def test_contour(self):
from radionets.evaluation.utils import (
read_config,
get_ifft,
read_pred,
)
from radionets.evaluation.contour import (
area_of_contour, )
import toml
import numpy as np
config = toml.load("./tests/evaluate.toml")
conf = read_config(config)
pred, img_test, img_true = read_pred(
"./tests/build/test_training/evaluation/predictions_model_eval.h5")
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
assert ~np.isnan([ifft_pred, ifft_truth]).any()
assert ifft_pred[0].shape == (64, 64)
assert ifft_truth[0].shape == (64, 64)
val = area_of_contour(ifft_pred[0], ifft_truth[0])
assert isinstance(val, np.float64)
assert ~np.isnan(val).any()
assert val > 0
def test_calc_blobs_and_crop_first_comp(self):
import torch
import toml
import numpy as np
from radionets.evaluation.utils import read_config, get_ifft, read_pred
from radionets.evaluation.blob_detection import calc_blobs, crop_first_component
config = toml.load("./tests/evaluate.toml")
conf = read_config(config)
pred, _, img_true = read_pred(
"./tests/build/test_training/evaluation/predictions_model_eval.h5")
ifft_pred = get_ifft(torch.tensor(pred[0]),
conf["amp_phase"]).reshape(64, 64)
ifft_truth = get_ifft(torch.tensor(img_true[0]),
conf["amp_phase"]).reshape(64, 64)
blobs_pred, blobs_truth = calc_blobs(ifft_pred, ifft_truth)
assert ~np.isnan(blobs_pred).any()
assert ~np.isnan(blobs_truth).any()
assert blobs_pred.all() >= 0
assert blobs_truth.all() >= 0
assert len(blobs_truth[0]) == 3
flux_pred, flux_truth = crop_first_component(ifft_pred, ifft_truth,
blobs_truth[0])
assert ~np.isnan(flux_pred).any()
assert ~np.isnan(flux_truth).any()
assert flux_pred.all() > 0
assert flux_truth.all() > 0
def test_pca(self):
import torch
import toml
from radionets.evaluation.jet_angle import im_to_array_value, bmul, pca
from radionets.evaluation.utils import read_pred, get_ifft, read_config
config = toml.load("./tests/evaluate.toml")
conf = read_config(config)
torch.set_printoptions(precision=16)
pred, img_test, img_true = read_pred(
"./tests/build/test_training/evaluation/predictions_model_eval.h5")
ifft_pred = get_ifft(pred, conf["amp_phase"])
assert ifft_pred.shape == (10, 64, 64)
pix_x, pix_y, image = im_to_array_value(torch.tensor(ifft_pred))
cog_x = (torch.sum(pix_x * image, axis=1) /
torch.sum(image, axis=1)).unsqueeze(-1)
cog_y = (torch.sum(pix_y * image, axis=1) /
torch.sum(image, axis=1)).unsqueeze(-1)
assert cog_x.shape == (10, 1)
assert cog_y.shape == (10, 1)
delta_x = pix_x - cog_x
delta_y = pix_y - cog_y
inp = torch.cat([delta_x.unsqueeze(1), delta_y.unsqueeze(1)], dim=1)
cov_w = bmul(
(cog_x - 1 * torch.sum(image * image, axis=1).unsqueeze(-1) /
cog_x).squeeze(1),
(torch.matmul(image.unsqueeze(1) * inp, inp.transpose(1, 2))),
)
eig_vals_torch, eig_vecs_torch = torch.linalg.eigh(cov_w, UPLO='U')
assert eig_vals_torch.shape == (10, 2)
assert eig_vecs_torch.shape == (10, 2, 2)
_, _, psi_torch = pca(torch.tensor(ifft_pred))
assert len(psi_torch) == 10
assert len(psi_torch[psi_torch > 360]) == 0
def test_calc_jet_angle(self):
import torch
import toml
from radionets.evaluation.jet_angle import calc_jet_angle
from radionets.evaluation.utils import read_config, read_pred, get_ifft
config = toml.load("./tests/evaluate.toml")
conf = read_config(config)
pred, _, _ = read_pred(
"./tests/build/test_training/evaluation/predictions_model_eval.h5")
image = get_ifft(pred, conf["amp_phase"])
assert image.shape == (10, 64, 64)
if not isinstance(image, torch.Tensor):
image = torch.tensor(image)
