def nao_valid(queue, model):
inputs = []
targets = []
predictions = []
archs = []
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
inputs += encoder_input.data.squeeze().tolist()
targets += encoder_target.data.squeeze().tolist()
predictions += predict_value.data.squeeze().tolist()
archs += arch.data.squeeze().tolist()
pa = utils.pairwise_accuracy(targets, predictions)
hd = utils.hamming_distance(inputs, archs)
return pa, hd
def nao_valid(queue, model):
pa = utils.AvgrageMeter()
hs = utils.AvgrageMeter()
with torch.no_grad():
model.eval()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
encoder_input = encoder_input.cuda()
encoder_target = encoder_target.cuda()
decoder_target = decoder_target.cuda()
predict_value, logits, arch = model(encoder_input)
n = encoder_input.size(0)
pairwise_acc = utils.pairwise_accuracy(
encoder_target.data.squeeze().tolist(),
predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(
decoder_target.data.squeeze().tolist(),
arch.data.squeeze().tolist())
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return pa.avg, hs.avg
def nao_valid(queue, model):
pa = utils.AvgMeter()
hs = utils.AvgMeter()
mse = utils.AvgMeter()
for step, sample in enumerate(queue):
encoder_input = sample['encoder_input']
encoder_target = sample['encoder_target']
decoder_target = sample['decoder_target']
predict_value, logits, arch = model(encoder_input)
n = encoder_input.shape[0]
pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
predict_value.data.squeeze().tolist())
hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
mse.update(keras.losses.MSE(encoder_target.data.squeeze(), predict_value.data.squeeze()), n)
pa.update(pairwise_acc, n)
hs.update(hamming_dis, n)
return mse.avg, pa.avg, hs.avg
