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import numpy as np |
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from light_training.dataloading.dataset import get_test_loader_from_test |
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import torch |
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import torch.nn as nn |
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from monai.networks.nets.basic_unet import BasicUNet |
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from monai.networks.nets.swin_unetr import SwinUNETR |
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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.utils.files_helper import save_new_model_and_delete_last |
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from models.uent3d import UNet3D |
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from monai.networks.nets.segresnet import SegResNet |
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from models.transbts.TransBTS_downsample8x_skipconnection import TransBTS |
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from einops import rearrange |
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from models.modelgenesis.unet3d import UNet3DModelGen |
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from models.transvw.models.ynet3d import UNet3DTransVW |
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from monai.networks.nets.basic_unet import BasicUNet |
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from monai.networks.nets.attentionunet import AttentionUnet |
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from light_training.loss.compound_losses import DC_and_CE_loss |
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from light_training.loss.dice import MemoryEfficientSoftDiceLoss |
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from light_training.evaluation.metric import dice |
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set_determinism(123) |
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from light_training.loss.compound_losses import DC_and_CE_loss |
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import os |
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from medpy import metric |
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from light_training.prediction import Predictor |
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data_dir = "./data/fullres/test" |
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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:2" |
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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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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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del batch |
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return image, label, properties |
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def define_model_diffunet(self): |
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from models.nnunet_denoise_ddp_infer.get_unet3d_denoise_uncer_edge import DiffUNet |
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model = DiffUNet(1, 10, 3, 1, bta=True) |
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model_path = "/home/xingzhaohu/zongweizhou/logs_gpu4/diffunet/model/final_model_0.8384.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, strict=False) |
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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=0.3, |
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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/diffunet_ep1000_test" |
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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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print(properties['spacing']) |
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ddim = True |
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model, predictor, save_path = self.define_model_diffunet() |
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if ddim: |
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model_output = predictor.maybe_mirror_and_predict(image, model, device=device, ddim=True) |
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else : |
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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).cpu() |
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model_output = model_output.argmax(dim=0) |
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model_output = predictor.predict_noncrop_probability(model_output, properties) |
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print(f"save shape is {model_output.shape}") |
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seg_list = ["aorta", "gall_bladder", "kidney_left", |
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"kidney_right", "liver", "pancreas", |
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"postcava", "spleen", "stomach"] |
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save_path = os.path.join(save_path, properties['name'][0], "predictions") |
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os.makedirs(save_path, exist_ok=True) |
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for i in range(1, len(seg_list) + 1): |
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model_output_c = model_output == i |
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predictor.save_to_nii(model_output_c, |
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raw_spacing=properties['spacing'], |
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case_name=seg_list[i-1], |
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save_dir=save_path) |
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return 0 |
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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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test_ds = get_test_loader_from_test(data_dir=data_dir) |
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trainer.validation_single_gpu(test_ds) |
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