def build_dataloader(cfg, is_train=True):
type_name = cfg.DATASETS.TYPE
model_type = cfg.BASE.TYPE
# 考虑不需区分dataset是否是检测或识别,只需填入需要的地址
if is_train == True:
'''Use a letter to encode the type of the model'''
if model_type == 'R':
alphabet = Alphabet(cfg.ADDRESS.ALPHABET)
else:
alphabet = Alphabet()
train_data_dir = cfg.ADDRESS.TRAIN_DATA_DIR
train_anno_dir = cfg.ADDRESS.TRAIN_GT_DIR
val_data_dir = cfg.ADDRESS.VAL_DATA_DIR
val_anno_dir = cfg.ADDRESS.VAL_GT_DIR
train_set = get_dataset(cfg, type_name, train_data_dir, train_anno_dir, split='train', alphabet=alphabet)
val_set = get_dataset(cfg, type_name, val_data_dir, val_anno_dir, split='val', alphabet=alphabet)
train_dataloader = get_dataloader(cfg, type_name, dataset=train_set, split='train')
val_dataloader = get_dataloader(cfg, type_name, dataset=val_set, split='val')
# remind the relation of batch_size and num of gpus
images_per_batch = cfg.MODEL.BATCH_SIZE
num_gpus = int(cfg.BASE.NUM_GPUS)
assert (
images_per_batch % num_gpus == 0
), "IMS_PER_BATCH ({}) must be divisible by the number "
"of GPUs ({}) used.".format(images_per_batch, num_gpus)
images_per_gpu = images_per_batch // num_gpus
if images_per_gpu > 5:
logger = logging.getLogger(__name__)
# logger.warning(
# "每GPU图片数量过高时可能遇到内存溢出，"
# "若发生该情况请调整BATCH_SIZE,并调整学习率等其他可能影响效果的因素"
# )
return train_dataloader, val_dataloader
else:
if model_type == 'R':
alphabet = Alphabet(cfg.ADDRESS.ALPHABET)
else:
alphabet = Alphabet()
test_data_dir = cfg.ADDRESS.TEST_DATA_DIR
test_anno_dir = cfg.ADDRESS.TEST_GT_DIR
test_set = get_dataset(cfg, name, test_data_dir, test_anno_dir, split='test', alphabet=alphabet)
test_dataloader = get_dataloader(cfg, name, dataset=test_set, split='test')
return test_dataloader
def __init__(self, opt):
nn.Module.__init__(self)
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.opt = opt
# self.stn = SpatialTransformer(self.opt)
self.cnn = self.getCNN_cap()
self.rnn = self.getEncoder()
# n_class,hidden_size,num_embedding,input_size
# self.attention = Attention(self.n_class,256, 128,256)
self.attention = Attention(256, 256, self.n_class, 128)
# Spatial transformer localization-network
self.localization = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=7),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True),
nn.Conv2d(8, 10, kernel_size=5),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True)
)
# Regressor for the 3 * 2 affine matrix
self.fc_loc = nn.Sequential(
nn.Linear(10 * 4 * 21, 32),
nn.ReLU(True),
nn.Linear(32, 3 * 2)
)
# Initialize the weights/bias with identity transformation
self.fc_loc[2].weight.data.fill_(0)
self.fc_loc[2].bias.data = torch.FloatTensor([1, 0, 0, 0, 1, 0])
def __init__(self, opt):
nn.Module.__init__(self)
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.blstm = BLSTM(512, 256)
self.attention = Attention(input_size=512, hidden_size=256, num_classes=self.n_class, num_embeddings=128)
def __init__(self, opt):
super(CRNN, self).__init__()
self.opt = opt
'''alphabet'''
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET)) + 1
'''cnn'''
self.cnn = self.getCNN()
'''rnn'''
self.rnn = self.getRNN()
def __init__(self, opt=None):
nn.Module.__init__(self)
self.opt = opt
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.cnn = ResNet(num_in=opt.IMAGE.IMG_CHANNEL, block=BasicBlock, layers=[1, 2, 5, 3]) # (BS,6,40)
