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 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 heatmap_to_coord(heatmaps, bbox_list):
center_list = []
scale_list = []
for i, bbox in enumerate(bbox_list):
x0 = bbox[0]
y0 = bbox[1]
x1 = bbox[2]
y1 = bbox[3]
w = (x1 - x0) / 2
h = (y1 - y0) / 2
center_list.append(np.array([x0 + w, y0 + h]))
scale_list.append(np.array([w, h]))
coords, maxvals = get_final_preds(heatmaps, center_list, scale_list)
return coords, maxvals
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