image = image.clone()
img_size = image.shape[-1]
# ignore negative pixels, which can appear in predictions
image[image < 0] = 0
if len(image.shape) == 2:
image = image.unsqueeze(0)
bs = image.shape[0]
# only use brightest pixel
max_val = torch.tensor([(i.max() * 0.4) for i in image])
max_arr = (torch.ones(img_size, img_size, bs) * max_val).permute(
2, 0, 1)
image[image < max_arr] = 0
assert image.shape == (10, 64, 64)
m, n, alpha = calc_jet_angle(image)
assert len(n) == 10
assert len(alpha) == 10
assert len(alpha[alpha > 360]) == 0
def test_get_prediction(self):
from pathlib import Path
from radionets.evaluation.utils import (
read_config,
save_pred,
)
import toml
import torch
from radionets.evaluation.train_inspection import get_prediction
config = toml.load("./tests/evaluate.toml")
conf = read_config(config)
pred, img_test, img_true = get_prediction(conf)
assert str(pred.device) == "cpu"
# test for uncertainty
if pred.shape[1] == 4:
pred_1 = pred[:, 0, :].unsqueeze(1)
pred_2 = pred[:, 2, :].unsqueeze(1)
pred = torch.cat((pred_1, pred_2), dim=1)
assert pred.shape == (10, 2, 64, 64)
assert img_test.shape == (10, 2, 64, 64)
assert img_true.shape == (10, 2, 64, 64)
pred = pred.numpy()
out_path = Path("./tests/build/test_training/evaluation/")
out_path.mkdir(parents=True, exist_ok=True)
save_pred(
str(out_path) + "/predictions_model_eval.h5",
pred,
img_test,
img_true,
"pred",
"img_test",
"img_true",
)
def main(configuration_path):
"""
Start evaluation of trained deep learning model.
Parameters
----------
configuration_path: str
Path to the configuration toml file
"""
conf = toml.load(configuration_path)
eval_conf = read_config(conf)
click.echo("\nEvaluation config:")
print(eval_conf, "\n")
for entry in conf["inspection"]:
if (
conf["inspection"][entry] is not False
and isinstance(conf["inspection"][entry], bool)
and entry != "random"
):
if (
not check_outpath(eval_conf["model_path"])
or conf["inspection"]["random"]
):
create_predictions(eval_conf)
break
if eval_conf["vis_pred"]:
create_inspection_plots(
eval_conf,
num_images=eval_conf["num_images"],
rand=eval_conf["random"],
)
click.echo(f"\nCreated {eval_conf['num_images']} test predictions.\n")
if eval_conf["vis_blobs"]:
click.echo("\nBlob visualization is enabled for source plots.\n")
if eval_conf["vis_ms_ssim"]:
click.echo("\nVisualization of ms ssim is enabled for source plots.\n")
if eval_conf["vis_dr"]:
click.echo(f"\nCreated {eval_conf['num_images']} dynamic range plots.\n")
if eval_conf["vis_source"]:
create_source_plots(
eval_conf, num_images=eval_conf["num_images"], rand=eval_conf["random"]
)
click.echo(f"\nCreated {eval_conf['num_images']} source predictions.\n")
if eval_conf["plot_contour"]:
create_contour_plots(
eval_conf, num_images=eval_conf["num_images"], rand=eval_conf["random"]
)
click.echo(f"\nCreated {eval_conf['num_images']} contour plots.\n")
if eval_conf["viewing_angle"]:
click.echo("\nStart evaluation of viewing angles.\n")
evaluate_viewing_angle(eval_conf)
if eval_conf["dynamic_range"]:
click.echo("\nStart evaluation of dynamic ranges.\n")
evaluate_dynamic_range(eval_conf)
if eval_conf["ms_ssim"]:
click.echo("\nStart evaluation of ms ssim.\n")
evaluate_ms_ssim(eval_conf)
if eval_conf["mean_diff"]:
click.echo("\nStart evaluation of mean difference.\n")
evaluate_mean_diff(eval_conf)
if eval_conf["area"]:
click.echo("\nStart evaluation of the area.\n")
evaluate_area(eval_conf)
if eval_conf["point"]:
click.echo("\nStart evaluation of point sources.\n")
evaluate_point(eval_conf)