def validate(val_data, val_dataset, net, ctx, opt):
if isinstance(ctx, mx.Context):
ctx = [ctx]
val_metric = COCOKeyPointsMetric(val_dataset,
'coco_keypoints',
in_vis_thresh=0)
for batch in tqdm(val_data, dynamic_ncols=True):
# data, scale, center, score, imgid = val_batch_fn(batch, ctx)
data, scale_box, score, imgid = val_batch_fn(batch, ctx)
outputs = [net(X) for X in data]
if opt.flip_test:
data_flip = [nd.flip(X, axis=3) for X in data]
outputs_flip = [net(X) for X in data_flip]
outputs_flipback = [
flip_heatmap(o, val_dataset.joint_pairs, shift=True)
for o in outputs_flip
]
outputs = [(o + o_flip) / 2
for o, o_flip in zip(outputs, outputs_flipback)]
if len(outputs) > 1:
outputs_stack = nd.concat(
*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
else:
outputs_stack = outputs[0].as_in_context(mx.cpu())
# preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
preds, maxvals = heatmap_to_coord_alpha_pose(outputs_stack, scale_box)
val_metric.update(preds, maxvals, score, imgid)
nullwriter = NullWriter()
oldstdout = sys.stdout
sys.stdout = nullwriter
try:
res = val_metric.get()
finally:
sys.stdout = oldstdout
return res
transform_val = AlphaPoseDefaultValTransform(num_joints=val_dataset.num_joints,
joint_pairs=val_dataset.joint_pairs,
image_size=input_size)
val_data = gluon.data.DataLoader(
val_dataset.transform(transform_val),
batch_size=batch_size, shuffle=False, last_batch='keep',
num_workers=num_workers)
return val_dataset, val_data, val_batch_fn
input_size = [int(i) for i in opt.input_size.split(',')]
val_dataset, val_data, val_batch_fn = get_data_loader(opt.dataset, batch_size,
num_workers, input_size)
val_metric = COCOKeyPointsMetric(val_dataset, 'coco_keypoints',
data_shape=tuple(input_size),
in_vis_thresh=opt.score_threshold)
use_pretrained = True if not opt.params_file else False
model_name = '_'.join((opt.model, opt.dataset))
kwargs = {'ctx': context,
'pretrained': use_pretrained,
'num_gpus': num_gpus}
net = get_model(model_name, **kwargs)
if not use_pretrained:
net.load_parameters(opt.params_file, ctx=context)
net.hybridize()
def validate(val_data, val_dataset, net, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]