def validate(val_data, val_dataset, net, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
val_metric.reset()
from tqdm import tqdm
for batch in tqdm(val_data):
# 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)
# print(preds, maxvals, scale_box)
# print(preds, maxvals)
# raise
val_metric.update(preds, maxvals, score, imgid)
res = val_metric.get()
return
def validate(val_data, val_dataset, net, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
val_metric.reset()
from tqdm import tqdm
for batch in tqdm(val_data):
data, scale, center, 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())
val_metric.update(preds, maxvals, score, imgid)
res = val_metric.get()
return
def validate(val_data, val_dataset, net, ctx):
if isinstance(ctx, mx.Context):
ctx = [ctx]
val_metric.reset()
from tqdm import tqdm
for batch in tqdm(val_data):
data, scale, center, 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 opt.dsnt:
outputs = [net_dsnt(X)[0] for X in outputs]
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())
if opt.dsnt:
preds = (outputs_stack - 0.5) * scale.expand_dims(
axis=1) + center.expand_dims(axis=1)
maxvals = nd.ones(preds.shape[0:2] + (1, ))
else:
preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(),
scale.asnumpy())
val_metric.update(preds, maxvals, score, imgid)
metric_name, metric_score = val_metric.get()
print("Inference Completed! %s = %.4f" % (metric_name, metric_score))
return
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