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model_predict.py
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model_predict.py
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import sys
import pickle
from pathlib import Path
from dotenv import find_dotenv, load_dotenv
# find .env automagically by walking up directories until it's found, then
# load up the .env entries as environment variables
load_dotenv(find_dotenv())
from tqdm import tqdm
import h5py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torchvision import transforms, models
from torch.utils.data import DataLoader
from nnunet.training.model_restore import restore_model
from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_ResencUNet import nnUNetTrainerV2_ResNetUNet
# local imports
from src.data import ADNIDatasetClassification
from src.net import BraTSnnUNet, load_from_wandb
PROJ_ROOT = Path('/home/jupyter/gama/bruno')
DATASET_FPATH = PROJ_ROOT/'data/interim/ADNI123_slices_fix_2mm_split_class.hdf5'
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# PARSE ARGS
args = sys.argv
model, run_id, split = args[-3:]
model = model.lower()
split = split.lower()
assert model in ['unet', 'resnet']
assert split in ['val', 'test']
# LOAD DATA
group_labels = ['CN', 'MCI', 'AD']
dataset = ADNIDatasetClassification(
DATASET_FPATH,
get_age=True,
dataset=split,
labels=group_labels,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
dataloader = DataLoader(dataset, batch_size=40, shuffle=False)
h = lambda x: x*25+75
# LOAD MODEL
if split == 'val':
wandb_model_file = 'model_best'
elif split == 'test':
wandb_model_file = 'model_last'
nnunet_trainer_kwargs = dict(
dataset_directory='/home/jupyter/gama/nnUNet/data/processed/Task102_BraTS2020',
batch_dice=True,
stage=0,
unpack_data=True,
deterministic=False,
fp16=True,
)
if model == 'unet':
nnunet_trainer = nnUNetTrainerV2(
'/home/jupyter/gama/nnUNet/data/processed/Task102_BraTS2020/nnUNetPlansv2.1_plans_2D.pkl',
0,
output_folder='/home/jupyter/gama/nnUNet/models/nnUNet/2d/Task102_BraTS2020/nnUNetTrainerV2__nnUNetPlansv2.1',
**nnunet_trainer_kwargs,
)
nnunet_trainer.initialize(False)
net = BraTSnnUNet(nnunet_trainer.network)
net.pooling = nn.AvgPool2d(3)
net = load_from_wandb(net, run_id, model_fname=wandb_model_file).to(device)
elif model == 'resnet':
nnunet_trainer = nnUNetTrainerV2_ResNetUNet(
'/home/jupyter/gama/nnUNet/data/processed/Task102_BraTS2020/nnUNetPlans_ResNetUNet_v2.1_plans_2D.pkl',
0,
output_folder='/home/jupyter/gama/nnUNet/models/nnUNet/2d/Task102_BraTS2020/nnUNetTrainerV2_ResNetUNet__nnUNetPlans_ResNetUNet_v2.1',
**nnunet_trainer_kwargs,
)
nnunet_trainer.initialize(False)
resnet_backbone = nnunet_trainer.network.encoder
resnet_backbone.default_return_skips = False
net = nn.Sequential(
resnet_backbone,
nn.AdaptiveAvgPool2d(output_size=(1,1)),
nn.Flatten(1),
nn.Linear(2048, 1),
)
net = load_from_wandb(net, run_id, model_fname=wandb_model_file).to(device)
net.eval()
# MAKE PREDICTIONS
age_hats = list()
ages = list()
groups = list()
for X, a, y in tqdm(dataloader):
group = group_labels[y.median().item()]
X = X.repeat((1,4,1,1)).to(device) # fix input channels
with torch.no_grad():
a_hat = h(net(X))
age_hats.append(a_hat.mean().item())
ages.append(a.median().item())
groups.append(group)
# SAVE PREDICTIONS
with open(f'/home/jupyter/gama/bruno/data/preds/predictions_{model}_{run_id}_{split}.pkl', 'wb') as f:
pickle.dump({
'age_hats': age_hats,
'ages': ages,
'groups': groups,
}, f)