def __init__(self,
prefix,
epoch,
scale=1.,
gpu=-1,
thd=0.6,
margin=0,
nms_thd=0.4,
verbose=False):
super().__init__(thd=thd,
gpu=gpu,
margin=margin,
nms_thd=nms_thd,
verbose=verbose)
self.scale = scale
self._rescale = partial(cv2.resize,
dsize=None,
fx=self.scale,
fy=self.scale,
interpolation=cv2.INTER_NEAREST)
self._ctx = mx.cpu() if self.device < 0 else mx.gpu(self.device)
self._fpn_anchors = generate_anchors_fpn()
self._runtime_anchors = {}
self.model = self._load_model(prefix, epoch)
self.exec_group = self.model._exec_group
def __init__(self,
prefix,
epoch,
scale,
gpu=-1,
thd=0.5,
margin=0,
nms_thd=0.1,
verbose=False):
super().__init__(thd=thd,
gpu=gpu,
margin=margin,
nms_thd=nms_thd,
verbose=verbose)
self.scale = scale
self._rescale = partial(cv2.resize,
dsize=None,
fx=self.scale,
fy=self.scale,
interpolation=cv2.INTER_LINEAR)
self._ctx = mx.cpu() if self.device < 0 else mx.gpu(self.device)
self._fpn_anchors = generate_anchors_fpn().items()
self._runtime_anchors = {}
model = self._load_model(prefix, epoch)
self._forward = partial(model.forward, is_train=False)
self._solotion = model.get_outputs
def __init__(self,
prefix,
epoch,
ctx_id=0,
network='net3',
nms=0.4,
nocrop=False,
decay4=0.5,
vote=False):
self.ctx_id = ctx_id
self.network = network
self.decay4 = decay4
self.nms_threshold = nms
self.vote = vote
self.nocrop = nocrop
self.debug = False
self.fpn_keys = []
self.anchor_cfg = None
pixel_means = [0.0, 0.0, 0.0]
pixel_stds = [1.0, 1.0, 1.0]
pixel_scale = 1.0
self.preprocess = False
_ratio = (1., )
fmc = 3
if network == 'ssh' or network == 'vgg':
pixel_means = [103.939, 116.779, 123.68]
self.preprocess = True
elif network == 'net3':
_ratio = (1., )
elif network == 'net3a':
_ratio = (1., 1.5)
elif network == 'net6': #like pyramidbox or s3fd
fmc = 6
elif network == 'net5': #retinaface
fmc = 5
elif network == 'net5a':
fmc = 5
_ratio = (1., 1.5)
elif network == 'net4':
fmc = 4
elif network == 'net4a':
fmc = 4
_ratio = (1., 1.5)
elif network == 'x5':
fmc = 5
pixel_means = [103.52, 116.28, 123.675]
pixel_stds = [57.375, 57.12, 58.395]
elif network == 'x3':
fmc = 3
pixel_means = [103.52, 116.28, 123.675]
pixel_stds = [57.375, 57.12, 58.395]
elif network == 'x3a':
fmc = 3
_ratio = (1., 1.5)
pixel_means = [103.52, 116.28, 123.675]
pixel_stds = [57.375, 57.12, 58.395]
else:
assert False, 'network setting error %s' % network
if fmc == 3:
self._feat_stride_fpn = [32, 16, 8]
self.anchor_cfg = {
'32': {
'SCALES': (32, 16),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'16': {
'SCALES': (8, 4),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'8': {
'SCALES': (2, 1),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
}
elif fmc == 4:
self._feat_stride_fpn = [32, 16, 8, 4]
self.anchor_cfg = {
'32': {
'SCALES': (32, 16),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'16': {
'SCALES': (8, 4),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'8': {
'SCALES': (2, 1),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'4': {
'SCALES': (2, 1),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
}
elif fmc == 6:
self._feat_stride_fpn = [128, 64, 32, 16, 8, 4]
self.anchor_cfg = {
'128': {
'SCALES': (32, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'64': {
'SCALES': (16, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'32': {
'SCALES': (8, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'16': {
'SCALES': (4, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'8': {
'SCALES': (2, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
'4': {
'SCALES': (1, ),
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
},
}
elif fmc == 5:
self._feat_stride_fpn = [64, 32, 16, 8, 4]
self.anchor_cfg = {}
_ass = 2.0**(1.0 / 3)
_basescale = 1.0
for _stride in [4, 8, 16, 32, 64]:
key = str(_stride)
value = {
'BASE_SIZE': 16,
'RATIOS': _ratio,
'ALLOWED_BORDER': 9999
}
scales = []
for _ in range(3):
scales.append(_basescale)
_basescale *= _ass
value['SCALES'] = tuple(scales)
self.anchor_cfg[key] = value
print(self._feat_stride_fpn, self.anchor_cfg)
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s' % s)
dense_anchor = False
self._anchors_fpn = dict(
zip(
self.fpn_keys,
generate_anchors_fpn(self.anchor_cfg,
dense_anchor=dense_anchor)))
for k in self._anchors_fpn:
v = self._anchors_fpn[k].astype(np.float32)
self._anchors_fpn[k] = v
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
#self._bbox_pred = nonlinear_pred
#self._landmark_pred = landmark_pred
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
#digraph = mx.viz.plot_network(sym, node_attrs={"shape":"oval", "fixedsize":"false"})
#digraph.view()
if self.ctx_id >= 0:
self.ctx = mx.gpu(self.ctx_id)
self.nms = nms_wrapper(self.nms_threshold)
else:
self.ctx = mx.cpu()
self.nms = nms_wrapper(self.nms_threshold)
self.pixel_means = np.array(pixel_means, dtype=np.float32)
self.pixel_stds = np.array(pixel_stds, dtype=np.float32)
self.pixel_scale = float(pixel_scale)
print('means', self.pixel_means)
self.use_landmarks = False
if len(sym) // len(self._feat_stride_fpn) >= 3:
self.use_landmarks = True
print('use_landmarks', self.use_landmarks)
self.cascade = 0
if float(len(sym)) // len(self._feat_stride_fpn) > 3.0:
self.cascade = 1
print('cascade', self.cascade)
#self.bbox_stds = [0.1, 0.1, 0.2, 0.2]
#self.landmark_std = 0.1
self.bbox_stds = [1.0, 1.0, 1.0, 1.0]
self.landmark_std = 1.0
if self.debug:
c = len(sym) // len(self._feat_stride_fpn)
sym = sym[(c * 0):]
self._feat_stride_fpn = [32, 16, 8]
print('sym size:', len(sym))
image_size = (640, 640)
self.model = mx.mod.Module(symbol=sym,
context=self.ctx,
label_names=None)
self.model.bind(data_shapes=[('data', (1, 3, image_size[0],
image_size[1]))],
for_training=False)
self.model.set_params(arg_params, aux_params)
