def val(dataset, criterion, max_iter=1000):
print('Start val')
data_loader = torch.utils.data.DataLoader(
dataset, shuffle=False, batch_size=opt.batchSize, num_workers=int(opt.workers)) # opt.batchSize
val_iter = iter(data_loader)
max_iter = min(max_iter, len(data_loader))
n_correct = 0
n_total = 0
loss_avg = utils.averager()
for i in range(max_iter):
data = val_iter.next()
if opt.BidirDecoder:
cpu_images, cpu_texts, cpu_texts_rev = data
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts, scanned=True)
t_rev, _ = converter.encode(cpu_texts_rev, scanned=True)
utils.loadData(text, t)
utils.loadData(text_rev, t_rev)
utils.loadData(length, l)
preds0, preds1 = MORAN(image, length, text, text_rev, test=True)
cost = criterion(torch.cat([preds0, preds1], 0), torch.cat([text, text_rev], 0))
preds0_prob, preds0 = preds0.max(1)
preds0 = preds0.view(-1)
preds0_prob = preds0_prob.view(-1)
sim_preds0 = converter.decode(preds0.data, length.data)
preds1_prob, preds1 = preds1.max(1)
preds1 = preds1.view(-1)
preds1_prob = preds1_prob.view(-1)
sim_preds1 = converter.decode(preds1.data, length.data)
sim_preds = []
for j in range(cpu_images.size(0)):
text_begin = 0 if j == 0 else length.data[:j].sum()
if torch.mean(preds0_prob[text_begin:text_begin+len(sim_preds0[j].split('$')[0]+'$')]).data[0] >\
torch.mean(preds1_prob[text_begin:text_begin+len(sim_preds1[j].split('$')[0]+'$')]).data[0]:
sim_preds.append(sim_preds0[j].split('$')[0]+'$')
else:
sim_preds.append(sim_preds1[j].split('$')[0][-1::-1]+'$')
else:
cpu_images, cpu_texts = data
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts, scanned=True)
utils.loadData(text, t)
utils.loadData(length, l)
preds = MORAN(image, length, text, text_rev, test=True)
cost = criterion(preds, text)
_, preds = preds.max(1)
preds = preds.view(-1)
sim_preds = converter.decode(preds.data, length.data)
loss_avg.add(cost)
for pred, target in zip(sim_preds, cpu_texts):
if pred == target.lower():
n_correct += 1
n_total += 1
print("correct / total: %d / %d, " % (n_correct, n_total))
accuracy = n_correct / float(n_total)
print('Test loss: %f, : %f' % (loss_avg.val(), accuracy))
return accuracy
def val(dataset, criterion, max_iter=10000, steps=None):
data_loader = torch.utils.data.DataLoader(
dataset,
shuffle=False,
batch_size=opt.batchSize,
num_workers=int(opt.workers)) # opt.batchSize
val_iter = iter(data_loader)
max_iter = min(max_iter, len(data_loader))
n_correct = 0
n_total = 0
distance = 0.0
loss_avg = utils.averager()
# f = open('./log.txt', 'a', encoding='utf-8')
for i in range(max_iter): # 设置很大的循环数值（达不到此值就会收敛）
data = val_iter.next()
if opt.BidirDecoder:
cpu_images, cpu_texts, cpu_texts_rev = data # data是dataloader导入的东西
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts,
scanned=False) # 这个encode是将字符encode成id
t_rev, _ = converter.encode(cpu_texts_rev, scanned=False)
utils.loadData(text, t)
utils.loadData(text_rev, t_rev)
utils.loadData(length, l)
preds0, preds1 = MORAN(image,
length,
text,
text_rev,
debug=False,
test=True,
steps=steps) # 跑模型HARN
cost = criterion(torch.cat([preds0, preds1], 0),
torch.cat([text, text_rev], 0))
preds0_prob, preds0 = preds0.max(1) # 取概率最大top1的结果
preds0 = preds0.view(-1)
preds0_prob = preds0_prob.view(-1) # 维度的变形（好像是
sim_preds0 = converter.decode(preds0.data,
length.data) # 将 id decode为字
preds1_prob, preds1 = preds1.max(1)
preds1 = preds1.view(-1)
preds1_prob = preds1_prob.view(-1)
sim_preds1 = converter.decode(preds1.data, length.data)
sim_preds = [] # 预测出来的字
for j in range(cpu_images.size(0)): # 对字典进行处理，把单个字符连成字符串
text_begin = 0 if j == 0 else length.data[:j].sum()
if torch.mean(preds0_prob[text_begin:text_begin + len(sim_preds0[j].split('$')[0] + '$')]).item() > \
torch.mean(
preds1_prob[text_begin:text_begin + len(sim_preds1[j].split('$')[0] + '$')]).item():
sim_preds.append(sim_preds0[j].split('$')[0] + '$')
