def val_test():
all_layers = sym.get_internals()
vsym = all_layers['heatmap_output']
vmodel = mx.mod.Module(symbol=vsym, context=ctx, label_names=None)
#model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
vmodel.bind(data_shapes=[('data', (args.batch_size, ) + data_shape)])
arg_params, aux_params = model.get_params()
vmodel.set_params(arg_params, aux_params)
for target in config.val_targets:
_file = os.path.join(config.dataset_path, '%s.rec' % target)
if not os.path.exists(_file):
continue
val_iter = FaceSegIter(
path_imgrec=_file,
batch_size=args.batch_size,
#batch_size = 4,
aug_level=0,
args=args,
)
_metric = NMEMetric()
val_metric = mx.metric.create(_metric)
val_metric.reset()
val_iter.reset()
for i, eval_batch in enumerate(val_iter):
#print(eval_batch.data[0].shape, eval_batch.label[0].shape)
batch_data = mx.io.DataBatch(eval_batch.data)
model.forward(batch_data, is_train=False)
model.update_metric(val_metric, eval_batch.label)
nme_value = val_metric.get_name_value()[0][1]
print('[%d][%s]NME: %f' % (global_step[0], target, nme_value))
else:
ctx = mx.cpu()
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
all_layers = sym.get_internals()
sym = all_layers['heatmap_output']
#model = mx.mod.Module(symbol=sym, context=ctx, data_names=['data'], label_names=['softmax_label'])
model = mx.mod.Module(symbol=sym, context=ctx, data_names=['data'], label_names=None)
#model = mx.mod.Module(symbol=sym, context=ctx)
model.bind(for_training=False, data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
model.set_params(arg_params, aux_params)
val_iter = FaceSegIter(path_imgrec = rec_path,
batch_size = 1,
aug_level = 0,
)
_metric = NMEMetric()
#val_metric = mx.metric.create(_metric)
#val_metric.reset()
#val_iter.reset()
nme = []
for i, eval_batch in enumerate(val_iter):
if i%10==0:
print('processing', i)
#print(eval_batch.data[0].shape, eval_batch.label[0].shape)
batch_data = mx.io.DataBatch(eval_batch.data)
model.forward(batch_data, is_train=False)
#model.update_metric(val_metric, eval_batch.label, True)
pred_label = model.get_outputs()[-1].asnumpy()
label = eval_batch.label[0].asnumpy()
_nme = _metric.cal_nme(label, pred_label)
nme.append(_nme)
dataset = args.dataset
ctx_id = args.gpu
prefix = args.prefix
epoch = args.epoch
use_essh = args.use_essh
image_size = (args.image_size, args.image_size)
config.landmark_type = args.landmark_type
config.input_img_size = image_size[0]
config.use_essh = args.use_essh
handler = Handler(prefix=prefix, epoch=epoch, ctx_id=ctx_id)
idx_path = rec_path[0:-4] + ".idx"
imgrec = mx.recordio.MXIndexedRecordIO(idx_path, rec_path, 'r')
seq = list(imgrec.keys)
_metric = NMEMetric()
nme = []
miss = 0
for img_idx in seq:
if img_idx % 10 == 0:
print('processing %d' % img_idx)
s = imgrec.read_idx(img_idx)
header, img = mx.recordio.unpack(s)
try:
img = mx.image.imdecode(img).asnumpy()
except:
continue
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
hlabel = np.array(header.label).reshape((68, 2))
hlabel = hlabel[:, ::-1] #convert to X/W first
preds, label = handler.get_landmark(img, hlabel, dataset, use_essh)