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(net, criterion, eval_data_batch):
print('Start val')
for p in crnn.parameters():
p.requires_grad = False
net.eval()
n_correct = 0
loss_avg_eval = utils.averager()
for data in eval_data_batch:
cpu_images, cpu_texts = data
batch_size = cpu_images.size(0)
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts)
utils.loadData(text, t)
utils.loadData(length, l)
preds = crnn(image)
preds_size = Variable(torch.LongTensor([preds.size(0)] * batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
loss_avg_eval.add(cost) # 计算loss
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
cpu_texts_decode = []
for i in cpu_texts:
cpu_texts_decode.append(i)
for pred, target in zip(sim_preds, cpu_texts_decode): # 计算准确率
if pred == target:
n_correct += 1
raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.n_val_disp]
for raw_pred, pred, gt in zip(raw_preds, sim_preds, cpu_texts_decode):
print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
accuracy = n_correct / float(len(eval_dataset))
print('Val loss: %f, accuray: %f' % (loss_avg.val(), accuracy))
if opt.cuda:
MORAN.cuda()
MORAN = torch.nn.DataParallel(MORAN, device_ids=range(opt.ngpu))
image = image.cuda()
text = text.cuda()
text_rev = text_rev.cuda()
criterion = criterion.cuda()
image = Variable(image)
text = Variable(text)
text_rev = Variable(text_rev)
length = Variable(length)
# loss averager
loss_avg = utils.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(MORAN.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
elif opt.adadelta:
optimizer = optim.Adadelta(MORAN.parameters(), lr=opt.lr)
elif opt.sgd:
optimizer = optim.SGD(MORAN.parameters(), lr=opt.lr, momentum=0.9)
else:
optimizer = optim.RMSprop(MORAN.parameters(), lr=opt.lr)
def val(dataset, criterion, max_iter=1000):
if opt.cuda:
MODEL.cuda()
MODEL = torch.nn.DataParallel(MODEL, device_ids=range(opt.ngpu))
text1_ori = text1_ori.cuda()
text2_ori = text2_ori.cuda()
criterion = criterion.cuda()
length_ori = length_ori.cuda()
image = Variable(image)
length_ori = Variable(length_ori)
text1_ori = Variable(text1_ori)
text2_ori = Variable(text2_ori)
# loss averager
loss_avg = utils.averager()
loss_pred_avg1 = utils.averager()
loss_pred_avg2 = utils.averager()
optimizer = optim.Adadelta(filter(lambda p: p.requires_grad,
MODEL.parameters()),
lr=opt.lr)
toPIL = transforms.ToPILImage()
toTensor = transforms.ToTensor()
def val_beam(dataset, max_iter=9999):
rotate90 = dataset.ifRotate90
data_loader = torch.utils.data.DataLoader(dataset,
shuffle=False,
def train_moran_v2(config_file):
import sys
sys.path.append('./recognition_model/MORAN_V2')
import argparse
import random
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import os
import tools.utils as utils
import tools.dataset as dataset
import time
from collections import OrderedDict
from models.moran import MORAN
from alphabet.wordlist import result
from yacs.config import CfgNode as CN
# from wordlistart import result
from alphabet.wordlistlsvt import result
def read_config_file(config_file):
# 用yaml重构配置文件
f = open(config_file)
opt = CN.load_cfg(f)
return opt
opt = read_config_file(config_file)
# Modify
opt.alphabet = result
assert opt.ngpu == 1, "Multi-GPU training is not supported yet, due to the variant lengths of the text in a batch."
if opt.experiment is None:
opt.experiment = 'expr'
os.system('mkdir {0}'.format(opt.experiment))
opt.manualSeed = random.randint(1, 10000) # fix seed
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
np.random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
cudnn.benchmark = True
print(opt)
if not torch.cuda.is_available():
assert not opt.cuda, 'You don\'t have a CUDA device.'
