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jblitzar
/
github-python-corpus

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from model import Model
from test import validation
from modules.rec_sar_loss import SARLoss
from modules.dctc_loss import DCTC
from optimizer import CosineAnnealingLR
os.environ["CUDA_VISIBLE_DEVICES"] = "1" # 可修改
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def train(opt):
""" dataset preparation """
if not opt.data_filtering_off:
print('Filtering the images containing characters which are not in opt.character')
print('Filtering the images whose label is longer than opt.batch_max_length')
# see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
train_dataset = Batch_Balanced_Dataset(opt)
log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a')
AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=opt.batch_size,
shuffle=True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True)
log.write(valid_dataset_log)
print('-' * 80)
log.write('-' * 80 + '\n')
log.close()
""" model configuration """
if 'CTC' in opt.Prediction:
if opt.baiduCTC:
converter = CTCLabelConverterForBaiduWarpctc(opt.character)
else:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
# weight initialization
# for name, param in model.named_parameters():
# if 'localization_fc2' in name:
# print(f'Skip {name} as it is already initialized')
# continue
# try:
# if 'bias' in name:
# init.constant_(param, 0.0)
# elif 'weight' in name:
# init.kaiming_normal_(param)
# except Exception as e: # for batchnorm.
# if 'weight' in name:
# param.data.fill_(1)
# continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).to(device)
model.train()
if opt.saved_model != '':
print(f'loading pretrained model from {opt.saved_model}')
if opt.FT:
model.load_state_dict(torch.load(opt.saved_model), strict=False)
else:
model.load_state_dict(torch.load(opt.saved_model))
# print("Model:") # 打印模型结构
# print(model)
""" setup loss """
if 'CTC' == opt.Prediction:
if opt.baiduCTC:
# need to install warpctc. see our guideline.
from warpctc_pytorch import CTCLoss
criterion = CTCLoss()
else:
criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
elif 'DCTC' in opt.Prediction:
criterion = DCTC(use_il=False, alpha=0.01)
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)