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generate_b50_example_for_all_explanations.py
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generate_b50_example_for_all_explanations.py
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from pathlib import Path
import jsonargparse
import matplotlib.pyplot as plt
import numpy as np
from generation_utils import B50_PATHS, get_transform, read_path
from matplotlib.colors import ListedColormap
EXPLANATIONS_FOLER = Path("data/")
EXPLANATION_PATHS = {
"VG": EXPLANATIONS_FOLER / "b50_vg_explanations_and_pred",
"IG": EXPLANATIONS_FOLER / "b50_ig_explanations_and_pred",
"SG": EXPLANATIONS_FOLER / "b50_sg_explanations_and_pred",
"kernelSHAP cubes": EXPLANATIONS_FOLER / "b50_kernelshap_explanations",
"kernelSHAP seg": EXPLANATIONS_FOLER / "b50_kernelshap_explanations",
}
LABELS = {
0: "background",
1: "aorta",
2: "lung_upper_lobe_left",
3: "lung_lower_lobe_left",
4: "lung_upper_lobe_right",
5: "lung_middle_lobe_right",
6: "lung_lower_lobe_right",
7: "trachea",
8: "heart",
9: "pulmonary_vein",
10: "thyroid_gland",
11: "ribs",
12: "vertebraes",
13: "autochthon_left",
14: "autochthon_right",
15: "sternum",
16: "costal_cartilages",
}
def load_explanation(
explanation,
selected_label=1,
binary_mask: bool = False,
min_val: float = 0.01,
max_val: float = 0.95,
use_abs: bool = False,
):
explanation = explanation[selected_label].squeeze()
if use_abs:
explanation = np.abs(explanation)
grad_min = np.min(explanation) * max_val
grad_max = np.max(explanation) * max_val
max_x, max_y, max_z = np.unravel_index(
np.argmax(explanation, axis=None), explanation.shape
)
if binary_mask:
_explanation = np.where(
explanation <= 0,
0,
1,
)
explanation = np.where(
(np.abs(explanation) <= (min_val * np.max(np.abs(explanation))))
| (np.abs(explanation) >= (max_val * np.max(np.abs(explanation)))),
0.5,
_explanation,
)
else:
explanation = np.where(
np.abs(explanation) <= (min_val * np.max(np.abs(explanation))),
-1,
(explanation - grad_min) / (grad_max - grad_min),
)
return explanation, (max_x, max_y, max_z)
def load_image(path, _transform):
return _transform(read_path(path)).squeeze().numpy()
def load_prediction(path):
return np.load(path)[0]
def get_cmap(binary=False, use_abs=False):
if binary:
cmap = ListedColormap(["blue", "#FFFFFF00", "red"])
elif use_abs:
cmap = plt.cm.Reds
cmap.set_under("#FFFFFF00")
else:
cmap = plt.cm.seismic
cmap.set_under("#FFFFFF00")
return cmap
def get_loading_function():
return get_transform()
def generate_example_explanations(
current_patient_id,
current_explanations,
label,
current_prediction,
min_val,
max_val,
output_path,
explanation_type="grad",
select_slices_with="explanation",
zero_background=False,
):
cmap = get_cmap(False, use_abs=False)
image_path = B50_PATHS[current_patient_id]
patient_path = "/".join(image_path.split("/")[-3:])
raw_prediction = load_prediction(current_prediction / patient_path / "pred.npy")
raw_prediction = np.argmax(raw_prediction, axis=0)
loaded_img = load_image(image_path, get_loading_function())
explanation_type_list = current_explanations.split(",")
fig, ax = plt.subplots(
len(explanation_type_list) // 2,
6,
figsize=(30, 5 * (len(explanation_type_list) // 2)),
)
fig.tight_layout()
ax = ax.flatten()
i = 0
for explanation_type in explanation_type_list:
if explanation_type == "kernelSHAP cubes":
raw_explanation = np.concatenate(
