meghbhalerao

import SimpleITK as sitk

# read image
inputImage = sitk.ReadImage('/path/to/input.nii.gz')

# get result in the form of a numpy array
npa_res = my_algorithm(sitk.GetArrayFromImage(inputImage)) # my_algorithm does something fancy

# Converting back to SimpleITK (assumes we didn't move the image in space as we copy the information from the original)
result_image = sitk.GetImageFromArray(npa_res)

meghbhalerao

# Choose the segmentation model here 
# options: unet, resunet, fcn
model:
  {
    architecture: resunet,
    final_layer: softmax
  }
# Set base filters: number of filters present in the initial module of the U-Net convolution; for IncU-Net, keep this divisible by 4
base_filters: 30
# Set the list of labels the model should train on and predict

meghbhalerao

import os
import numpy as np
folds = [5,5]
exp_path = "./outputs/resunet_e1_new/"
avg_dice = []
for holdout in os.listdir(os.path.join(exp_path)):
    for fold in os.listdir(os.path.join(exp_path,holdout)):
        if not ".pkl" in fold:
            for item in os.listdir(os.path.join(exp_path,holdout,fold)):
                if "stdout" in item:

meghbhalerao

2020-10-29 7:15:17.47: =============================== logger created =======================================
2020-10-29 7:15:17.47: 
2020-10-29 7:15:17.47: ======================== Starting new session ============================
2020-10-29 7:15:17.47: Command line arguments given: 
Namespace(device='cuda', model_cfg='./outputs/dm_e1/holdout_0/0/modelConfig.cfg', reset_trainer=False, saved_model=None, test_cfg=None, train_cfg='./outputs/dm_e1/holdout_0/0/trainConfig.cfg')
2020-10-29 7:15:18.08: Available devices to Tensorflow:
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {

meghbhalerao

DATA_DIR='./data/datasets'

MAX_DEPTH=15
MAX_NODES=30
SEARCH_METHOD=bfs
MODEL=LSTM
NUM_EPOCHS_MENTION_ONLY=1
NUM_EPOCHS_WITH_COHERENCE=30
BATCH_SIZE=32

meghbhalerao

from turtle import forward
import torch.nn as nn
import torch.nn.functional as F
import torch
from typing import Dict, Iterable, Callable
import torchvision
from models.pretrained_implementations import resnet18_pret
from models.conv_iResNet import conv_iResNet
# Acknowledgement to
# https://github.com/kuangliu/pytorch-cifar,

meghbhalerao

import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import kornia as K
import tqdm
from torch.utils.data import Dataset
from torchvision import datasets, transforms

meghbhalerao

import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision
from torch.utils.data import Dataset
from scipy.ndimage.interpolation import rotate as scipyrotate
import sys

meghbhalerao

from utils.evaluator_utils import EvaluatorUtils

ds_train = datasets.CIFAR10('data', train=True, download=True, transform=transform)
ds_test = datasets.CIFAR10('data', train=False, download=True, transform=transform)


images_all = [torch.unsqueeze(ds_train[i][0], dim=0) for i in range(len(ds_train))]
labels_all = [ds_train[i][1] for i in range(len(ds_train))]
class_pos_list = []

meghbhalerao

import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision
from torch.utils.data import Dataset
from scipy.ndimage.interpolation import rotate as scipyrotate
import sys