File size: 4,710 Bytes
fde6d70 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | from pathlib import Path
import numpy as np
import yaml
from ultralytics import YOLO
WEIGHTS = Path("/media/rtx5090/Scripts/runs/detect/training_stats/reverse_study/All_minus_Intrinsics_merged/RTX5090/run_5/weights/best.pt")
DATA = "/media/rtx5090/IRIS/Real_Test_Set/dataset.yaml"
PROJECT = WEIGHTS.parent.parent / "evaluation"
# Path to the ORIGINAL training dataset.yaml whose class mapping is suspected to be wrong.
TRAIN_YAML = Path("/media/rtx5090/IRIS/Reverse_Ablation/All_minus_Intrinsics_merged/yolo/dataset.yaml")
CORRECTED_YAML_OUT = TRAIN_YAML.parent / "dataset_corrected.yaml"
def main():
PROJECT.mkdir(exist_ok=True)
print(f"Evaluating {WEIGHTS}")
model = YOLO(WEIGHTS)
metrics = model.val(
data=DATA,
split="test",
imgsz=1024,
batch=16,
device=0,
workers=8,
project=str(PROJECT),
name=WEIGHTS.parent.parent.name,
exist_ok=True,
save_json=True,
plots=True,
verbose=True,
)
box = metrics.box
print("\nResults")
print(f"mAP50 : {box.map50:.4f}")
print(f"mAP50-95 : {box.map:.4f}")
print(f"Precision : {box.mp:.4f}")
print(f"Recall : {box.mr:.4f}")
print(f"F1 : {box.f1.mean():.4f}")
# --- Extract raw confusion matrix ---
# Shape: (nc+1, nc+1). Rows = predicted, columns = true. Last row/col = background.
cm = metrics.confusion_matrix.matrix
names = metrics.names # dict {index: name}, from DATA yaml (test set)
nc = len(names)
if cm.shape[0] != nc + 1:
raise ValueError(
f"Unexpected confusion matrix shape {cm.shape} for nc={nc}. "
"Aborting mapping inference; check Ultralytics version compatibility."
)
np.save(PROJECT / WEIGHTS.parent.parent.name / "confusion_matrix_raw.npy", cm)
print(f"\nRaw confusion matrix saved to {PROJECT / WEIGHTS.parent.parent.name / 'confusion_matrix_raw.npy'}")
# --- Derive candidate mapping: for each TRUE class j, which class i is most often predicted? ---
class_block = cm[:nc, :nc] # exclude background row/col
predicted_for_true = np.argmax(class_block, axis=0) # length nc, index = predicted class
confidence = np.max(class_block, axis=0) / (class_block.sum(axis=0) + 1e-9)
# --- Check bijection ---
unique, counts = np.unique(predicted_for_true, return_counts=True)
is_bijection = len(unique) == nc and set(unique) == set(range(nc))
print("\nCandidate mapping (true_class -> predicted_class, confidence):")
for j in range(nc):
i = predicted_for_true[j]
flag = "" if confidence[j] > 0.5 else " <-- LOW CONFIDENCE"
print(f" {names[j]:<28s} -> {names[i]:<28s} (conf={confidence[j]:.2f}){flag}")
if not is_bijection:
collisions = {v: (unique[unique == v], np.where(predicted_for_true == v)[0]) for v in unique if counts[unique.tolist().index(v)] > 1}
print("\nWARNING: mapping is not a valid bijection. The following predicted classes")
print("receive votes from more than one true class, or some class received none:")
for j in range(nc):
if list(predicted_for_true).count(predicted_for_true[j]) > 1:
print(f" true={names[j]} -> predicted={names[predicted_for_true[j]]} (collision)")
print("\nDo not trust the auto-generated yaml below without manual review.")
print("Recommend inspecting raw counts in confusion_matrix_raw.npy for ambiguous rows,")
print("and cross-checking against rendered synthetic images for the classes involved.")
# --- Build corrected names list ---
# new_names[i] = name of the true class that training-index i actually represents
new_names = [None] * nc
for j in range(nc):
i = predicted_for_true[j]
new_names[i] = names[j]
if any(n is None for n in new_names):
unmapped = [idx for idx, n in enumerate(new_names) if n is None]
print(f"\nWARNING: training indices with no inferred mapping: {unmapped}")
print("These cannot be safely written. Inspect manually before using the corrected yaml.")
# --- Write corrected yaml, preserving original structure ---
with open(TRAIN_YAML) as f:
train_yaml = yaml.safe_load(f)
train_yaml["names"] = {i: (new_names[i] if new_names[i] is not None else f"UNRESOLVED_{i}") for i in range(nc)}
with open(CORRECTED_YAML_OUT, "w") as f:
yaml.dump(train_yaml, f, sort_keys=False, allow_unicode=True)
print(f"\nCorrected dataset.yaml written to {CORRECTED_YAML_OUT}")
print("This file is a candidate correction, not a verified one. Review before retraining.")
if __name__ == "__main__":
main()
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