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
train_nips_dataset = dataset.lmdbDataset(root=opt.train_nips,
transform=dataset.resizeNormalize((opt.imgW, opt.imgH)), reverse=opt.BidirDecoder)
assert train_nips_dataset
'''
train_cvpr_dataset = dataset.lmdbDataset(root=opt.train_cvpr,
transform=dataset.resizeNormalize((opt.imgW, opt.imgH)), reverse=opt.BidirDecoder)
assert train_cvpr_dataset
'''
'''
train_dataset = torch.utils.data.ConcatDataset([train_nips_dataset, train_cvpr_dataset])
'''
train_dataset = train_nips_dataset
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batchSize,
shuffle=False, sampler=dataset.randomSequentialSampler(train_dataset, opt.batchSize),
num_workers=int(opt.workers))
test_dataset = dataset.lmdbDataset(root=opt.valroot,
transform=dataset.resizeNormalize((opt.imgW, opt.imgH)), reverse=opt.BidirDecoder)
nclass = len(opt.alphabet.split(opt.sep))
nc = 1
converter = utils.strLabelConverterForAttention(opt.alphabet, opt.sep)
criterion = torch.nn.CrossEntropyLoss()
if opt.cuda:
MORAN = MORAN(nc, nclass, opt.nh, opt.targetH, opt.targetW, BidirDecoder=opt.BidirDecoder, CUDA=opt.cuda)
else:
MORAN = MORAN(nc, nclass, opt.nh, opt.targetH, opt.targetW, BidirDecoder=opt.BidirDecoder, inputDataType='torch.FloatTensor', CUDA=opt.cuda)
if opt.MORAN != '':
print('loading pretrained model from %s' % opt.MORAN)
if opt.cuda:
state_dict = torch.load(opt.MORAN)
else:
state_dict = torch.load(opt.MORAN, map_location='cpu')
MORAN_state_dict_rename = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "") # remove `module.`
MORAN_state_dict_rename[name] = v
MORAN.load_state_dict(MORAN_state_dict_rename, strict=True)
image = torch.FloatTensor(opt.batchSize, nc, opt.imgH, opt.imgW)
text = torch.LongTensor(opt.batchSize * 5)
text_rev = torch.LongTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
if opt.cuda:
MORAN.cuda()
MORAN = torch.nn.DataParallel(MORAN, device_ids=range(opt.ngpu))
image = image.cuda()
text = text.cuda()
text_rev = text_rev.cuda()
criterion = criterion.cuda()
image = Variable(image)
text = Variable(text)
text_rev = Variable(text_rev)
length = Variable(length)
# loss averager
loss_avg = utils.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(MORAN.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
elif opt.adadelta:
optimizer = optim.Adadelta(MORAN.parameters(), lr=opt.lr)
elif opt.sgd:
optimizer = optim.SGD(MORAN.parameters(), lr=opt.lr, momentum=0.9)
else:
optimizer = optim.RMSprop(MORAN.parameters(), lr=opt.lr)
def levenshtein(s1, s2):
if len(s1) < len(s2):
return levenshtein(s2, s1)
# len(s1) >= len(s2)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
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
distance = 0.0
loss_avg = utils.averager()
f = open('./log.txt','a',encoding='utf-8')
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
f.write("预测 %s 目标 %s\n" % ( pred,target ) )
distance += levenshtein(pred,target) / max(len(pred),len(target))
n_total += 1
f.close()
print("correct / total: %d / %d, " % (n_correct, n_total))
print('levenshtein distance: %f' % (distance/n_total))
accuracy = n_correct / float(n_total)
print('Test loss: %f, accuray: %f' % (loss_avg.val(), accuracy))
return accuracy
def trainBatch():
data = train_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)
cost = criterion(torch.cat([preds0, preds1], 0), torch.cat([text, text_rev], 0))
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)
cost = criterion(preds, text)
MORAN.zero_grad()
cost.backward()