self.encoder = Encoder() # (40,BS,512)
self.decoder = Attention(opt=opt)
def __init__(self, opt):
nn.Module.__init__(self)
self.opt = opt
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.cnn = CapsNet(E=10) # (BS,6,40)
self.encoder = Encoder() # (40,BS,512)
self.decoder = Attention(opt)
self.fc = nn.Linear(272, 512, bias=True)
self.relu = nn.ReLU()
def __init__(self, opt):
super(Attention, self).__init__()
self.attention_cell = AttentionCell()
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.generator = nn.Linear(512, self.n_class)
self.char_embeddings = Parameter(torch.randn(self.n_class + 1, 128))
'''给conv_feats用的'''
self.conv = nn.Conv2d(512, 512, 3, 1, 1)
self.bn = nn.BatchNorm2d(512)
self.relu = nn.ReLU()
def __init__(self, opt):
nn.Module.__init__(self)
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.opt = opt
self.stn = SpatialTransformer(self.opt)
self.cnn = self.getCNN()
self.rnn = self.getEncoder()
# n_class,hidden_size,num_embedding,input_size
# self.attention = Attention(self.n_class,256, 128,256)
self.attention = Attention(256, 256, self.n_class, 128)
def loadTool(self):
'''
根据模型的类型，加载相应的组件
'''
if self.opt.BASE.TYPE == 'R':
self.alphabet = Alphabet(self.opt.ADDRESS.ALPHABET)
if self.opt.BASE.MODEL == 'GRCNN' or self.opt.BASE.MODEL == 'CRNN' or self.opt.BASE.MODEL == 'CAPSOCR2':
from utils.strLabelConverterForCTC import strLabelConverterForCTC
self.converter = strLabelConverterForCTC(self.alphabet.str)
else:
from utils.strLabelConverterForAttention import strLabelConverterForAttention
self.converter = strLabelConverterForAttention(self.alphabet.str)
self.highestAcc = 0
self.val_times = 0
def __init__(self, opt):
nn.Module.__init__(self)
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.opt = opt
# self.stn = SpatialTransformer(self.opt)
self.cnn = self.getCNN_sr()
self.rnn = self.getEncoder()
# n_class,hidden_size,num_embedding,input_size
# self.attention = Attention(self.n_class,256, 128,256)
self.attention = Attention(256, 256, self.n_class, 128)
self.conv_layers = nn.ModuleList()
self.norm_layers = nn.ModuleList()
# ========= ConvCaps Layers
for d in range(1, 2):
'''自回归模型'''
self.conv_layers.append(
SelfRouting2d(num_caps,
num_caps,
caps_size,
caps_size,
kernel_size=3,
stride=1,
padding=1,
pose_out=True))
'''bn'''
self.norm_layers.append(nn.BatchNorm2d(caps_size * num_caps))
'''恒等输出'''
self.conv_a = nn.Conv2d(8 * planes,
num_caps,
kernel_size=3,
stride=1,
padding=1,
bias=False)
'''姿态变量'''
self.conv_pose = nn.Conv2d(8 * planes,
num_caps * caps_size,
kernel_size=3,
stride=1,
padding=1,
bias=False)
'''两个bn'''
self.bn_a = nn.BatchNorm2d(num_caps)
self.bn_pose = nn.BatchNorm2d(num_caps * caps_size)
def predict(self, texts, embedding_alphabet: AlphabetEmbeddings,
label_alphabet: Alphabet, batch_size):
lens = len(texts)
batch_num = (lens + batch_size - 1) // batch_size
ans = []
for i in range(batch_num):
start = i * batch_size
end = min(start + batch_size, lens)
part = texts[start:end]
part, lengths, mask = embedding_alphabet.add_padding_tensor(
part, gpu=self.gpu)
pred = self.forward(part, lengths, mask)
pred = torch.argmax(pred, dim=-1, keepdim=False)
pred = pred.tolist()
pred = label_alphabet.get_instance(pred)
