) for s in sigs])
# print s0,sigs[ind]
# bests.append([s0,sigs[ind]])
return out
def test_tv3_gpu():
from imgtools import test_images, calcPSNR, read3dTiff
dev = OCLDevice(useDevice=1)
data = read3dTiff("/Users/mweigert/Data/synthetics/blobs64.tif")
np.random.seed(0)
data = 100.*data/np.amax(data)
y = np.maximum(0,data + np.random.normal(0,0.2*np.amax(data),data.shape))
y = y.astype(np.float32)
t = time()
out = tv3_gpu(dev,y,.4*np.amax(data))
print "time:", time()-t
print "PSNR: ",utils.calcPSNR(data,out)
return data,y,out
def test_tv3_gpu():
from imgtools import test_images, calcPSNR, read3dTiff
dev = OCLDevice(useDevice=1)
data = read3dTiff("/Users/mweigert/Data/synthetics/blobs64.tif")
np.random.seed(0)
data = 100.*data/np.amax(data)
y = np.maximum(0,data + np.random.normal(0,0.2*np.amax(data),data.shape))
y = y.astype(np.float32)
t = time()
out = tv3_gpu(dev,y,.4*np.amax(data))
print "time:", time()-t
print "PSNR: ",utils.calcPSNR(data,out)
return data,y,out
def test(self):
self.model.eval()
self.checkpoint.saveLog('\nEvaluation:')
epoch = self.checkpoint.getEpoch()
scale = self.scale[0]
testTimer = utils.timer()
self.checkpoint.addLog(torch.zeros(1, len(self.scale)), False)
testTimer.tic()
for scaleIdx in range(len(self.scale)):
scale = self.scale[scaleIdx]
self.scaleChange(scaleIdx, self.testLoader)
for imgIdx, (input, target, _) in enumerate(self.testLoader):
input, target = self.prepareData(input, target, volatile=True)
# Self ensemble!
if self.args.selfEnsemble:
output = utils.x8Forward(input, self.model,
self.args.precision)
else:
output = self.model(input)
evalValue = utils.calcPSNR(output, target,
self.testLoader.dataset.name,
self.args.rgbRange, scale)
self.checkpoint.testLog[-1, scaleIdx] \
+= evalValue / len(self.testLoader)
self.checkpoint.saveResults(imgIdx, input, output, target,
scale)
bestValue, bestEpoch = self.checkpoint.testLog.max(0)
performance = 'PSNR: {:.3f}'.format(
self.checkpoint.testLog[-1, scaleIdx])
self.checkpoint.saveLog(
'[SR on {} x{}]\t{} (Best: {:.3f} from epoch {})'.format(
self.testLoader.dataset.name, scale, performance,
bestValue.squeeze(0)[scaleIdx],
bestEpoch.squeeze(0)[scaleIdx] + 1))
self.checkpoint.saveLog('Time: {:.2f}s\n'.format(testTimer.toc()),
refresh=True)
self.checkpoint.save(self, epoch)