optimizer.step()
return cost
t0 = time.time()
acc = 0
acc_tmp = 0
for epoch in range(opt.niter):
train_iter = iter(train_loader)
i = 0
while i < len(train_loader):
# print("main函数里,可迭代次数为 %d" % len(train_loader))
if i % opt.valInterval == 0:
for p in MORAN.parameters():
p.requires_grad = False
MORAN.eval()
acc_tmp = val(test_dataset, criterion)
if acc_tmp > acc:
acc = acc_tmp
torch.save(MORAN.state_dict(), '{0}/{1}_{2}.pth'.format(
opt.experiment, i, str(acc)[:6]))
if i % opt.saveInterval == 0:
torch.save(MORAN.state_dict(), '{0}/{1}_{2}.pth'.format(
opt.experiment, epoch, i))
for p in MORAN.parameters():
p.requires_grad = True
MORAN.train()
cost = trainBatch()
loss_avg.add(cost)
if i % opt.displayInterval == 0:
t1 = time.time()
print ('Epoch: %d/%d; iter: %d/%d; Loss: %f; time: %.2f s;' %
(epoch, opt.niter, i, len(train_loader), loss_avg.val(), t1-t0)),
loss_avg.reset()
t0 = time.time()
i += 1
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 train_HARN(config_file):
import sys
sys.path.append('./recognition_model/HARN')
import argparse
import os
import random
import io
import sys
import time
from models.moran import MORAN
import tools.utils as utils
import torch.optim as optim
import numpy as np
import torch.backends.cudnn as cudnn
import torch.utils.data
import tools.dataset as dataset
from torch.autograd import Variable
from collections import OrderedDict
from tools.logger import logger
# from wordlist import result
# from wordlistlsvt import result
import warnings
warnings.filterwarnings('ignore')
# os.environ['CUDA_VISIBLE_DEVICES'] = '1' # 指定GPU
# os.environ['CUDA_VISIBLE_DEVICES'] = '5' # 指定GPU
from yacs.config import CfgNode as CN
def read_config_file(config_file):
# 用yaml重构配置文件
f = open(config_file)
opt = CN.load_cfg(f)
return opt
opt = read_config_file(config_file) # 获取了yaml文件
print("配置文件", opt)
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
# Modify
# opt.alphabet = result
assert opt.ngpu == 1, "Multi-GPU training is not supported yet, due to the variant lengths of the text in a batch."
if opt.experiment is None:
opt.experiment = 'expr'
os.system('mkdir {0}'.format(opt.experiment))
opt.manualSeed = random.randint(1, 10000) # fix seed
random.seed(opt.manualSeed)
np.random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
cudnn.benchmark = True
# ---------save logger---------#
log = logger('./asrn_se50_OCRdata_50_logger')
# log = logger('./logger/asrn_se50_lsvt_50') # # 保存日志的路径 / 需要改
# -----------------------------#
if not torch.cuda.is_available():
assert not opt.cuda, 'You don\'t have a CUDA device.'
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
train_nips_dataset = dataset.lmdbDataset(root=opt.train_nips,
transform=dataset.resizeNormalize(
(opt.imgW, opt.imgH)),
reverse=opt.BidirDecoder,
alphabet=opt.alphabet)
assert train_nips_dataset
'''
train_cvpr_dataset = dataset.lmdbDataset(root=opt.train_cvpr,
transform=dataset.resizeNormalize((opt.imgW, opt.imgH)), reverse=opt.BidirDecoder)
assert train_cvpr_dataset
'''
'''
train_dataset = torch.utils.data.ConcatDataset([train_nips_dataset, train_cvpr_dataset])
'''
train_dataset = train_nips_dataset
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=opt.batchSize,
shuffle=False,
sampler=dataset.randomSequentialSampler(train_dataset, opt.batchSize),
num_workers=int(opt.workers))
test_dataset = dataset.lmdbDataset(root=opt.valroot,
transform=dataset.resizeNormalize(
(opt.imgW, opt.imgH)),
reverse=opt.BidirDecoder)
nclass = len(
