def evaluate(architecture, waves, trues, labels, infos, gpu_id, waveFs,
fileParam):
if cupy is not None and gpu_id >= 0:
xp = cupy
cupy.cuda.Device(gpu_id).use()
else:
xp = np
valIndex = coreTestIndex(infos)
devBatchSizeUpper = 2**8
devSegmentSecUpper = 0.1
devSegmentLenUpper = int(devSegmentSecUpper * waveFs)
devIndex = sorted(valIndex, key=lambda i: len(waves[i]))
devIndex = np.array(devIndex)
devBatchIndex = np.array_split(
devIndex, int(np.ceil(len(devIndex) / devBatchSizeUpper)))
devLabelSize = np.zeros(len(labels), int32)
for i in devIndex:
for li, la in enumerate(labels):
devLabelSize[li] += (trues[i] == li).sum()
net = Net(len(labels), architecture, functions.elu)
serializers.load_hdf5(fileParam, net)
if gpu_id >= 0: net.to_gpu(gpu_id)
inputLength = totalInputLength(architecture)
with chainer.using_config("enable_backprop", False):
confusion = np.zeros((len(labels), len(labels)), int32)
for index in devBatchIndex:
waveLen = len(waves[index[-1]])
segmentTimes = np.array_split(
np.arange(waveLen), int(np.ceil(waveLen / devSegmentLenUpper)))
net.reset()
for si, segTime in enumerate(segmentTimes):
t0 = segTime[0]
t1 = segTime[-1] + 1
x = np.zeros((len(index), t1 - t0), float32)
tr = -np.ones((len(index), t1 - t0), int32)
for xi, wi in enumerate(index):
if len(waves[wi]) > t0:
w = waves[wi][t0:t1]
x[xi, :len(w)] = w
if len(waves[wi]) > t0: tr[xi, :len(w)] = trues[wi][t0:t1]
x = x[:, newaxis, :, newaxis]
x = xp.asarray(x)
x = Variable(x)
x = net(x, False)
x = xp.argmax(x.data, axis=1)
if cupy is not None: x = cupy.asnumpy(x)
x = x.flatten()
tr = tr.flatten()
for xi, ti in zip(x, tr):
if ti >= 0: confusion[ti, xi] += 1
assert (np.sum(confusion, axis=1) == devLabelSize).all()
return confusion
def evaluate(architecture, waves, infos, gpu_id, waveFs, fileParam):
if cupy is not None and gpu_id >= 0:
xp = cupy
cupy.cuda.Device(gpu_id).use()
else:
xp = np
inputLength = totalInputLength(architecture)
labels = getLabels()
numLabel = len(labels)
groupFold = ((0, 1, 2), (3, ), (4, ))
devSize = 2**1
devSegmentSecUpper = 10
net = Net(numLabel, architecture, functions.elu)
serializers.load_hdf5(fileParam, net)
if gpu_id >= 0: net.to_gpu(gpu_id)
devSegmentLenUpper = int(devSegmentSecUpper * waveFs)
devFold = sorted(set(groupFold[2]))
devLabelWave = groupLabelWave((devFold, ), infos)[0]
devLabelWave = list(
itertools.chain.from_iterable([[(li, i) for i in devLabelWave[la]]
for li, la in enumerate(labels)]))
devLabelWave = sorted(devLabelWave, key=lambda lw: len(waves[lw[1]]))
devBatchIndex = np.array_split(np.arange(len(devLabelWave)),
int(np.ceil(len(devLabelWave) / devSize)))
devLabelSize = np.zeros(numLabel, int32)
for li, wi in devLabelWave:
devLabelSize[li] += len(waves[wi])
devWaves = {}
for li, wi in devLabelWave:
wave = waves[wi]
wave = np.concatenate((wave, np.zeros((inputLength - 1) // 2,
float32)))
devWaves[wi] = wave
with chainer.using_config("enable_backprop", False):
confusion = np.zeros((numLabel, numLabel), int32)
for bi, index in enumerate(devBatchIndex):
waveIndex = np.array([devLabelWave[i][1] for i in index])
tru = np.array([devLabelWave[i][0] for i in index])
waveLen = len(devWaves[waveIndex[-1]])
segmentTimes = np.array_split(
np.arange(waveLen), int(np.ceil(
(waveLen) / devSegmentLenUpper)))
net.reset()
for si, segTime in enumerate(segmentTimes):
t0 = segTime[0]
t1 = segTime[-1] + 1
x = np.zeros((len(index), t1 - t0), float32)
tr = -np.ones((len(index), t1 - t0), int32)
for xi, wi in enumerate(waveIndex):
if len(devWaves[wi]) <= t0: continue
w = devWaves[wi][t0:t1]
x[xi, :len(w)] = w
tr[xi, :len(w)] = tru[xi]
if t0 < (inputLength - 1) // 2:
tr[:, :(inputLength - 1) // 2 - t0] = -1
x = x[:, newaxis, :, newaxis]
x = xp.asarray(x)
x = Variable(x)
x = net(x, False)
x.unchain_backward()
x = xp.argmax(x.data, axis=1)
if gpu_id >= 0: x = cupy.asnumpy(x)
x = x.flatten()
tr = tr.flatten()
for xi, ti in zip(x[tr >= 0], tr[tr >= 0]):
confusion[ti, xi] += 1
net.reset()
assert (np.sum(confusion, axis=1) == devLabelSize).all()
return confusion
