from pynet import NetParameters
from pynet.datasets import DataManager, fetch_registration
from pynet.utils import setup_logging
from pynet.interfaces import (VoxelMorphNetRegister, ADDNetRegister,
VTNetRegister, RCNetRegister)
import pynet
from pynet.models.voxelmorphnet import FlowRegularizer
from pynet.models.vtnet import ADDNetRegularizer
from torch.optim import lr_scheduler
from pynet.plotting import plot_history
from pynet.history import History
from pynet.losses import MSELoss, NCCLoss, RCNetLoss, PCCLoss
from pynet.plotting import Board, update_board
import matplotlib.pyplot as plt
setup_logging(level="debug")
logger = logging.getLogger("pynet")
losses = pynet.get_tools(tool_name="losses")
outdir = "/neurospin/nsap/tmp/registration"
data = fetch_registration(datasetdir=outdir)
manager = DataManager(input_path=data.input_path,
metadata_path=data.metadata_path,
number_of_folds=2,
batch_size=8,
sampler="random",
stratify_label="studies",
projection_labels={"studies": ["abide"]},
test_size=0.1,
add_input=True,
sample_size=0.1)
from pynet.utils import setup_logging
from pynet.metrics import SKMetrics
from pynet.plotting import Board, update_board
from mne.viz import circular_layout, plot_connectivity_circle
import collections
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import scipy
from scipy.stats.stats import pearsonr
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
setup_logging(level="info")
logger = logging.getLogger("pynet")
# Load the data
outdir = "/tmp/graph_connectome"
(injury, x_train, y_train, x_test, y_test, x_valid,
y_valid) = get_fetchers()["fetch_connectome"](outdir)
labels = [str(idx) for idx in range(1, x_train.shape[-1] + 1)]
for name, (x, y) in (("train", (x_train, y_train)), ("test", (x_test, y_test)),
("validation", (x_valid, y_valid))):
print("{0}: x {1} - y {2}".format(name, x.shape, y.shape))
# View the realistic base connectome and the injury signatures.
plt.figure(figsize=(16, 4))
plt.subplot(1, 3, 1)
plt.imshow(injury.X_mn, interpolation="None")
