| import numpy as np |
| from light_training.dataloading.dataset import get_train_val_test_loader_from_train |
| import torch |
| import torch.nn as nn |
| from monai.inferers import SlidingWindowInferer |
| from light_training.evaluation.metric import dice |
| from light_training.trainer import Trainer |
| from monai.utils import set_determinism |
| from light_training.evaluation.metric import dice |
| set_determinism(123) |
| import os |
| from light_training.prediction import Predictor |
|
|
| data_dir = "./data/fullres/train" |
| env = "pytorch" |
| max_epoch = 1000 |
| batch_size = 2 |
| val_every = 2 |
| num_gpus = 1 |
| device = "cuda:0" |
| patch_size = [128, 128, 128] |
|
|
| class BraTSTrainer(Trainer): |
| def __init__(self, env_type, max_epochs, batch_size, device="cpu", val_every=1, num_gpus=1, logdir="./logs/", master_ip='localhost', master_port=17750, training_script="train.py"): |
| super().__init__(env_type, max_epochs, batch_size, device, val_every, num_gpus, logdir, master_ip, master_port, training_script) |
| |
| self.patch_size = patch_size |
| self.augmentation = False |
| |
| def convert_labels(self, labels): |
| |
| result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3] |
| |
| return torch.cat(result, dim=1).float() |
|
|
| def get_input(self, batch): |
| image = batch["data"] |
| label = batch["seg"] |
| properties = batch["properties"] |
| label = self.convert_labels(label) |
|
|
| return image, label, properties |
|
|
| def define_model_segmamba(self): |
| from model_segmamba.segmamba import SegMamba |
| model = SegMamba(in_chans=4, |
| out_chans=4, |
| depths=[2,2,2,2], |
| feat_size=[48, 96, 192, 384]) |
| |
| model_path = "/home/xingzhaohu/dev/jiuding_code/brats23/logs/segmamba/model/final_model_0.9038.pt" |
| new_sd = self.filte_state_dict(torch.load(model_path, map_location="cpu")) |
| model.load_state_dict(new_sd) |
| model.eval() |
| window_infer = SlidingWindowInferer(roi_size=patch_size, |
| sw_batch_size=2, |
| overlap=0.5, |
| progress=True, |
| mode="gaussian") |
|
|
| predictor = Predictor(window_infer=window_infer, |
| mirror_axes=[0,1,2]) |
|
|
| save_path = "./prediction_results/segmamba" |
| os.makedirs(save_path, exist_ok=True) |
|
|
| return model, predictor, save_path |
| |
| def validation_step(self, batch): |
| image, label, properties = self.get_input(batch) |
| ddim = False |
| |
| model, predictor, save_path = self.define_model_segmamba() |
|
|
| model_output = predictor.maybe_mirror_and_predict(image, model, device=device) |
|
|
| model_output = predictor.predict_raw_probability(model_output, |
| properties=properties) |
| |
|
|
| model_output = model_output.argmax(dim=0)[None] |
| model_output = self.convert_labels_dim0(model_output) |
|
|
| label = label[0] |
| c = 3 |
| dices = [] |
| for i in range(0, c): |
| output_i = model_output[i].cpu().numpy() |
| label_i = label[i].cpu().numpy() |
| d = dice(output_i, label_i) |
| dices.append(d) |
|
|
| print(dices) |
|
|
| model_output = predictor.predict_noncrop_probability(model_output, properties) |
| predictor.save_to_nii(model_output, |
| raw_spacing=[1,1,1], |
| case_name = properties['name'][0], |
| save_dir=save_path) |
| |
| return 0 |
|
|
| def convert_labels_dim0(self, labels): |
| |
| result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3] |
| |
| return torch.cat(result, dim=0).float() |
| |
|
|
| def filte_state_dict(self, sd): |
| if "module" in sd : |
| sd = sd["module"] |
| new_sd = {} |
| for k, v in sd.items(): |
| k = str(k) |
| new_k = k[7:] if k.startswith("module") else k |
| new_sd[new_k] = v |
| del sd |
| return new_sd |
| |
| if __name__ == "__main__": |
|
|
| trainer = BraTSTrainer(env_type=env, |
| max_epochs=max_epoch, |
| batch_size=batch_size, |
| device=device, |
| logdir="", |
| val_every=val_every, |
| num_gpus=num_gpus, |
| master_port=17751, |
| training_script=__file__) |
| |
| train_ds, val_ds, test_ds = get_train_val_test_loader_from_train(data_dir) |
|
|
| trainer.validation_single_gpu(test_ds) |
|
|
| |
|
|
|
|
|
|
