def __init__(self, feat_stride_fpn, scales, ratios, output_score,
rpn_pre_nms_top_n, rpn_post_nms_top_n, threshold, rpn_min_size_fpn):
super(ProposalFPNOperator, self).__init__()
self._feat_stride_fpn = np.fromstring(feat_stride_fpn[1:-1], dtype=int, sep=',')
self.fpn_keys = []
fpn_stride = []
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s'%s)
fpn_stride.append(int(s))
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',')
self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn(base_size=fpn_stride, scales=self._scales, ratios=self._ratios)))
self._num_anchors = dict(zip(self.fpn_keys, [anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._output_score = output_score
self._rpn_pre_nms_top_n = rpn_pre_nms_top_n
self._rpn_post_nms_top_n = rpn_post_nms_top_n
self._threshold = threshold
self._rpn_min_size_fpn = dict(zip(self.fpn_keys, np.fromstring(rpn_min_size_fpn[1:-1], dtype=int, sep=',')))
self._bbox_pred = nonlinear_pred
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride_fpn)
print 'anchors: {}'.format(self._anchors_fpn)
print 'num_anchors: {}'.format(self._num_anchors)
def __init__(self, output_score,
rpn_pre_nms_top_n, rpn_post_nms_top_n, threshold, rpn_min_size_fpn):
super(ProposalFPNOperator, self).__init__()
#self._feat_stride_fpn = np.fromstring(feat_stride_fpn[1:-1], dtype=int, sep=',')
self._feat_stride_fpn = config.RPN_FEAT_STRIDE
self.fpn_keys = []
fpn_stride = []
fpn_base_size = []
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s'%s)
fpn_stride.append(int(s))
fpn_base_size.append(config.RPN_ANCHOR_CFG[str(s)]['BASE_SIZE'])
#self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
#self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',')
#print('ProposalFPN scales', self._scales)
#print('ProposalFPN ratios', self._ratios)
self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn()))
self._num_anchors = dict(zip(self.fpn_keys, [anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._output_score = output_score
self._rpn_pre_nms_top_n = rpn_pre_nms_top_n
self._rpn_post_nms_top_n = rpn_post_nms_top_n
self._threshold = threshold
self._rpn_min_size_fpn = dict(zip(self.fpn_keys, np.fromstring(rpn_min_size_fpn[1:-1], dtype=int, sep=',')))
self._bbox_pred = nonlinear_pred
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride_fpn)
print 'anchors: {}'.format(self._anchors_fpn)
print 'num_anchors: {}'.format(self._num_anchors)
def __init__(self, args, nms=0.4, verbose=False):
self.threshold = args.threshold
self.scales = args.scales
self.nms_threshold = nms
self.ctx_id = args.gpu
self.margin = np.array([-args.face_margin, -args.face_margin,
args.face_margin, args.face_margin])
self._feat_stride_fpn = [int(k) for k in anchor_shape.keys()]
self.anchor_cfg = anchor_shape
self.fpn_keys = [f'stride{s}' for s in self._feat_stride_fpn]
anchors_fpn_list = generate_anchors_fpn(cfg=self.anchor_cfg)
self._anchors_fpn = dict(
zip(self.fpn_keys, np.asarray(anchors_fpn_list, dtype=np.float32)))
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
if self.ctx_id < 0:
self.ctx = mx.cpu()
self.nms = cpu_nms_wrapper(self.nms_threshold)
else:
self.ctx = mx.gpu(self.ctx_id)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
sym, arg_params, aux_params = mx.model.load_checkpoint(
args.retina_model, 0)
self.model = mx.mod.Module(sym, context=self.ctx, label_names=None)
self.model.bind(
data_shapes=[('data', (1, 3, 640, 640))], for_training=False)
self.model.set_params(arg_params, aux_params)
def generate_anchors(self):
_ratio = (1., )
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
},
}
self.fpn_keys = [32, 16, 8]
self._anchors_fpn = dict(
zip(self.fpn_keys,
generate_anchors_fpn(dense_anchor=False, cfg=anchor_cfg)))
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
def __init__(self, prefix, epoch, ctx_id=0, test_mode=False):
self.ctx_id = ctx_id
self.test_mode = test_mode
self.fpn_keys = []
fpn_stride = []
fpn_base_size = []
self._feat_stride_fpn = [32, 16, 8]
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s' % s)
fpn_stride.append(int(s))