opt.alphabet.split(opt.sep)
) # 一共有多少类，英文是36，中文就是wordlist，系统只认名字为wordlist.py的文件，记得将需要用的文件改为这个名字
nc = 1
converter = utils.strLabelConverterForAttention(
opt.alphabet, opt.sep) # 给每个字一个编号，例如：中(2)国(30)人(65)；convert是id和字符之间的转换
criterion = torch.nn.CrossEntropyLoss()
if opt.cuda:
MORAN = MORAN(nc,
nclass,
opt.nh,
opt.targetH,
opt.targetW,
BidirDecoder=opt.BidirDecoder,
CUDA=opt.cuda,
log=log)
else:
MORAN = MORAN(nc,
nclass,
opt.nh,
opt.targetH,
opt.targetW,
BidirDecoder=opt.BidirDecoder,
inputDataType='torch.FloatTensor',
CUDA=opt.cuda,
log=log)
if opt.MORAN != '':
print('loading pretrained model from %s' % opt.MORAN)
if opt.cuda:
state_dict = torch.load(opt.MORAN)
else:
state_dict = torch.load(opt.MORAN, map_location='cpu')
MORAN_state_dict_rename = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "") # remove `module.`
MORAN_state_dict_rename[name] = v
MORAN.load_state_dict(MORAN_state_dict_rename, strict=True)
image = torch.FloatTensor(opt.batchSize, nc, opt.imgH, opt.imgW)
text = torch.LongTensor(opt.batchSize * 5)
text_rev = torch.LongTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
if opt.cuda:
MORAN.cuda()
MORAN = torch.nn.DataParallel(MORAN, device_ids=range(opt.ngpu))
image = image.cuda()
text = text.cuda()
text_rev = text_rev.cuda()
criterion = criterion.cuda()
image = Variable(image) # 把图片转换成 CUDA 可以识别的 Variable 变量
text = Variable(text)
text_rev = Variable(text_rev)
length = Variable(length)
# loss averager
loss_avg = utils.averager()
# setup optimizer # 优化器的选择，这里用的Adam
if opt.adam:
optimizer = optim.Adam(MORAN.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
elif opt.adadelta:
optimizer = optim.Adadelta(MORAN.parameters(), lr=opt.lr)
elif opt.sgd:
optimizer = optim.SGD(MORAN.parameters(), lr=opt.lr, momentum=0.9)
else:
optimizer = optim.RMSprop(MORAN.parameters(), lr=opt.lr)
def levenshtein(s1, s2): # 莱温斯坦距离，编辑距离的一种
if len(s1) < len(s2):
return levenshtein(s2, s1)
# len(s1) >= len(s2)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
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 trainBatch(steps):
data = train_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)
cost = criterion(torch.cat([preds0, preds1], 0),
torch.cat([text, text_rev], 0))
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)
cost = criterion(preds, text)
MORAN.zero_grad()
cost.backward() # 反向传播
optimizer.step() # 优化器
return cost
t0 = time.time()
acc, acc_tmp = 0, 0
print(' === HARN === ')
for epoch in range(opt.niter):
print(" === Loading Train Data === ")
train_iter = iter(train_loader)
i = 0
print(" === start training === ")
while i < len(train_loader): # len()：数据大小
# print("main函数里,可迭代次数为 %d" % len(train_loader))
steps = i + epoch * len(train_loader) # step用来计算什么时候进行存储/打印
if steps % opt.valInterval == 0:
for p in MORAN.parameters():
p.requires_grad = False
MORAN.eval()
print('---------------Please Waiting----------------'
) # train的一些打印信息
acc_tmp = val(test_dataset, criterion, steps=steps)
if acc_tmp > acc:
acc = acc_tmp
try:
time.sleep(0.01)
torch.save(
MORAN.state_dict(),
'{0}/{1}_{2}.pth'.format(opt.experiment, i,
str(acc)[:6]))
print(".pth")
except RuntimeError:
print("RuntimeError")
pass
for p in MORAN.parameters():
p.requires_grad = True
MORAN.train()
cost = trainBatch(steps)
loss_avg.add(cost)
t1 = time.time() # niter是参数部分设置的epoch数量
print('Epoch: %d/%d; iter: %d/%d; Loss: %f; time: %.2f s;' %
(epoch, opt.niter, i, len(train_loader), loss_avg.val(),
t1 - t0)),
log.scalar_summary('train loss', loss_avg.val(),
