File size: 2,724 Bytes
dadf189 | 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 | from __future__ import annotations
from typing import Iterable
import cv2
import numpy as np
import torch
from data.degradation import DegradationPipeline
from models.concept_head import ConceptHead
def _rankdata(x: np.ndarray) -> np.ndarray:
order = np.argsort(x)
ranks = np.empty_like(order, dtype=np.float64)
ranks[order] = np.arange(len(x), dtype=np.float64)
return ranks
def _spearman(x: np.ndarray, y: np.ndarray) -> float:
if len(x) < 2 or len(y) < 2:
return 0.0
rx = _rankdata(x)
ry = _rankdata(y)
return float(np.corrcoef(rx, ry)[0, 1])
@torch.no_grad()
def compute_crosstalk_matrix(
model: torch.nn.Module,
degradation_pipeline: DegradationPipeline,
test_images: Iterable[np.ndarray],
device: str = "cpu",
) -> list[dict[str, float | str]]:
"""Return row-wise crosstalk values as list of dicts.
Each row corresponds to one degradation type and contains Spearman
correlation with all concept outputs.
"""
model.eval()
deg_types = ["blur", "noise", "jpeg", "occlusion", "dry_skin", "wet_press"]
rows: list[dict[str, float | str]] = []
to_tensor = lambda img: torch.from_numpy(img.astype(np.float32) / 255.0).unsqueeze(0).unsqueeze(0)
for deg_type in deg_types:
severity_vals: list[float] = []
concept_vals: list[np.ndarray] = []
for image in test_images:
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
for level in DegradationPipeline.LEVELS:
# Fix: seed np.random per level so occlusion block lands at a
# consistent position across severity levels. Without this, each
# level draws a random block position → Spearman ρ is computed
# over randomly-placed blocks of increasing size, not a coherent
# severity sweep → sign of ρ is unreliable for occlusion.
np.random.seed(level)
degraded = degradation_pipeline.apply(image, deg_type, level)
x = to_tensor(degraded).to(device)
outputs = model(x)
concepts = outputs["concepts"].squeeze(0).detach().cpu().numpy()
concept_vals.append(concepts)
severity_vals.append(float(level))
if not concept_vals:
continue
concept_arr = np.stack(concept_vals, axis=0)
sev_arr = np.asarray(severity_vals, dtype=np.float64)
row: dict[str, float | str] = {"degradation": deg_type}
for idx, cname in enumerate(ConceptHead.CONCEPT_NAMES):
row[cname] = _spearman(sev_arr, concept_arr[:, idx])
rows.append(row)
return rows
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