XAI optim
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everbarry
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@ -55,7 +55,6 @@ The server starts at **http://localhost:5000**.
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assignment-HAI/
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├── dashboard.py # Flask application (main entry point)
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├── train.py # Standalone training script
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├── final.ipynb # Training, evaluation, and XAI notebook
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├── map.webp # Park map background image
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├── pyproject.toml # Project metadata and dependencies
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├── uv.lock # Locked dependency versions
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@ -2,15 +2,6 @@
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This guide explains how to use the Wildlife Monitoring Dashboard as an end user (e.g. a park ranger).
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## Starting the Dashboard
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After setup (see [README.md](README.md)), run:
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```bash
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uv run python dashboard.py
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```
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Open **http://localhost:5000** in your browser.
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## Home Page — Live Map
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118
dashboard.py
118
dashboard.py
@ -9,7 +9,6 @@ Run: uv run python dashboard.py
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"""
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import csv
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import io
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import json
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import random
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import threading
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@ -20,8 +19,8 @@ from pathlib import Path
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import numpy as np
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from PIL import Image
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import matplotlib.cm as mpl_cm
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from PIL import Image, ImageDraw
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import torch
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from torch import nn
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@ -170,33 +169,41 @@ def _save_img(arr, path):
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Image.fromarray(arr).save(path)
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def compute_xai(det):
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out = XAI_DIR / det["id"]
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if (out / "meta.json").exists():
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return
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out.mkdir(exist_ok=True)
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idx = det["idx"]
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tensor, label = test_ds[idx]
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img_path, _ = test_ds.samples[idx]
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raw = np.array(
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Image.open(img_path).convert("RGB").resize((224, 224)),
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dtype=np.float64,
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) / 255.0
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inp = tensor.unsqueeze(0).to(device)
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_save_img(raw, out / "original.png")
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# single forward pass: probabilities + feature vector for neighbours
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def _forward(inp):
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"""Single split forward pass returning probs, pred_idx, and neighbour query vector."""
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with torch.no_grad():
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feats = model.features(inp)
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pooled = model.avgpool(feats)
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pooled = model.avgpool(model.features(inp))
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logits = model.classifier(torch.flatten(pooled, 1))
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probs = torch.nn.functional.softmax(logits, dim=1).cpu().squeeze()
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qf = torch.nn.functional.normalize(
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torch.flatten(pooled, 1).cpu(), dim=1,
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).squeeze()
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pred_idx = probs.argmax().item()
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return probs, probs.argmax().item(), qf
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def compute_xai(det, pre=None):
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"""Compute all XAI artefacts. `pre` is an optional dict with keys
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inp, raw, probs, pred_idx, qf to skip the duplicate forward pass."""
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out = XAI_DIR / det["id"]
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if (out / "meta.json").exists():
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return
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out.mkdir(exist_ok=True)
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if pre is not None:
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inp, raw, probs, pred_idx, qf = (
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pre["inp"], pre["raw"], pre["probs"], pre["pred_idx"], pre["qf"])
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else:
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idx = det["idx"]
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tensor, _ = test_ds[idx]
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img_path, _ = test_ds.samples[idx]
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raw = np.array(
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Image.open(img_path).convert("RGB").resize((224, 224)),
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dtype=np.float64,
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) / 255.0
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inp = tensor.unsqueeze(0).to(device)
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_save_img(raw, out / "original.png")
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probs, pred_idx, qf = _forward(inp)
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entropy = -(probs * torch.log(probs + 1e-12)).sum().item()
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# ScoreCAM
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@ -206,7 +213,7 @@ def compute_xai(det):
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out / "scorecam.png",
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)
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# LIME (reduced from 1000 to 500 samples)
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# LIME (500 samples)
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expl = lime_exp.explain_instance(raw, _lime_predict, top_labels=2, hide_color=0, num_samples=500)
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c1, c2 = expl.top_labels[0], expl.top_labels[1]
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t1, m1 = expl.get_image_and_mask(c1, positive_only=False, num_features=10, hide_rest=False)
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@ -221,15 +228,29 @@ def compute_xai(det):
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diff[segs == sid] = w1.get(sid, 0) - w2.get(sid, 0)
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mx = max(np.abs(diff).max(), 1e-8)
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diff /= mx
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col = plt.cm.RdBu_r((diff + 1) / 2)[:, :, :3]
