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test_gcn_e.py
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from __future__ import division
import torch
import numpy as np
import os.path as osp
from mmcv.runner import load_checkpoint
from mmcv.parallel import MMDataParallel
from vegcn.datasets import build_dataset
from vegcn.deduce import peaks_to_labels
from lgcn.datasets import build_dataloader
from utils import (list2dict, write_meta, mkdir_if_no_exists, Timer)
from evaluation import evaluate, accuracy
def output_accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def test(model, dataset, cfg, logger):
if cfg.load_from:
print('load from {}'.format(cfg.load_from))
load_checkpoint(model, cfg.load_from, strict=True, logger=logger)
losses = []
accs = []
pred_conns = []
max_lst = []
multi_max = []
if cfg.gpus == 1:
data_loader = build_dataloader(dataset,
cfg.batch_size_per_gpu,
cfg.workers_per_gpu,
train=False)
size = len(data_loader)
model = MMDataParallel(model, device_ids=range(cfg.gpus))
if cfg.cuda:
model.cuda()
model.eval()
for i, data in enumerate(data_loader):
with torch.no_grad():
output, loss = model(data, return_loss=True)
if not dataset.ignore_label:
labels = data[2].view(-1)
if not cfg.regressor:
acc = output_accuracy(output, labels)
accs += [acc.item()]
losses += [loss.item()]
if not cfg.regressor:
output = output[:, 1]
if cfg.max_conn == 1:
output_max = output.max()
pred = (output == output_max).nonzero().view(-1)
pred_size = len(pred)
if pred_size > 1:
multi_max.append(pred_size)
pred_i = np.random.choice(np.arange(pred_size))
else:
pred_i = 0
pred = [int(pred[pred_i].detach().cpu().numpy())]
max_lst.append(output_max.detach().cpu().numpy())
elif cfg.max_conn > 1:
output = output.detach().cpu().numpy()
pred = output.argpartition(cfg.max_conn)[:cfg.max_conn]
pred_conns.append(pred)
if i % cfg.log_config.interval == 0:
if dataset.ignore_label:
logger.info('[Test] Iter {}/{}'.format(i, size))
else:
logger.info('[Test] Iter {}/{}: Loss {:.4f}'.format(
i, size, loss))
else:
raise NotImplementedError
if not dataset.ignore_label:
avg_loss = sum(losses) / len(losses)
logger.info('[Test] Overall Loss {:.4f}'.format(avg_loss))
if not cfg.regressor:
avg_acc = sum(accs) / len(accs)
logger.info('[Test] Overall Accuracy {:.4f}'.format(avg_acc))
if size > 0:
logger.info('max val: mean({:.2f}), max({:.2f}), min({:.2f})'.format(
sum(max_lst) / size, max(max_lst), min(max_lst)))
multi_max_size = len(multi_max)
if multi_max_size > 0:
logger.info('multi-max({:.2f}): mean({:.1f}), max({}), min({})'.format(
1. * multi_max_size / size,
sum(multi_max) / multi_max_size, max(multi_max), min(multi_max)))
return np.array(pred_conns)
def test_gcn_e(model, cfg, logger):
for k, v in cfg.model['kwargs'].items():
setattr(cfg.test_data, k, v)
dataset = build_dataset(cfg.model['type'], cfg.test_data)
pred_peaks = dataset.peaks
pred_dist2peak = dataset.dist2peak
ofn_pred = osp.join(cfg.work_dir, 'pred_conns.npz')
if osp.isfile(ofn_pred) and not cfg.force:
data = np.load(ofn_pred)
pred_conns = data['pred_conns']
inst_num = data['inst_num']
if inst_num != dataset.inst_num:
logger.warn(
'instance number in {} is different from dataset: {} vs {}'.
format(ofn_pred, inst_num, len(dataset)))
else:
if cfg.random_conns:
pred_conns = []
for nbr, dist, idx in zip(dataset.subset_nbrs,
dataset.subset_dists,
dataset.subset_idxs):
for _ in range(cfg.max_conn):
pred_rel_nbr = np.random.choice(np.arange(len(nbr)))
pred_abs_nbr = nbr[pred_rel_nbr]
pred_peaks[idx].append(pred_abs_nbr)
pred_dist2peak[idx].append(dist[pred_rel_nbr])
pred_conns.append(pred_rel_nbr)
pred_conns = np.array(pred_conns)
else:
pred_conns = test(model, dataset, cfg, logger)
for pred_rel_nbr, nbr, dist, idx in zip(pred_conns,
dataset.subset_nbrs,
dataset.subset_dists,
dataset.subset_idxs):
pred_abs_nbr = nbr[pred_rel_nbr]
pred_peaks[idx].extend(pred_abs_nbr)
pred_dist2peak[idx].extend(dist[pred_rel_nbr])
inst_num = dataset.inst_num
if len(pred_conns) > 0:
logger.info(
'pred_conns (nbr order): mean({:.1f}), max({}), min({})'.format(
pred_conns.mean(), pred_conns.max(), pred_conns.min()))
if not dataset.ignore_label and cfg.eval_interim:
subset_gt_labels = dataset.subset_gt_labels
for i in range(cfg.max_conn):
pred_peaks_labels = np.array([
dataset.idx2lb[pred_peaks[idx][i]]
for idx in dataset.subset_idxs
])
acc = accuracy(pred_peaks_labels, subset_gt_labels)
logger.info(
'[{}-th] accuracy of pred_peaks labels ({}): {:.4f}'.format(
i, len(pred_peaks_labels), acc))
# the rule for nearest nbr is only appropriate when nbrs is sorted
nearest_idxs = np.where(pred_conns[:, i] == 0)[0]
acc = accuracy(pred_peaks_labels[nearest_idxs],
subset_gt_labels[nearest_idxs])
logger.info(
'[{}-th] accuracy of pred labels (nearest: {}): {:.4f}'.format(
i, len(nearest_idxs), acc))
not_nearest_idxs = np.where(pred_conns[:, i] > 0)[0]
acc = accuracy(pred_peaks_labels[not_nearest_idxs],
subset_gt_labels[not_nearest_idxs])
logger.info(
'[{}-th] accuracy of pred labels (not nearest: {}): {:.4f}'.
format(i, len(not_nearest_idxs), acc))
with Timer('Peaks to clusters (th_cut={})'.format(cfg.tau)):
pred_labels = peaks_to_labels(pred_peaks, pred_dist2peak, cfg.tau,
inst_num)
if cfg.save_output:
logger.info(
'save predicted connectivity and labels to {}'.format(ofn_pred))
if not osp.isfile(ofn_pred) or cfg.force:
np.savez_compressed(ofn_pred,
pred_conns=pred_conns,
inst_num=inst_num)
# save clustering results
idx2lb = list2dict(pred_labels, ignore_value=-1)
folder = '{}_gcne_k_{}_th_{}_ig_{}'.format(cfg.test_name, cfg.knn,
cfg.th_sim,
cfg.test_data.ignore_ratio)
opath_pred_labels = osp.join(cfg.work_dir, folder,
'tau_{}_pred_labels.txt'.format(cfg.tau))
mkdir_if_no_exists(opath_pred_labels)
write_meta(opath_pred_labels, idx2lb, inst_num=inst_num)
# evaluation
if not dataset.ignore_label:
print('==> evaluation')
for metric in cfg.metrics:
evaluate(dataset.gt_labels, pred_labels, metric)