[np.zeros((1, 1) + loaded_img.shape)]
+ [
np.load(
EXPLANATION_PATHS[explanation_type]
/ patient_path
/ f"kernelshap_cubes_{i}.npy"
)
for i in range(1, 17)
],
axis=0,
)
elif explanation_type == "kernelSHAP seg":
raw_explanation = np.concatenate(
[np.zeros((1, 1) + loaded_img.shape)]
+ [
np.load(
EXPLANATION_PATHS[explanation_type]
/ patient_path
/ f"kernelshap_segmentations_{i}.npy"
)
for i in range(1, 17)
],
axis=0,
)
else:
raw_explanation = explanation = np.load(
(EXPLANATION_PATHS[explanation_type] / patient_path / "grad.npy")
)
prediction_cmap = ListedColormap(["#00000000", "yellow"])
explanation, (max_x, max_y, max_z) = load_explanation(
raw_explanation,
label,
binary_mask=False,
min_val=min_val,
max_val=max_val,
)
pred = raw_prediction == label
if select_slices_with == "prediction":
max_x = np.argmax(pred.sum(axis=(1, 2)))
max_y = np.argmax(pred.sum(axis=(0, 2)))
max_z = np.argmax(pred.sum(axis=(0, 1)))
if zero_background:
explanation = np.where(raw_prediction == 0, -1, explanation)
# Y-Z projection with rotation
ax[i].imshow(np.rot90(loaded_img[max_x], k=1), cmap="gray")
ax[i].imshow(
np.rot90(explanation[max_x], k=1),
cmap=cmap,
alpha=0.5,
vmin=0,
vmax=1,
)
ax[i].imshow(
np.rot90(pred[max_x], k=1),
cmap=prediction_cmap,
alpha=0.3,
)
ax[i].set_title(
f"Y-Z slice {explanation_type}",
fontdict={"fontsize": 23},
)
ax[i].axis("off")
ax[i].set_aspect("auto")
i += 1
# X-Z projection with rotation
ax[i].imshow(np.rot90(loaded_img[:, max_y], k=1), cmap="gray")
ax[i].imshow(
np.rot90(explanation[:, max_y], k=1),
cmap=cmap,
alpha=0.5,
vmin=0,
vmax=1,
)
ax[i].imshow(
np.rot90(pred[:, max_y], k=1),
cmap=prediction_cmap,
alpha=0.3,
)
ax[i].set_title(
f"X-Z slice {explanation_type}",
fontdict={"fontsize": 23},
)
ax[i].axis("off")
ax[i].set_aspect("auto")
i += 1
# X-Y projection with rotation
ax[i].imshow(np.rot90(loaded_img[:, :, max_z], k=1), cmap="gray")
ax[i].imshow(
np.rot90(explanation[:, :, max_z], k=1),
cmap=cmap,
alpha=0.5,
vmin=0,
vmax=1,
)
ax[i].imshow(
np.rot90(pred[:, :, max_z], k=1),
cmap=prediction_cmap,
alpha=0.3,
)
ax[i].set_title(
f"X-Y slice {explanation_type}",
fontdict={"fontsize": 23},
)
ax[i].axis("off")
ax[i].set_aspect("auto")
i += 1
plt.savefig(output_path, bbox_inches="tight", dpi=50)
if __name__ == "__main__":
parser = jsonargparse.ArgumentParser()
parser.add_argument("--current_patient_id", type=int, required=True)
parser.add_argument("--current_explanations", type=str, required=True)
parser.add_argument("--selected_label", type=int, default=1)
parser.add_argument("--current_prediction", type=Path, required=True)
parser.add_argument("--min_val", type=float, default=0.01)
parser.add_argument("--max_val", type=float, default=1.0)
parser.add_argument("--output_path", type=str, default="example_explanations.png")
parser.add_argument(
"--explanation_type",
type=str,
default="grad",
choices=["grad", "kernelshap_cubes", "kernelshap_segmentations"],
)
parser.add_argument(
"--select_slices_with",
type=str,
default="explanation",
choices=["prediction", "explanation"],
)
parser.add_argument("--zero_background", type=bool, default=False)
args = parser.parse_args()
generate_example_explanations(
args.current_patient_id,
args.current_explanations,
args.selected_label,
args.current_prediction,
args.min_val,
args.max_val,
args.output_path,
args.explanation_type,
args.select_slices_with,
args.zero_background,
)