def evaluate_ms_ssim(conf):
# create DataLoader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
vals = []
if img_size < 160:
click.echo("\nThis is only a placeholder!\
Images too small for meaningful ms ssim calculations.\n")
# iterate trough DataLoader
for i, (img_test, img_true) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
if img_size < 160:
ifft_truth = pad_unsqueeze(torch.tensor(ifft_truth))
ifft_pred = pad_unsqueeze(torch.tensor(ifft_pred))
vals.extend([
ms_ssim(pred.unsqueeze(0),
truth.unsqueeze(0),
data_range=truth.max())
for pred, truth in zip(ifft_pred, ifft_truth)
])
click.echo("\nCreating ms-ssim histogram.\n")
vals = torch.tensor(vals)
histogram_ms_ssim(
vals,
out_path,
plot_format=conf["format"],
)
click.echo(f"\nThe mean ms-ssim value is {vals.mean()}.\n")
def evaluate_point(conf):
# create DataLoader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
vals = []
lengths = []
for i, (img_test, img_true, source_list) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
fluxes_pred, fluxes_truth, length = flux_comparison(
ifft_pred, ifft_truth, source_list)
val = ((fluxes_pred - fluxes_truth) / fluxes_truth) * 100
vals += list(val)
lengths += list(length)
vals = np.concatenate(vals).ravel()
lengths = np.array(lengths, dtype="object")
mask = lengths < 10
click.echo("\nCreating pointsources histogram.\n")
hist_point(vals, mask, out_path, plot_format=conf["format"])
click.echo(f"\nThe mean flux difference is {vals.mean()}.\n")
click.echo("\nCreating linear extent-mean flux diff plot.\n")
plot_length_point(lengths,
vals,
mask,
out_path,
plot_format=conf["format"])
def evaluate_mean_diff(conf):
# create DataLoader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
vals = []
# iterate trough DataLoader
for i, (img_test, img_true) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
for pred, truth in zip(ifft_pred, ifft_truth):
blobs_pred, blobs_truth = calc_blobs(pred, truth)
flux_pred, flux_truth = crop_first_component(
pred, truth, blobs_truth[0])
vals.extend([
(flux_pred.mean() - flux_truth.mean()) / flux_truth.mean()
])
click.echo("\nCreating mean_diff histogram.\n")
vals = torch.tensor(vals) * 100
histogram_mean_diff(
vals,
out_path,
plot_format=conf["format"],
)
click.echo(f"\nThe mean difference is {vals.mean()}.\n")
def evaluate_dynamic_range(conf):
# create Dataloader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
dr_truths = np.array([])
dr_preds = np.array([])
# iterate trough DataLoader
for i, (img_test, img_true) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
dr_truth, dr_pred, _, _ = calc_dr(ifft_truth, ifft_pred)
dr_truths = np.append(dr_truths, dr_truth)
dr_preds = np.append(dr_preds, dr_pred)
click.echo(f"\nMean dynamic range for true source distributions:\
{round(dr_truths.mean())}\n")
click.echo(f"\nMean dynamic range for predicted source distributions:\
{round(dr_preds.mean())}\n")
click.echo("\nCreating histogram of dynamic ranges.\n")
histogram_dynamic_ranges(
dr_truths,
dr_preds,
out_path,
plot_format=conf["format"],
)
def evaluate_viewing_angle(conf):
# create DataLoader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
alpha_truths = []
alpha_preds = []
# iterate trough DataLoader
for i, (img_test, img_true) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
m_truth, n_truth, alpha_truth = calc_jet_angle(
torch.tensor(ifft_truth))
m_pred, n_pred, alpha_pred = calc_jet_angle(torch.tensor(ifft_pred))
alpha_truths.extend(abs(alpha_truth))
alpha_preds.extend(abs(alpha_pred))
alpha_truths = torch.tensor(alpha_truths)
alpha_preds = torch.tensor(alpha_preds)
click.echo("\nCreating histogram of jet angles.\n")
histogram_jet_angles(
alpha_truths,
alpha_preds,
out_path,
plot_format=conf["format"],
)
def evaluate_area(conf):
# create DataLoader
loader = create_databunch(conf["data_path"], conf["fourier"],
conf["source_list"], conf["batch_size"])
model_path = conf["model_path"]
out_path = Path(model_path).parent / "evaluation"
out_path.mkdir(parents=True, exist_ok=True)
img_size = loader.dataset[0][0][0].shape[-1]
model = load_pretrained_model(conf["arch_name"], conf["model_path"],
img_size)
if conf["model_path_2"] != "none":
model_2 = load_pretrained_model(conf["arch_name_2"],
conf["model_path_2"], img_size)
vals = []
# iterate trough DataLoader
for i, (img_test, img_true) in enumerate(tqdm(loader)):
pred = eval_model(img_test, model)
if conf["model_path_2"] != "none":
pred_2 = eval_model(img_test, model_2)
pred = torch.cat((pred, pred_2), dim=1)
ifft_truth = get_ifft(img_true, amp_phase=conf["amp_phase"])
ifft_pred = get_ifft(pred, amp_phase=conf["amp_phase"])
for pred, truth in zip(ifft_pred, ifft_truth):
val = area_of_contour(pred, truth)
vals.extend([val])
click.echo("\nCreating eval_area histogram.\n")
vals = torch.tensor(vals)
histogram_area(
vals,
out_path,
plot_format=conf["format"],
)
click.echo(f"\nThe mean area ratio is {vals.mean()}.\n")