def validate(m, heatmap_to_coord, batch_size=20):
det_dataset = builder.build_dataset(cfg.DATASET.TEST, preset_cfg=cfg.DATA_PRESET, detector_cfg=cfg.DETECTOR, train=False, opt=opt)
eval_joints = det_dataset.EVAL_JOINTS
det_loader = torch.utils.data.DataLoader(
det_dataset, batch_size=batch_size, shuffle=False, num_workers=20, drop_last=False)
kpt_json = []
m.eval()
norm_type = cfg.LOSS.get('NORM_TYPE', None)
hm_size = cfg.DATA_PRESET.HEATMAP_SIZE
for inps, crop_bboxes, bboxes, img_ids, scores, imghts, imgwds in tqdm(det_loader, dynamic_ncols=True):
if isinstance(inps, list):
inps = [inp.cuda() for inp in inps]
else:
inps = inps.cuda()
output = m(inps)
if opt.flip_test:
if isinstance(inps, list):
inps_flip = [flip(inp).cuda() for inp in inps]
else:
inps_flip = flip(inps).cuda()
output_flip = flip_heatmap(m(inps_flip), det_dataset.joint_pairs, shift=True)
pred_flip = output_flip[:, eval_joints, :, :]
else:
output_flip = None
pred = output
assert pred.dim() == 4
pred = pred[:, eval_joints, :, :]
for i in range(output.shape[0]):
bbox = crop_bboxes[i].tolist()
pose_coords, pose_scores = heatmap_to_coord(
pred[i], bbox, hms_flip=pred_flip[i], hm_shape=hm_size, norm_type=norm_type)
keypoints = np.concatenate((pose_coords, pose_scores), axis=1)
keypoints = keypoints.reshape(-1).tolist()
data = dict()
data['bbox'] = bboxes[i, 0].tolist()
data['image_id'] = int(img_ids[i])
data['area'] = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
# data['score'] = float(scores[i] + np.mean(pose_scores) + np.max(pose_scores))
data['score'] = float(scores[i])
data['category_id'] = 1
data['keypoints'] = keypoints
kpt_json.append(data)
kpt_json = oks_pose_nms(kpt_json)
with open('./exp/json/validate_rcnn_kpt.json', 'w') as fid:
json.dump(kpt_json, fid)
res = evaluate_mAP('./exp/json/validate_rcnn_kpt.json', ann_type='keypoints')
return res['AP']
def validate(m, heatmap_to_coord, batch_size=20):
det_dataset = builder.build_dataset(cfg.DATASET.TEST,
preset_cfg=cfg.DATA_PRESET,
train=False,
opt=opt)
eval_joints = det_dataset.EVAL_JOINTS
det_loader = torch.utils.data.DataLoader(det_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=20,
drop_last=False)
kpt_json = []
m.eval()
norm_type = cfg.LOSS.get('NORM_TYPE', None)
hm_size = cfg.DATA_PRESET.HEATMAP_SIZE
combined_loss = (cfg.LOSS.get('TYPE') == 'Combined')
halpe = (cfg.DATA_PRESET.NUM_JOINTS == 133) or (cfg.DATA_PRESET.NUM_JOINTS
== 136)
for inps, crop_bboxes, bboxes, img_ids, scores, imghts, imgwds in tqdm(
det_loader, dynamic_ncols=True):
if isinstance(inps, list):
inps = [inp.cuda() for inp in inps]
else:
inps = inps.cuda()
output = m(inps)
if opt.flip_test:
if isinstance(inps, list):
inps_flip = [flip(inp).cuda() for inp in inps]
else:
inps_flip = flip(inps).cuda()
output_flip = flip_heatmap(m(inps_flip),
det_dataset.joint_pairs,
shift=True)
pred_flip = output_flip[:, eval_joints, :, :]
else:
output_flip = None
pred_flip = None
pred = output
assert pred.dim() == 4
pred = pred[:, eval_joints, :, :]
if output.size()[1] == 68:
face_hand_num = 42
else:
face_hand_num = 110
for i in range(output.shape[0]):
bbox = crop_bboxes[i].tolist()
if combined_loss:
pose_coords_body_foot, pose_scores_body_foot = heatmap_to_coord[
0](pred[i][det_dataset.EVAL_JOINTS[:-face_hand_num]],
bbox,
hm_shape=hm_size,
norm_type=norm_type,
hms_flip=pred_flip[i][
det_dataset.EVAL_JOINTS[:-face_hand_num]]
if pred_flip is not None else None)
pose_coords_face_hand, pose_scores_face_hand = heatmap_to_coord[
1](pred[i][det_dataset.EVAL_JOINTS[-face_hand_num:]],
bbox,
hm_shape=hm_size,
norm_type=norm_type,
hms_flip=pred_flip[i][
det_dataset.EVAL_JOINTS[-face_hand_num:]]
if pred_flip is not None else None)
pose_coords = np.concatenate(
(pose_coords_body_foot, pose_coords_face_hand), axis=0)
pose_scores = np.concatenate(
(pose_scores_body_foot, pose_scores_face_hand), axis=0)
else:
pose_coords, pose_scores = heatmap_to_coord(
pred[i][det_dataset.EVAL_JOINTS],
bbox,
hm_shape=hm_size,
norm_type=norm_type,
hms_flip=pred_flip[i][det_dataset.EVAL_JOINTS]
if pred_flip is not None else None)
keypoints = np.concatenate((pose_coords, pose_scores), axis=1)
keypoints = keypoints.reshape(-1).tolist()
data = dict()
data['bbox'] = bboxes[i, 0].tolist()
data['image_id'] = int(img_ids[i])
data['area'] = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
data['score'] = float(scores[i] + np.mean(pose_scores) +
1.25 * np.max(pose_scores))
# data['score'] = float(scores[i])
data['category_id'] = 1
data['keypoints'] = keypoints
kpt_json.append(data)
if opt.ppose_nms:
from alphapose.utils.pPose_nms import ppose_nms_validate_preprocess, pose_nms, write_json
final_result = []
tmp_data = ppose_nms_validate_preprocess(kpt_json)
for key in tmp_data:
boxes, scores, ids, preds_img, preds_scores = tmp_data[key]
boxes, scores, ids, preds_img, preds_scores, pick_ids = \
pose_nms(boxes, scores, ids, preds_img, preds_scores, 0, cfg.LOSS.get('TYPE') == 'MSELoss')
_result = []
for k in range(len(scores)):
_result.append({
'keypoints':
preds_img[k],
'kp_score':
preds_scores[k],
'proposal_score':
torch.mean(preds_scores[k]) + scores[k] +
1.25 * max(preds_scores[k]),
'idx':
ids[k],
'box': [
boxes[k][0], boxes[k][1], boxes[k][2] - boxes[k][0],
boxes[k][3] - boxes[k][1]
]
})
im_name = str(key).zfill(12) + '.jpg'
result = {'imgname': im_name, 'result': _result}
final_result.append(result)
write_json(final_result,
'./exp/json/',
form='coco',
for_eval=True,
outputfile='validate_rcnn_kpt.json')
else:
if opt.oks_nms:
from alphapose.utils.pPose_nms import oks_pose_nms
kpt_json = oks_pose_nms(kpt_json)
with open('./exp/json/validate_rcnn_kpt.json', 'w') as fid:
json.dump(kpt_json, fid)
sysout = sys.stdout
res = evaluate_mAP('./exp/json/validate_rcnn_kpt.json',
ann_type='keypoints',
ann_file=os.path.join(cfg.DATASET.TEST.ROOT,
cfg.DATASET.TEST.ANN),
halpe=halpe)
sys.stdout = sysout
return res