def evaluate_nc(
model,
multicoil=False,
three_d=False,
run_id=None,
n_epochs=200,
contrast=None,
acq_type='radial',
n_samples=None,
cuda_visible_devices='0123',
dcomp=False,
brain=False,
**acq_kwargs,
):
if multicoil:
if brain:
val_path = f'{FASTMRI_DATA_DIR}brain_multicoil_val/'
else:
val_path = f'{FASTMRI_DATA_DIR}multicoil_val/'
elif three_d:
val_path = f'{OASIS_DATA_DIR}/val/'
else:
val_path = f'{FASTMRI_DATA_DIR}singlecoil_val/'
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(cuda_visible_devices)
if multicoil:
dataset = multicoil_dataset
image_size = IM_SIZE
elif three_d:
dataset = three_d_dataset
image_size = VOLUME_SIZE
else:
dataset = singlecoil_dataset
image_size = IM_SIZE
if not three_d:
add_kwargs = {
'contrast': contrast,
'rand': False,
'inner_slices': None,
}
if multicoil:
add_kwargs.update(brain=brain)
else:
add_kwargs = {}
add_kwargs.update(**acq_kwargs)
val_set = dataset(val_path,
image_size,
acq_type=acq_type,
compute_dcomp=dcomp,
scale_factor=1e6 if not three_d else 1e-2,
**add_kwargs)
if n_samples is not None:
val_set = val_set.take(n_samples)
else:
val_set = val_set.take(199)
example_input = next(iter(val_set))[0]
inputs = _extract_first_elem_of_batch(example_input)
model(inputs)
if run_id is not None:
model.load_weights(
f'{CHECKPOINTS_DIR}checkpoints/{run_id}-{n_epochs:02d}.hdf5')
res_name = f'{run_id}_eval_on_{acq_type}'
if brain:
res_name += '_brain'
if contrast is not None:
res_name += f'_{contrast}'
if acq_kwargs:
af = acq_kwargs['af']
if af != 4:
res_name += f'_af{af}'
if three_d:
m = Metrics({'PSNR': METRIC_FUNCS['PSNR']}, res_name)
else:
m = Metrics(METRIC_FUNCS, res_name)
for x, y_true in tqdm(val_set.as_numpy_iterator(),
total=199 if n_samples is None else n_samples):
y_pred = model.predict(x, batch_size=1)
m.push(y_true[..., 0], y_pred[..., 0])
del x
del y_true
del y_pred
print(METRIC_FUNCS.keys())
print(list(m.means().values()))
m.to_csv()
return METRIC_FUNCS, list(m.means().values())
Path(CHECKPOINTS_DIR) / model_path.format(n_coils=n_coils))
model_checkpoint = model_checkpoints.get(n_coils, None)
model_kwargs.update(n_coils=n_coils)
else:
output_shape = (320, 320)
save_path = str(Path(CHECKPOINTS_DIR) / model_path)
x = x[0:2]
y_pred = reconstruct_dip(
x[1],
x[0],
model_checkpoint=model_checkpoint,
save_model=model_checkpoint is None,
save_path=save_path,
multicoil=multicoil,
n_iter=n_iter if model_checkpoint is None else n_iter // 10,
output_shape=output_shape,
**model_kwargs,
)
if brain:
model_checkpoints[n_coils] = save_path
else:
model_checkpoint = save_path
m.push(y_true[..., 0], y_pred[..., 0].numpy())
del x
del y_true
del y_pred
print(METRIC_FUNCS.keys())
print(list(m.means().values()))
m.to_csv()
return METRIC_FUNCS, list(m.means().values())