# 设置网络骨架为ResNet34
backbone = resnet_fpn_backbone('resnet34', True)
# 建立脊柱模型
spinal_model = SpinalModel(train_images,
train_annotation,
num_candidates=128,
num_selected_templates=8,
max_translation=0.05,
scale_range=(0.9, 1.1),
max_angel=10)
# 建立关键点模型
kp_model = KeyPointModelV2(backbone,
pixel_mean=0.5,
pixel_std=1,
loss=KeyPointBCELossV2(lamb=1),
spinal_model=spinal_model,
cascade_loss=CascadeLossV2(1),
loss_scaler=100,
num_cascades=3)
# 加载第二次训练好的模型
kp_model.load_state_dict(torch.load('../models/pretrained_34.kp_model_v2'))
# 建立疾病分类模型
dis_model = DiseaseModelV3(
kp_model,
sagittal_size=(512, 512),
loss_scaler=0.01,
use_kp_loss=True,
share_backbone=True,
transverse_size=(192, 192),
sagittal_shift=1,
'../data/lumbar_train150', '../data/lumbar_train150_annotation.json', multiprocessing=False)
valid_studies, valid_annotation, valid_counter = construct_studies(
'../data/lumbar_train51/', '../data/lumbar_train51_annotation.json', multiprocessing=False)
# 设定模型参数
train_images = {}
for study_uid, study in train_studies.items():
frame = study.t2_sagittal_middle_frame
train_images[(study_uid, frame.series_uid, frame.instance_uid)] = frame.image
backbone = resnet_fpn_backbone('resnet34', True)
spinal_model = SpinalModel(train_images, train_annotation,
num_candidates=128, num_selected_templates=8,
max_translation=0.05, scale_range=(0.9, 1.1), max_angel=10)
kp_model = KeyPointModelV2(backbone, pixel_mean=0.5, pixel_std=1,
loss=KeyPointBCELossV2(lamb=1), spinal_model=spinal_model,
cascade_loss=CascadeLossV2(1), loss_scaler=100, num_cascades=3)
kp_model.load_state_dict(torch.load('../models/pretrained_34.kp_model_v2'))
dis_model = DiseaseModelV3(
kp_model, sagittal_size=(512, 512), loss_scaler=0.01, use_kp_loss=True, share_backbone=True,
transverse_size=(192, 192), sagittal_shift=1, k_nearest=0,
# disc_loss=DisLoss(), vertebra_loss=DisLoss()
)
dis_model.cuda(0)
print(dis_model)
# 设定训练参数
train_dataloader = DisDataLoader(
train_studies, train_annotation, batch_size=8, num_workers=5, num_rep=20, prob_rotate=1, max_angel=180,
sagittal_size=dis_model.sagittal_size, transverse_size=dis_model.transverse_size, k_nearest=dis_model.k_nearest,