ans.extend(pred)
return ans
def __init__(self, opt):
nn.Module.__init__(self)
self.opt = opt
from alphabet.alphabet import Alphabet
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET))
self.fe = Feature_Extractor(strides=[(1, 1), (2, 2), (1, 1), (2, 2),
(1, 1), (1, 1)],
compress_layer=False,
input_shape=[1, 32, 128])
scales = self.fe.Iwantshapes()
self.cam = CAM(scales=scales, maxT=25, depth=8, num_channels=64)
self.dtd = DTD(
nclass=self.n_class,
nchannel=512,
dropout=0.3,
)
def __init__(self, opt):
# self.alphabet = Alphabet(self.opt.ADDRESS.RECOGNITION.ALPHABET)
from alphabet.alphabet import Alphabet
# self.nclass = len(alphabet)
self.n_class = len(Alphabet(opt.ADDRESS.ALPHABET)) + 1
# self.n_class = len(alphabet)
self.crann_config = json.loads(json.dumps(opt))
n_class = self.n_class
crann_config = self.crann_config
print('crann_config"s value is ', crann_config)
print(type(crann_config))
super(newCRANN, self).__init__()
self.ngpu = crann_config['BASE']['NUM_GPUS']
cnn_conf = crann_config['CNN']
print('Constructing {}'.format(cnn_conf['MODEL']))
self.cnn = ConvNets.__dict__[cnn_conf['MODEL']]()
rnn_conf = crann_config['RNN']
print('Constructing {}'.format(rnn_conf['MODEL']))
self.rnn = SeqNets.__dict__[rnn_conf['MODEL']](rnn_conf, n_class)
def __init__(self, opt):
from alphabet.alphabet import Alphabet
# self.nclass = len(alphabet)
self.nclass = len(Alphabet(opt.ADDRESS.ALPHABET))
self.nh = opt.nh
self.targetH = opt.targetH
self.targetW = opt.targetW
self.BidirDecoder = opt.BidirDecoder
self.inputDataType = opt.inputDataType
self.maxBatch = opt.maxBatch
self.CUDA = opt.CUDA
self.nc = opt.IMAGE.IMG_CHANNEL
# def __init__(self, nc, nclass, nh, targetH, targetW, BidirDecoder=False,
# inputDataType='torch.cuda.FloatTensor', maxBatch=256, CUDA=True):
'''
初始化MORAN模型，由MORN和ASRN两部分构成
:param int nc 图片通道数
:param int nclass 字符表中的字符数量
:param int nh 图片的高
:param int targetH 经过MORN调整后图片的目标高度
:param int targetW 经过MORN调整后图片的目标宽度
:param bool bidirDeccoder 是否使用双向LSTM
:param str inputDataType 数据类型
:param int maxBatch Batch的最大数量
:param bool CUDA 是否使用CUDA
'''
super(newMORAN, self).__init__()
self.MORN = MORN(self.nc, self.targetH, self.targetW,
self.inputDataType, self.maxBatch, self.CUDA)
self.ASRN = ASRN(self.targetH, self.nc, self.nclass, self.nh,
self.BidirDecoder, self.CUDA)
# Fix the random seed of Pytorch when using GPU.
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.random_state)
torch.cuda.manual_seed(args.random_state)
# Fix the random seed of Pytorch when using CPU.
torch.manual_seed(args.random_state)
torch.random.manual_seed(args.random_state)
# get dataset and alphabets
dataset = DataIOSST2(config['data'])
if config['use_pre_embedding']:
seq_alphabet = AlphabetEmbeddings(**config['embedding'])
seq_alphabet.load_embeddings_from_file()
else:
seq_alphabet = AlphabetEmbeddings(**config['embedding'])
seq_alphabet.add_instance(dataset.train_word)
label_alphabet = Alphabet('label', False, False)
label_alphabet.add_instance(dataset.train_label)
# get model
if args.load is not None:
model = torch.load(args.load)
else:
model = ModelFactory.get_model(config, args, seq_alphabet,
label_alphabet)
process = Process(config, args, dataset, model, seq_alphabet,
label_alphabet)
process.train()
