def calculate_mIoU_accuracy(pred, labelBatch, labelIndexBatch, maskBatch, numClasses):
'''
Calculage mIoU during training, given the temporary results during during
code mainly borrowd from test.py
'''
confcounts = np.zeros( (numClasses, numClasses), dtype=np.int64 )
accuracy = np.zeros(numClasses, dtype=np.float32 )
# Compute mean IOU
hist = utils.computeAccuracy(pred, labelIndexBatch, maskBatch )
confcounts += hist
for n in range(0, numClasses):
rowSum = np.sum(confcounts[n, :] )
colSum = np.sum(confcounts[:, n] )
interSum = confcounts[n, n]
accuracy[n] = float(100.0 * interSum) / max(float(rowSum + colSum - interSum ), 1e-5)
meanAccuracy = np.mean(accuracy )
return meanAccuracy
labelIndex_cpu = dataBatch['labelIndex' ]
labelIndexBatch.data.resize_(labelIndex_cpu.size() )
labelIndexBatch.data.copy_(labelIndex_cpu )
mask_cpu = dataBatch['mask' ]
maskBatch.data.resize_( mask_cpu.size() )
maskBatch.data.copy_( mask_cpu )
# Test network
x1, x2, x3, x4, x5 = encoder(imBatch )
pred = decoder(imBatch, x1, x2, x3, x4, x5 )
# Compute mean IOU
loss = torch.mean( pred * labelBatch )
hist = utils.computeAccuracy(pred, labelIndexBatch, maskBatch )
confcounts += hist
for n in range(0, opt.numClasses ):
rowSum = np.sum(confcounts[n, :] )
colSum = np.sum(confcounts[:, n] )
interSum = confcounts[n, n]
accuracy[n] = float(100.0 * interSum) / max(float(rowSum + colSum - interSum ), 1e-5)
# Output the log information
lossArr.append(loss.cpu().data.item() )
meanLoss = np.mean(np.array(lossArr[:] ) )
meanAccuracy = np.mean(accuracy )
print('Epoch %d iteration %d: Loss %.5f Accumulated Loss %.5f' \
% ( epoch, iteration, lossArr[-1], meanLoss ) )
def valid_model(in_path, da_path, sum_path,sess):
#return accuracy for dialogue act, rouge-1,2,3,L for summary
#some useful items are also calculated
#da_outputs, correct_das: predicted / ground truth of dialogue act
rouge_1 = []
rouge_2 = []
rouge_3 = []
rouge_L = []
da_outputs = []
correct_das = []
data_processor_valid = DataProcessor(in_path, da_path, sum_path, in_vocab, da_vocab)
while True:
#get a batch of data
in_data, da_data, da_weight, length, sums, sum_weight,sum_lengths, in_seq, da_seq, sum_seq = data_processor_valid.get_batch(batch_size)
feed_dict = {input_data.name: in_data, sequence_length.name: length, sum_length.name: sum_lengths}
if data_processor_valid.end != 1 or in_data:
ret = sess.run(inference_outputs, feed_dict)
#summary part
pred_sums = []
correct_sums = []
for batch in ret[1]:
tmp = []
for time_i in batch:
tmp.append(np.argmax(time_i))
pred_sums.append(tmp)
for i in sums:
correct_sums.append(i.tolist())
for pred,corr in zip(pred_sums,correct_sums):
rouge_score_map = rouge.rouge(pred,corr)
rouge1 = 100*rouge_score_map['rouge_1/f_score']
rouge2 = 100*rouge_score_map['rouge_2/f_score']
rouge3 = 100*rouge_score_map['rouge_3/f_score']
rougeL = 100*rouge_score_map['rouge_l/f_score']
rouge_1.append(rouge1)
rouge_2.append(rouge2)
rouge_3.append(rouge3)
rouge_L.append(rougeL)
#dialogue act part
pred_das = ret[0].reshape((da_data.shape[0], da_data.shape[1], -1))
for p, t, i, l in zip(pred_das, da_data, in_data, length):
p = np.argmax(p, 1)
tmp_pred = []
tmp_correct = []
for j in range(l):
tmp_pred.append(da_vocab['rev'][p[j]])
tmp_correct.append(da_vocab['rev'][t[j]])
da_outputs.append(tmp_pred)
correct_das.append(tmp_correct)
if data_processor_valid.end == 1:
break
precision = computeAccuracy(correct_das, da_outputs)
logging.info('da precision: ' + str(precision))
logging.info('sum rouge1: ' + str(np.mean(rouge_1)))
logging.info('sum rouge2: ' + str(np.mean(rouge_2)))
logging.info('sum rouge3: ' + str(np.mean(rouge_3)))
logging.info('sum rougeL: ' + str(np.mean(rouge_L)))
data_processor_valid.close()
return np.mean(rouge_1),np.mean(rouge_2),np.mean(rouge_3),np.mean(rouge_L),precision
maskBatch.data.copy_(mask_cpu)
# Train network
optEncoder.zero_grad()
optDecoder.zero_grad()
x1, x2, x3, x4, x5 = encoder(imBatch)
pred = decoder(imBatch, x1, x2, x3, x4, x5)
loss = torch.mean(pred * labelBatch)
loss.backward()
optEncoder.step()
optDecoder.step()
confcounts = utils.computeAccuracy(pred, labelIndexBatch, maskBatch)
accuracy = np.zeros(opt.numClasses, dtype=np.float32)
for n in range(0, opt.numClasses):
rowSum = np.sum(confcounts[n, :])
colSum = np.sum(confcounts[:, n])
interSum = confcounts[n, n]
accuracy[n] = float(100.0 * interSum) / max(
float(rowSum + colSum - interSum), 1e-5)
# Output the log information
lossArr.append(loss.cpu().data.item())
accuracyArr.append(np.mean(accuracy))
if iteration >= 1000:
meanLoss = np.mean(np.array(lossArr[-1000:]))
meanAccuracy = np.mean(np.array(accuracyArr[-1000:]))