fpn_base_size.append(16)
self._scales = np.array([32, 16, 8, 4, 2, 1])
self._ratios = np.array([1.0] * len(self._feat_stride_fpn))
self._anchors_fpn = dict(
zip(
self.fpn_keys,
generate_anchors_fpn(base_size=fpn_base_size,
scales=self._scales,
ratios=self._ratios)))
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._rpn_pre_nms_top_n = 1000
# self._rpn_post_nms_top_n = rpn_post_nms_top_n
# self.score_threshold = 0.05
self.nms_threshold = 0.3
self._bbox_pred = nonlinear_pred
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
if self.ctx_id >= 0:
self.ctx = mx.gpu(self.ctx_id)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
else:
self.ctx = mx.cpu()
self.nms = cpu_nms_wrapper(self.nms_threshold)
# self.nms = py_nms_wrapper(self.nms_threshold)
# self.pixel_means = np.array([103.939, 116.779, 123.68]) #BGR,VGG16
self.pixel_means = np.array([0.0, 0.0, 0.0]) #BGR,R50
if not self.test_mode:
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)
else:
from rcnn.core.module import MutableModule
image_size = (2400, 2400)
data_shape = [('data', (1, 3, image_size[0], image_size[1]))]
self.model = MutableModule(symbol=sym,
data_names=['data'],
label_names=None,
context=self.ctx,
max_data_shapes=data_shape)
self.model.bind(data_shape, None, for_training=False)
self.model.set_params(arg_params, aux_params)
def __init__(self, gpu=0, test_mode=False):
self.ctx_id = gpu
self.ctx = mx.gpu(self.ctx_id)
self.fpn_keys = []
fpn_stride = []
fpn_base_size = []
self._feat_stride_fpn = [32, 16, 8]
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s' % s)
fpn_stride.append(int(s))
fpn_base_size.append(16)
self._scales = np.array([32, 16, 8, 4, 2, 1])
self._ratios = np.array([1.0] * len(self._feat_stride_fpn))
self._anchors_fpn = dict(
zip(
self.fpn_keys,
generate_anchors_fpn(base_size=fpn_base_size,
scales=self._scales,
ratios=self._ratios)))
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._rpn_pre_nms_top_n = 1000
# self._rpn_post_nms_top_n = rpn_post_nms_top_n
# self.score_threshold = 0.05
self.nms_threshold = config.TEST.NMS
self._bbox_pred = nonlinear_pred
base_path = os.path.dirname(__file__)
sym, arg_params, aux_params = mx.model.load_checkpoint(
base_path + '/model/model-v2.0/e2e', 1)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
self.pixel_means = np.array([103.939, 116.779, 123.68]) # BGR
if not test_mode:
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)
else:
from rcnn.core.module import MutableModule
image_size = (640, 640)
data_shape = [('data', (1, 3, image_size[0], image_size[1]))]
self.model = MutableModule(symbol=sym,
data_names=['data'],
label_names=None,
context=self.ctx,
max_data_shapes=data_shape)
self.model.bind(data_shape, None, for_training=False)
self.model.set_params(arg_params, aux_params)
print('init ssh success')
def __init__(self, model_weights, use_gpu_nms=True, nms=0.4, decay4=0.5):
self.decay4 = decay4
self.nms_threshold = nms
self.fpn_keys = []
self.anchor_cfg = None
self.preprocess = False
_ratio = (1., )
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
},
}
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s' % s)
self._anchors_fpn = dict(
zip(self.fpn_keys,
generate_anchors_fpn(dense_anchor=False, cfg=self.anchor_cfg)))
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()]))
if use_gpu_nms:
self.nms = gpu_nms_wrapper(self.nms_threshold, 0)
else:
self.nms = cpu_nms_wrapper(self.nms_threshold)
pixel_means = [0.0, 0.0, 0.0]
pixel_stds = [1.0, 1.0, 1.0]
pixel_scale = 1.0
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)
self.bbox_stds = [1.0, 1.0, 1.0, 1.0]
self.scales = [1024, 1980]
self.model = tf.function(RetinaFaceNetwork(model_weights).model,
input_signature=(tf.TensorSpec(
shape=[None, None, None, 3],
dtype=np.float32), ))
def __init__(self, prefix, epoch, ctx_id=0):
self.ctx_id = ctx_id
self.ctx = mx.gpu(self.ctx_id)
self.fpn_keys = []
fpn_stride = []
fpn_base_size = []
self._feat_stride_fpn = [32, 16, 8]
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s'%s)
fpn_stride.append(int(s))
fpn_base_size.append(16)
self._scales = np.array([32,16,8,4,2,1])
