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crnn_main_v2.py
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#coding:utf-8
"""
fzh created on 2019/10/15
crnn模型训练程序
"""
from __future__ import print_function
from torch.utils.data import DataLoader
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
# from warpctc_pytorch import CTCLoss
import os
import utils
# import dataset
import crnn as crnn
import re
import params
from dataset_v2 import baiduDataset
# def init_args():
# args = argparse.ArgumentParser()
# args.add_argument('--trainroot', help='path to dataset', default='./to_lmdb/train')
# args.add_argument('--valroot', help='path to dataset', default='./to_lmdb/train')
# args.add_argument('--cuda', action='store_true', help='enables cuda', default=False)
# return args.parse_args()
# custom weights initialization called on crnn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def val(net, val_loader, criterion, epoch, max_i=1000):
print('================Start val=================')
for p in crnn.parameters():
p.requires_grad = False
net.eval()
i = 0
n_correct = 0
n_all = 0
loss_avg = utils.averager()
for i_batch, (image, index) in enumerate(val_loader):
image = image.to(device)
print('image.shape:',image.shape)
label = utils.get_batch_label(val_dataset, index)
# [41,batch,nclass]
preds = crnn(image)
batch_size = image.size(0)
# index = np.array(index.data.numpy())
label_text, label_length = converter.encode(label)
preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))
cost = criterion(preds, label_text, preds_size, label_length) / batch_size
loss_avg.add(cost)
# [41,batch]
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
# preds = preds.transpose(1, 0).reshape(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
print('label:',label[:2])
print('sim_preds:',sim_preds[:2])
# print(list(zip(sim_preds, label)))
n_all += len(label)
for pred, target in list(zip(sim_preds, label)):
if pred == target:
n_correct += 1
if (i_batch+1)%params.displayInterval == 0:
print('[%d/%d][%d/%d]' %
(epoch, params.epochs, i_batch, len(val_loader)))
if i_batch == max_i:
break
raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:params.n_test_disp]
for raw_pred, pred, gt in zip(raw_preds, sim_preds, label):
print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
#
# print('n_correct:',n_correct)
# accuracy = n_correct / float(max_i * params.val_batchSize)
accuracy = n_correct / n_all
print('Test loss: %f, accuray: %f' % (loss_avg.val(), accuracy))
return accuracy
def train(crnn, train_loader, criterion, epoch):
for p in crnn.parameters():
p.requires_grad = True
crnn.train()
#loss averager
loss_avg = utils.averager()
for i_batch, (image, index) in enumerate(train_loader):
#[b,c,h,w] [32,1,32,160]
image = image.to(device)
print('image.shape:',image.shape)
batch_size = image.size(0)
#['xxx','xxxx',...batch]
label = utils.get_batch_label(dataset, index)
#[41,batch,nclass]
preds = crnn(image)
# print('preds.shape',preds.shape)
# index = np.array(index.data.numpy())
#[, , ,] [len(lable[0]),len(lable[1]),...]
label_text, label_length = converter.encode(label)
# print('label_text:', len(label_text))
# print('label_length:', label_length)
#[41,41,41,...]*batch
preds_size = torch.IntTensor([preds.size(0)] * batch_size)
# print('preds.shape, label_text.shape, preds_size.shape, label_length.shape',preds.shape, label_text.shape, preds_size.shape, label_length.shape)
# torch.Size([41, 32, 6736]) torch.Size([320]) torch.Size([320]) torch.Size([320])
cost = criterion(preds, label_text, preds_size, label_length) / batch_size
# print('cost:',cost)
crnn.zero_grad()
cost.backward()
optimizer.step()
loss_avg.add(cost)
if (i_batch+1) % params.displayInterval == 0:
print('[%d/%d][%d/%d] Loss: %f' %
(epoch, params.epochs, i_batch, len(train_loader), loss_avg.val()))
loss_avg.reset()
def main(crnn, train_loader, val_loader, criterion, optimizer):
crnn = crnn.to(device)
criterion = criterion.to(device)
for i,epoch in enumerate(range(params.epochs)):
# if i<1:
train(crnn, train_loader, criterion, epoch)
# # ## max_i: cut down the consuming time of testing, if you'd like to validate on the whole testset, please set it to len(val_loader)
accuracy = val(crnn, val_loader, criterion, epoch, max_i=1000)
for p in crnn.parameters():
p.requires_grad = True
# if accuracy > params.best_accuracy:
torch.save(crnn.state_dict(), '{0}/crnn_Rec_done_{1}_{2}.pth'.format(params.experiment, epoch, accuracy))
torch.save(crnn.state_dict(), '{0}/crnn_best.pth'.format(params.experiment))
print("is best accuracy: {0}".format(accuracy > params.best_accuracy))
def backward_hook(self, grad_input, grad_output):
for g in grad_input:
g[g != g] = 0 # replace all nan/inf in gradients to zero
if __name__ == '__main__':
# args = init_args()
# manualSeed = random.randint(1, 10000) #fix seed
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
manualSeed = 10
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
cudnn.benchmark = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# store model path
if not os.path.exists('./expr'):
os.mkdir('./expr')
# read train set
dataset = baiduDataset("./data_chinese_tra/images_add_fake", "./data_chinese_tra/label_add_fake/train_add_fake.txt",
params.alphabet, False, (params.imgW, params.imgH))
val_dataset = baiduDataset("./data_chinese_tra/images_add_fake", "./data_chinese_tra/label_add_fake/val_add_fake.txt",
params.alphabet, False, (params.imgW, params.imgH))
train_loader = DataLoader(dataset, batch_size=params.batchSize, shuffle=True, num_workers=params.workers)
# shuffle=True, just for time consuming.
val_loader = DataLoader(val_dataset, batch_size=params.val_batchSize, shuffle=True, num_workers=params.workers)
converter = utils.strLabelConverter(dataset.alphabet)
nclass = len(params.alphabet) + 1
print('nclass:',nclass)
nc = 1
criterion = torch.nn.CTCLoss(reduction='sum')
# criterion = CTCLoss()
# cnn and rnn
crnn = crnn.CRNN(32, nc, nclass, params.nh)
crnn.apply(weights_init)
if params.crnn != '':
print('loading pretrained model from %s' % params.crnn)
crnn.load_state_dict(torch.load(params.crnn))
# setup optimizer
if params.adam:
optimizer = optim.Adam(crnn.parameters(), lr=params.lr,
betas=(params.beta1, 0.999))
elif params.adadelta:
optimizer = optim.Adadelta(crnn.parameters(), lr=params.lr)
else:
optimizer = optim.RMSprop(crnn.parameters(), lr=params.lr)
crnn.register_backward_hook(backward_hook)
main(crnn, train_loader, val_loader, criterion, optimizer)