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