with torch.no_grad():

net.eval()

if model_file is not None:

net.load_state_dict(torch.load(model_file))

final_res = {}

final_res['version'] = 'VERSION 1.3'

final_res['results'] = {}

final_res['external_data'] = {'used': True, 'details': 'Features from I3D Network'}

num_correct = 0.

num_total = 0.

load_iter = iter(test_loader)

for i in range(len(test_loader.dataset)):

_data, _label, _, vid_name, vid_num_seg = next(load_iter)

_data = _data.cuda()

_label = _label.cuda()

vid_num_seg = vid_num_seg[0].cpu().item()

num_segments = _data.shape[1]

score_act, _, feat_act, feat_bkg, features, cas_softmax = net(_data)

feat_magnitudes_act = torch.mean(torch.norm(feat_act, dim=2), dim=1)

feat_magnitudes_bkg = torch.mean(torch.norm(feat_bkg, dim=2), dim=1)

label_np = _label.cpu().data.numpy()

score_np = score_act[0].cpu().data.numpy()

pred_np = np.zeros_like(score_np)

pred_np[np.where(score_np < config.class_thresh)] = 0

pred_np[np.where(score_np >= config.class_thresh)] = 1

correct_pred = np.sum(label_np == pred_np, axis=1)

num_correct += np.sum((correct_pred == config.num_classes).astype(np.float32))

num_total += correct_pred.shape[0]

feat_magnitudes = torch.norm(features, p=2, dim=2)

feat_magnitudes = utils.minmax_norm(feat_magnitudes, max_val=feat_magnitudes_act, min_val=feat_magnitudes_bkg)

feat_magnitudes = feat_magnitudes.repeat((config.num_classes, 1, 1)).permute(1, 2, 0)

cas = utils.minmax_norm(cas_softmax * feat_magnitudes)

pred = np.where(score_np >= config.class_thresh)[0]

if len(pred) == 0:

pred = np.array([np.argmax(score_np)])

cas_pred = cas[0].cpu().numpy()[:, pred]

cas_pred = np.reshape(cas_pred, (num_segments, -1, 1))

cas_pred = utils.upgrade_resolution(cas_pred, config.scale)

proposal_dict = {}

feat_magnitudes_np = feat_magnitudes[0].cpu().data.numpy()[:, pred]

feat_magnitudes_np = np.reshape(feat_magnitudes_np, (num_segments, -1, 1))

feat_magnitudes_np = utils.upgrade_resolution(feat_magnitudes_np, config.scale)

for i in range(len(config.act_thresh_cas)):

cas_temp = cas_pred.copy()

zero_location = np.where(cas_temp[:, :, 0] < config.act_thresh_cas[i])

cas_temp[zero_location] = 0

seg_list = []

for c in range(len(pred)):

pos = np.where(cas_temp[:, c, 0] > 0)

seg_list.append(pos)

proposals = utils.get_proposal_oic(seg_list, cas_temp, score_np, pred, config.scale, \

vid_num_seg, config.feature_fps, num_segments)

for i in range(len(proposals)):

class_id = proposals[i][0][0]

if class_id not in proposal_dict.keys():

proposal_dict[class_id] = []

proposal_dict[class_id] += proposals[i]

for i in range(len(config.act_thresh_magnitudes)):

cas_temp = cas_pred.copy()

feat_magnitudes_np_temp = feat_magnitudes_np.copy()

zero_location = np.where(feat_magnitudes_np_temp[:, :, 0] < config.act_thresh_magnitudes[i])

feat_magnitudes_np_temp[zero_location] = 0

seg_list = []

for c in range(len(pred)):

pos = np.where(feat_magnitudes_np_temp[:, c, 0] > 0)

seg_list.append(pos)

proposals = utils.get_proposal_oic(seg_list, cas_temp, score_np, pred, config.scale, \

vid_num_seg, config.feature_fps, num_segments)

for i in range(len(proposals)):

class_id = proposals[i][0][0]

if class_id not in proposal_dict.keys():

proposal_dict[class_id] = []

proposal_dict[class_id] += proposals[i]

final_proposals = []

for class_id in proposal_dict.keys():

final_proposals.append(utils.nms(proposal_dict[class_id], 0.6))

final_res['results'][vid_name[0]] = utils.result2json(final_proposals)

test_acc = num_correct / num_total

json_path = os.path.join(config.output_path, 'result.json')

with open(json_path, 'w') as f:

json.dump(final_res, f)

f.close()

tIoU_thresh = np.linspace(0.1, 0.7, 7)

anet_detection = ANETdetection(config.gt_path, json_path,

subset='test', tiou_thresholds=tIoU_thresh,

verbose=False, check_status=False)

mAP, average_mAP = anet_detection.evaluate()

logger.log_value('Test accuracy', test_acc, step)

for i in range(tIoU_thresh.shape[0]):

logger.log_value('mAP@{:.1f}'.format(tIoU_thresh[i]), mAP[i], step)

logger.log_value('Average mAP', average_mAP, step)

test_info["step"].append(step)

test_info["test_acc"].append(test_acc)

test_info["average_mAP"].append(average_mAP)

for i in range(tIoU_thresh.shape[0]):