def train(**kwargs):
opt = Config()
for k, v in kwargs.items():
setattr(opt, k, v)
device=t.device('cuda') if opt.use_gpu else t.device('cpu')
opt.caption_data_path = 'caption.pth' # 原始数据
opt.test_img = '' # 输入图片
# opt.model_ckpt='caption_0914_1947' # 预训练的模型
# 数据
vis = Visualizer(env=opt.env)
dataloader = get_dataloader(opt)
_data = dataloader.dataset._data
word2ix, ix2word = _data['word2ix'], _data['ix2word']
# 模型
model = CaptionModel(opt, word2ix, ix2word)
if opt.model_ckpt:
model.load(opt.model_ckpt)
optimizer = model.get_optimizer(opt.lr)
criterion = t.nn.CrossEntropyLoss()
model.to(device)
# 统计
loss_meter = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset()
for ii, (imgs, (captions, lengths), indexes) in tqdm.tqdm(enumerate(dataloader)):
# 训练
optimizer.zero_grad()
imgs = imgs.to(device)
captions = captions.to(device)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
score, _ = model(imgs, input_captions, lengths)
loss = criterion(score, target_captions)
loss.backward()
optimizer.step()
loss_meter.add(loss.item())
# 可视化
if (ii + 1) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
# 可视化原始图片 + 可视化人工的描述语句
raw_img = _data['ix2id'][indexes[0]]
img_path = opt.img_path + raw_img
raw_img = Image.open(img_path).convert('RGB')
raw_img = tv.transforms.ToTensor()(raw_img)
raw_caption = captions.data[:, 0]
raw_caption = ''.join([_data['ix2word'][ii] for ii in raw_caption])
vis.text(raw_caption, u'raw_caption')
vis.img('raw', raw_img, caption=raw_caption)
# 可视化网络生成的描述语句
results = model.generate(imgs.data[0])
vis.text('</br>'.join(results), u'caption')
model.save()
def train(**kwargs):
opt = Config()
for k, v in kwargs.items():
setattr(opt, k, v)
vis = Visualizer(env=opt.env)
dataloader = get_dataloader(opt)
_data = dataloader.dataset._data
word2ix, ix2word = _data['word2ix'], _data['ix2word']
# cnn = tv.models.resnet50(True)
model = CaptionModel(opt, None, word2ix, ix2word)
if opt.model_ckpt:
model.load(opt.model_ckpt)
optimizer = model.get_optimizer(opt.lr1)
criterion = t.nn.CrossEntropyLoss()
model.cuda()
criterion.cuda()
loss_meter = meter.AverageValueMeter()
perplexity = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset()
perplexity.reset()
for ii, (imgs, (captions, lengths),
indexes) in tqdm.tqdm(enumerate(dataloader)):
optimizer.zero_grad()
input_captions = captions[:-1]
imgs = imgs.cuda()
captions = captions.cuda()
imgs = Variable(imgs)
captions = Variable(captions)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
score, _ = model(imgs, input_captions, lengths)
loss = criterion(score, target_captions)
loss.backward()
# clip_grad_norm(model.rnn.parameters(),opt.grad_clip)
optimizer.step()
loss_meter.add(loss.data[0])
perplexity.add(t.exp(loss.data)[0])
# 可视化
if (ii + 1) % opt.plot_every == 0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss', loss_meter.value()[0])
vis.plot('perplexity', perplexity.value()[0])
# 可视化原始图片
raw_img = _data['train']['ix2id'][indexes[0]]
img_path = '/data/image/ai_cha/caption/ai_challenger_caption_train_20170902/caption_train_images_20170902/' + raw_img
raw_img = Image.open(img_path).convert('RGB')
raw_img = tv.transforms.ToTensor()(raw_img)
vis.img('raw', raw_img)
# raw_img = (imgs.data[0]*0.25+0.45).clamp(max=1,min=0)
# vis.img('raw',raw_img)
# 可视化人工的描述语句
raw_caption = captions.data[:, 0]
raw_caption = ''.join(
[_data['ix2word'][ii] for ii in raw_caption])
vis.text(raw_caption, u'raw_caption')
# 可视化网络生成的描述语句
results = model.generate(imgs.data[0])
vis.text('</br>'.join(results), u'caption')
if (epoch + 1) % 100 == 0:
model.save()
def train(**kwargs):
opt = Config()
opt.caption_data_path = 'caption.pth' # 原始数据
opt.test_img = '' # 输入图片
#opt.model_ckpt='caption_0914_1947' # 预训练的模型
# 数据w
vis = Visualizer(env=opt.env)
dataloader = get_dataloader(opt)
_data = dataloader.dataset._data
word2ix, ix2word = _data['word2ix'], _data['ix2word']
# 模型
model = CaptionModel(opt, word2ix, ix2word)
if opt.model_ckpt:
model.load(opt.model_ckpt)
optimizer = model.get_optimizer(opt.lr)
criterion = t.nn.CrossEntropyLoss()
if opt.use_gpu:
model.cuda()
criterion.cuda()
# 统计
loss_meter = meter.AverageValueMeter()
for epoch in range(opt.epoch):
loss_meter.reset()
for ii, (imgs, (captions, lengths),
indexes) in tqdm.tqdm(enumerate(dataloader)):
# 训练
optimizer.zero_grad()
if opt.use_gpu:
imgs = imgs.cuda()
captions = captions.cuda()
imgs = Variable(imgs)
captions = Variable(captions)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
score, _ = model(imgs, input_captions, lengths)
loss = criterion(score, target_captions)
loss.backward()
optimizer.step()
loss_meter.add(loss.data[0])
'''
if (ii+1)%opt.plot_every ==0:
if os.path.exists(opt.debug_file):
ipdb.set_trace()
vis.plot('loss',loss_meter.value()[0])
# 可视化原始图片 + 可视化人工的描述语句
raw_img = _data['ix2id'][indexes[0]]
img_path=opt.img_path+raw_img
raw_img = Image.open(img_path).convert('RGB')
raw_img = tv.transforms.ToTensor()(raw_img)
raw_caption = captions.data[:,0]
raw_caption = ''.join([_data['ix2word'][int(ii)] for ii in raw_caption])
vis.text(raw_caption,u'raw_caption')
vis.img('raw',raw_img,caption=raw_caption)
# 可视化网络生成的描述语句
results = model.generate(imgs.data[0])
vis.text('</br>'.join(results),u'caption')
'''
model.save()