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col = mpl_cm.RdBu_r((diff + 1) / 2)[:, :, :3]
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_save_img((0.6 * raw + 0.4 * col).clip(0, 1), out / "contrastive.png")
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# nearest neighbours (reuse qf from above)
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sims, idxs = (train_feats @ qf).topk(3)
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# nearest neighbours — 2x2 composite with labels
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sims, idxs = (train_feats @ qf).topk(4)
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nbs = []
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for k, (ni, ns) in enumerate(zip(idxs, sims)):
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_save_img(_to_display(train_ds[ni.item()][0]), out / f"nb{k+1}.png")
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nbs.append({"cls": CLASS_NAMES[train_labels[ni]], "sim": f"{ns:.3f}"})
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nb_pils = []
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for ni, ns in zip(idxs, sims):
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arr = _to_display(train_ds[ni.item()][0])
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cls = CLASS_NAMES[train_labels[ni]]
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sim = f"{ns:.3f}"
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nbs.append({"cls": cls, "sim": sim})
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pil = Image.fromarray((np.clip(arr, 0, 1) * 255).astype(np.uint8))
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draw = ImageDraw.Draw(pil)
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label = f"{cls} ({sim})"
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draw.rectangle([(0, pil.height - 22), (pil.width, pil.height)], fill=(0, 0, 0, 180))
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draw.text((6, pil.height - 20), label, fill=(255, 255, 255))
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nb_pils.append(pil)
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w, h = nb_pils[0].size
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grid = Image.new("RGB", (w * 2, h * 2))
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for i, p in enumerate(nb_pils):
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grid.paste(p, ((i % 2) * w, (i // 2) * h))
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grid.save(out / "neighbours.png")
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meta = {
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"pred": CLASS_NAMES[pred_idx],
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@ -252,10 +273,10 @@ _xai_events: dict[str, threading.Event] = {}
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_xai_lock = threading.Lock()
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def _precompute_xai(det):
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def _precompute_xai(det, pre=None):
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try:
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with _xai_lock:
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compute_xai(det)
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compute_xai(det, pre)
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finally:
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ev = _xai_events.get(det["id"])
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if ev:
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@ -280,12 +301,15 @@ def detail(det_id):
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def api_simulate():
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idx = random.randint(0, len(test_ds) - 1)
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cam_id = random.choice(list(CAMERAS.keys()))
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tensor, label = test_ds[idx]
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with torch.no_grad():
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probs = torch.nn.functional.softmax(
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model(tensor.unsqueeze(0).to(device)), dim=1,
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).cpu().squeeze()
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pred_idx = probs.argmax().item()
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tensor, _ = test_ds[idx]
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inp = tensor.unsqueeze(0).to(device)
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probs, pred_idx, qf = _forward(inp)
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img_path, _ = test_ds.samples[idx]
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raw = np.array(
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Image.open(img_path).convert("RGB").resize((224, 224)), dtype=np.float64,
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) / 255.0
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det = {
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"id": uuid.uuid4().hex[:8],
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"idx": idx,
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@ -300,14 +324,12 @@ def api_simulate():
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detections.append(det)
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out = XAI_DIR / det["id"]
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out.mkdir(exist_ok=True)
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img_path, _ = test_ds.samples[idx]
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raw = np.array(
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Image.open(img_path).convert("RGB").resize((224, 224)), dtype=np.float64,
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) / 255.0
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_save_img(raw, out / "original.png")
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pre = {"inp": inp, "raw": raw, "probs": probs, "pred_idx": pred_idx, "qf": qf}
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ev = threading.Event()
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_xai_events[det["id"]] = ev
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threading.Thread(target=_precompute_xai, args=(det,), daemon=True).start()
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threading.Thread(target=_precompute_xai, args=(det, pre), daemon=True).start()
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return jsonify(det)
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@ -326,7 +348,7 @@ def api_xai(det_id):
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meta["urls"] = {
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k: f"{base}/{k}.png"
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for k in ["original", "scorecam", "lime1", "lime2", "contrastive",
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"nb1", "nb2", "nb3"]
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"neighbours"]
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}
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return jsonify(meta)
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@ -962,11 +984,9 @@ document.addEventListener('keydown',e=>{
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cap:'Green superpixels support this class; red superpixels oppose it.'},
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{src:u.contrastive, title:'Contrastive Explanation',
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cap:d.contrast_leg},
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{src:u.neighbours, title:'Nearest Training Neighbours',
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cap:d.nbs.map((nb,i)=>`#${i+1}: ${nb.cls} (sim ${nb.sim})`).join(' | ')},
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];
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d.nbs.forEach((nb,i)=>{
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slides.push({src:u['nb'+(i+1)], title:'Nearest Neighbour '+(i+1),
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cap:nb.cls+' (cosine similarity '+nb.sim+')'});
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});
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let dots='';
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slides.forEach((_,i)=>{dots+=`<button class="dot${i===0?' active':''}" onclick="cur=${i};render()"></button>`;});
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