steps) # 拟合到90多/拟合到1，完全收敛，训练充分
log.scalar_summary('speed batches/persec',
steps / (time.time() - t0), steps)
loss_avg.reset()
t0 = time.time()
'''
if i % 100 == 0:
t1 = time.time() # niter是参数部分设置的epoch数量
print('Epoch: %d/%d; iter: %d/%d; Loss: %f; time: %.2f s;' %
(epoch, opt.niter, i, len(train_loader), loss_avg.val(), t1 - t0)),
log.scalar_summary('train loss', loss_avg.val(), i) # 拟合到90多/拟合到1，完全收敛，训练充分
log.scalar_summary('speed batches/persec', i / (time.time() - t0), i)
loss_avg.reset()
t0 = time.time()
'''
'''
if steps % opt.displayInterval == 0:
t1 = time.time() # niter是参数部分设置的epoch数量
print('Epoch: %d/%d; iter: %d/%d; Loss: %f; time: %.2f s;' %
(epoch, opt.niter, i, len(train_loader), loss_avg.val(), t1 - t0)),
log.scalar_summary('train loss', loss_avg.val(), steps) # 拟合到90多/拟合到1，完全收敛，训练充分
log.scalar_summary('speed batches/persec', steps / (time.time() - t0), steps)
loss_avg.reset()
t0 = time.time()
'''
i += 1
MODEL = MODEL(nclass) image = torch.FloatTensor(args.batchSize, 1, args.imgH, args.imgW) ori_label = torch.IntTensor(args.batchSize) label = torch.IntTensor(args.batchSize, nclass) if args.cuda: MODEL.cuda() MODEL = torch.nn.DataParallel(MODEL, device_ids=range(args.ngpu)) image = image.cuda() ori_label = ori_label.cuda() label = label.cuda() criterion = criterion.cuda() # loss averager loss_pred = utils.averager() optimizer = optim.Adadelta(filter(lambda p: p.requires_grad,MODEL.parameters()), lr=args.lr) for epoch in range(args.niter): train_iter = iter(train_loader) i = 0 while i < len(train_loader): # adjust_lr_exp(argsimizer, args.lr, i+len(train_loader)*epoch, len(train_loader)*args.niter) # show the result on val dataset # the result include acc if i % args.valInterval == 0 and epoch+float(i)/len(train_loader)>=args.val_start_epoch:
def main():
config = Config()
if not os.path.exists(config.expr_dir):
os.makedirs(config.expr_dir)
if torch.cuda.is_available() and not config.use_cuda:
print("WARNING: You have a CUDA device, so you should probably set cuda in params.py to True")
# 加载训练数据集
train_dataset = HubDataset(config, "train", transform=None)
train_kwargs = {'num_workers': 2, 'pin_memory': True,
'collate_fn': alignCollate(config.img_height, config.img_width, config.keep_ratio)} if torch.cuda.is_available() else {}
training_data_batch = DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True, drop_last=False, **train_kwargs)
# 加载定长校验数据集
eval_dataset = HubDataset(config, "eval", transform=transforms.Compose([ResizeNormalize(config.img_height, config.img_width)]))
eval_kwargs = {'num_workers': 2, 'pin_memory': False} if torch.cuda.is_available() else {}
eval_data_batch = DataLoader(eval_dataset, batch_size=config.eval_batch_size, shuffle=False, drop_last=False, **eval_kwargs)
# 加载不定长校验数据集
# eval_dataset = HubDataset(config, "eval")
# eval_kwargs = {'num_workers': 2, 'pin_memory': False,
# 'collate_fn': alignCollate(config.img_height, config.img_width, config.keep_ratio)} if torch.cuda.is_available() else {}
# eval_data_batch = DataLoader(eval_dataset, batch_size=config.eval_batch_size, shuffle=False, drop_last=False, **eval_kwargs)
# 定义网络模型
nclass = len(config.label_classes) + 1
crnn = CRNN(config.img_height, config.nc, nclass, config.hidden_size, n_rnn=config.n_layers)
# 加载预训练模型
if config.pretrained != '':
print('loading pretrained model from %s' % config.pretrained)
crnn.load_state_dict(torch.load(config.pretrained))
print(crnn)
# Compute average for `torch.Variable` and `torch.Tensor`.
loss_avg = utils.averager()