else:
sim_preds.append(sim_preds1[j].split('$')[0][-1::-1] +
'$')
else: # 用不到的另一种情况
cpu_images, cpu_texts = data
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts, scanned=True)
utils.loadData(text, t)
utils.loadData(length, l)
preds = MORAN(image, length, text, text_rev, test=True)
cost = criterion(preds, text)
_, preds = preds.max(1)
preds = preds.view(-1)
sim_preds = converter.decode(preds.data, length.data)
loss_avg.add(cost) # 计算loss的平均值
for pred, target in zip(
sim_preds, cpu_texts
): # 与GroundTruth的对比，cpu_texts是GroundTruth，sim_preds是连接起来的字符串
if pred == target.lower(): # 完全匹配量
n_correct += 1
# f.write("pred %s\t target %s\n" % (pred, target))
distance += levenshtein(pred, target) / max(
len(pred), len(target)) # 莱温斯坦距离
n_total += 1 # 完成了一个单词
# f.close()
# print and save # 跑完之后输出到日志中
for pred, gt in zip(sim_preds, cpu_texts):
gt = ''.join(gt.split(opt.sep))
print('%-20s, gt: %-20s' % (pred, gt))
print("correct / total: %d / %d, " % (n_correct, n_total))
print('levenshtein distance: %f' % (distance / n_total))
accuracy = n_correct / float(n_total)
log.scalar_summary('Validation/levenshtein distance',
distance / n_total, steps)
log.scalar_summary('Validation/loss', loss_avg.val(), steps)
log.scalar_summary('Validation/accuracy', accuracy, steps)
print('Test loss: %f, accuray: %f' % (loss_avg.val(), accuracy))
return accuracy
def val(dataset, criterion, max_iter=10000, steps=0):
data_loader = torch.utils.data.DataLoader(
dataset,
shuffle=False,
batch_size=args.batchSize,
num_workers=args.workers) # args.batchSize
val_iter = iter(data_loader)
max_iter = min(max_iter, len(data_loader))
n_correct = 0
n_total = 0
distance = 0.0
loss_avg = utils.averager()
f = open('logger/log.txt', 'w', encoding='utf-8')
for i in range(max_iter):
data = val_iter.next()
cpu_images, cpu_texts, cpu_texts_rev = data
# utils.loadData(image, encode_coordinates_fn(cpu_images))
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts, scanned=True)
t_rev, _ = converter.encode(cpu_texts_rev, scanned=True)
utils.loadData(text, t)
utils.loadData(text_rev, t_rev)
utils.loadData(length, l)
preds0, _, preds1, _ = MORAN(image,
length,
text,
text_rev,
debug=False,
test=True,
steps=steps)
cost = criterion(torch.cat([preds0, preds1], 0),
torch.cat([text, text_rev], 0))
preds0_prob, preds0 = preds0.max(1)
preds0 = preds0.view(-1)
preds0_prob = preds0_prob.view(-1)
sim_preds0 = converter.decode(preds0.data, length.data)
preds1_prob, preds1 = preds1.max(1)
preds1 = preds1.view(-1)
preds1_prob = preds1_prob.view(-1)
sim_preds1 = converter.decode(preds1.data, length.data)
sim_preds = []
for j in range(cpu_images.size(0)):
text_begin = 0 if j == 0 else length.data[:j].sum()
if torch.mean(preds0_prob[text_begin:text_begin + len(sim_preds0[j].split('$')[0] + '$')]).item() > \
torch.mean(preds1_prob[text_begin:text_begin + len(sim_preds1[j].split('$')[0] + '$')]).item():
sim_preds.append(sim_preds0[j].split('$')[0] + '$')
else:
sim_preds.append(sim_preds1[j].split('$')[0][-1::-1] + '$')
# img_shape = cpu_images.shape[3] / 100, cpu_images.shape[2] / 100
# input_seq = cpu_texts[0]
# output_seq = sim_preds[0]
# attention = alpha[0]
# attention_image = showAttention(input_seq, output_seq, attention, img_shape)
# log.image_summary('map/attention', [attention_image], steps)
loss_avg.add(cost)
for pred, target in zip(sim_preds, cpu_texts):
if pred == target.lower():
n_correct += 1
f.write("pred %s\t\t\t\t\ttarget %s\n" % (pred, target))
distance += levenshtein(pred, target) / max(len(pred), len(target))
n_total += 1
f.close()
accuracy = n_correct / float(n_total)
log.scalar_summary('Validation/levenshtein distance', distance / n_total,
steps)
log.scalar_summary('Validation/loss', loss_avg.val(), steps)
log.scalar_summary('Validation/accuracy', accuracy, steps)
return accuracy
