# fo.data,
# final_weight,
# features[-1],
# thresholds[-1],
# preds,
# logits,
# prev_layername=layernames[-1],
# prev_tally=tallies[-1],
# contributors=contrs_spread[-1],
# skip=False,
# )
# # ==== STEP 6: generate index html ====
# # Add final contrs/tallies/layernames/htmls:
# all_card_htmls["final"] = final_card_htmls
# if len(all_card_htmls) <= 1:
# # Don't overwrite the existing one
# print("Warning - no card htmls collected")
# else:
# with open(all_card_htmls_fname, "wb") as f:
# pickle.dump(all_card_htmls, f)
# # Add final tallies and layernames (one indexed)
# tallies.append({k: {"label": v} for k, v in ade20k.I2S.items()})
# layernames.append("final")
# vindex.generate_index(layernames, contrs_spread, tallies, all_card_htmls)
if settings.CLEAN:
clean()
# setup a hook to extract unit info
for name in args.FEATURE_NAMES:
model._modules.get(name).register_forward_hook(hook_feature)
if args.GPU:
model.cuda()
model.eval()
############ STEP 1: feature extraction ###############
features, maxfeature = fo.feature_extraction(model=model)
for layer_id, layer in enumerate(args.FEATURE_NAMES):
############ STEP 2: calculating threshold ############
thresholds = fo.quantile_threshold(features[layer_id],
savepath="quantile.npy")
############ STEP 3: calculating IoU scores ###########
tally_result = fo.tally(features[layer_id],
thresholds,
savepath="tally.csv")
############ STEP 4: generating results ###############
generate_html_summary(fo.data,
layer,
args,
tally_result=tally_result,
maxfeature=maxfeature[layer_id],
features=features[layer_id],
thresholds=thresholds)
if args.CLEAN:
clean(args)