# Convert between str and label.
converter = utils.strLabelConverter(config.label_classes)
criterion = CTCLoss() # 定义损失函数
# 设置占位符
image = torch.FloatTensor(config.train_batch_size, 3, config.img_height, config.img_height)
text = torch.LongTensor(config.train_batch_size * 5)
length = torch.LongTensor(config.train_batch_size)
if config.use_cuda and torch.cuda.is_available():
criterion = criterion.cuda()
image = image.cuda()
crnn = crnn.to(config.device)
image = Variable(image)
text = Variable(text)
length = Variable(length)
# 设定优化器
if config.adam:
optimizer = optim.Adam(crnn.parameters(), lr=config.lr, betas=(config.beta1, 0.999))
elif config.adadelta:
optimizer = optim.Adadelta(crnn.parameters())
else:
optimizer = optim.RMSprop(crnn.parameters(), lr=config.lr)
def val(net, criterion, eval_data_batch):
print('Start val')
for p in crnn.parameters():
p.requires_grad = False
net.eval()
n_correct = 0
loss_avg_eval = utils.averager()
for data in eval_data_batch:
cpu_images, cpu_texts = data
batch_size = cpu_images.size(0)
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts)
utils.loadData(text, t)
utils.loadData(length, l)
preds = crnn(image)
preds_size = Variable(torch.LongTensor([preds.size(0)] * batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
loss_avg_eval.add(cost) # 计算loss
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
cpu_texts_decode = []
for i in cpu_texts:
cpu_texts_decode.append(i)
for pred, target in zip(sim_preds, cpu_texts_decode): # 计算准确率
if pred == target:
n_correct += 1
raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.n_val_disp]
for raw_pred, pred, gt in zip(raw_preds, sim_preds, cpu_texts_decode):
print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
accuracy = n_correct / float(len(eval_dataset))
print('Val loss: %f, accuray: %f' % (loss_avg.val(), accuracy))
# 训练每个batch数据
def train(net, criterion, optimizer, data):
cpu_images, cpu_texts = data
batch_size = cpu_images.size(0) # 计算当前batch_size大小
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts) # 转换为类别
utils.loadData(text, t)
utils.loadData(length, l)
optimizer.zero_grad() # 清零梯度
preds = net(image)
preds_size = Variable(torch.LongTensor([preds.size(0)] * batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
cost.backward()
optimizer.step()
return cost
for epoch in range(config.nepoch):
i = 0
for batch_data in training_data_batch:
for p in crnn.parameters():
p.requires_grad = True
crnn.train()
cost = train(crnn, criterion, optimizer, batch_data)
loss_avg.add(cost)
i += 1
if i % config.displayInterval == 0:
print('[%d/%d][%d/%d] Loss: %f' %
(epoch, config.nepoch, i, len(training_data_batch), loss_avg.val()))
loss_avg.reset()
# if i % config.valInterval == 0:
# val(crnn, criterion, eval_data_batch)
#
# # do checkpointing
# if i % config.saveInterval == 0:
# torch.save(crnn.state_dict(), '{0}/netCRNN_{1}_{2}.pth'.format(config.expr_dir, epoch, i))
val(crnn, criterion, eval_data_batch)
torch.save(crnn.state_dict(), '{0}/netCRNN_{1}_end.pth'.format(config.expr_dir, epoch))
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
nclass = 2
criterion = torch.nn.CrossEntropyLoss()
MODEL = MODEL()
image = torch.FloatTensor(args.batchSize, 1, args.imgH, args.imgW)
ori_label = torch.LongTensor(args.batchSize)
if args.cuda:
MODEL.cuda()
# MODEL = torch.nn.DataParallel(MODEL, device_ids=range(args.ngpu))
image = image.cuda()
ori_label = ori_label.cuda()
criterion = criterion.cuda()
# # loss averager
loss = utils.averager()
optimizer = torch.optim.Adadelta(filter(lambda p: p.requires_grad,
MODEL.parameters()),
lr=args.lr)
acc = 0
import os
path = args.output
if not os.path.exists(path):
os.system("mkdir " + path)
for epoch in range(args.niter):
length = len(train_loader)
train_iter = iter(train_loader)