self._ratios = np.array([1.0]*len(self._feat_stride_fpn))
self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn(base_size=fpn_base_size, scales=self._scales, ratios=self._ratios)))
self._num_anchors = dict(zip(self.fpn_keys, [anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._rpn_pre_nms_top_n = 1000
#self._rpn_post_nms_top_n = rpn_post_nms_top_n
#self.score_threshold = 0.05
self.nms_threshold = 0.3
self._bbox_pred = nonlinear_pred
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
#all_layers = sym.get_internals()
#outs = []
#for stride in self._feat_stride_fpn:
# _name = 'rpn_cls_score_stride%s_output' % stride
# rpn_cls_score = all_layers[_name]
# rpn_cls_score_reshape = mx.symbol.Reshape(data=rpn_cls_score,
# shape=(0, 2, -1, 0),
# name="rpn_cls_score_reshape_stride%d" % stride)
# rpn_cls_prob = mx.symbol.SoftmaxActivation(data=rpn_cls_score_reshape,
# mode="channel",
# name="rpn_cls_prob_stride%d" % stride)
# rpn_cls_prob_reshape = mx.symbol.Reshape(data=rpn_cls_prob,
# shape=(0, 2 * num_anchors, -1, 0),
# name='rpn_cls_prob_reshape_stride%d' % stride)
# outs.append(rpn_cls_prob_reshape)
# _name = 'rpn_bbox_pred_stride%s_output' % stride
# rpn_bbox_pred = all_layers[_name]
# outs.append(rpn_bbox_pred)
#sym = mx.sym.Group(outs)
self.model = mx.mod.Module(symbol=sym, context=self.ctx, label_names = None)
image_size = (640, 640)
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)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
pass
def __init__(self, prefix, epoch, ctx_id=0, test_mode=False):
self.ctx_id = ctx_id
self.ctx = mx.gpu(self.ctx_id)
self.fpn_keys = []
fpn_stride = []
fpn_base_size = []
self._feat_stride_fpn = [32, 16, 8]
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s'%s)
fpn_stride.append(int(s))
fpn_base_size.append(16)
self._scales = np.array([32,16,8,4,2,1])
self._ratios = np.array([1.0]*len(self._feat_stride_fpn))
self._anchors_fpn = dict(zip(self.fpn_keys, generate_anchors_fpn(base_size=fpn_base_size, scales=self._scales, ratios=self._ratios)))
self._num_anchors = dict(zip(self.fpn_keys, [anchors.shape[0] for anchors in self._anchors_fpn.values()]))
self._rpn_pre_nms_top_n = 1000
#self._rpn_post_nms_top_n = rpn_post_nms_top_n
#self.score_threshold = 0.05
self.nms_threshold = 0.3
self._bbox_pred = nonlinear_pred
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
self.pixel_means = np.array([103.939, 116.779, 123.68]) #BGR
if not test_mode:
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)
else:
from rcnn.core.module import MutableModule
image_size = (2400, 2400)
data_shape = [('data', (1,3,image_size[0], image_size[1]))]
self.model = MutableModule(symbol=sym, data_names=['data'], label_names=None,
context=self.ctx, max_data_shapes=data_shape)
self.model.bind(data_shape, None, for_training=False)
self.model.set_params(arg_params, aux_params)
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(base_size=fpn_base_size, scales=self._scales, ratios=self._ratios)))
self._anchors_fpn = dict(
zip(
self.fpn_keys,
generate_anchors_fpn(dense_anchor=dense_anchor,
cfg=self.anchor_cfg)))
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)
if self.ctx_id >= 0:
self.ctx = mx.gpu(self.ctx_id)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
else:
self.ctx = mx.cpu()
self.nms = cpu_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)
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
self.preprocess = False
_ratio = (1., )
fmc = 3
if network == 'ssh' or network == 'vgg':
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)
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(dense_anchor=dense_anchor,
cfg=self.anchor_cfg)))
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()]))
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
if self.ctx_id >= 0:
self.ctx = mx.gpu(self.ctx_id)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
else:
self.ctx = mx.cpu()
self.nms = cpu_nms_wrapper(self.nms_threshold)
self.use_landmarks = False
if len(sym) // len(self._feat_stride_fpn) == 3:
self.use_landmarks = True
print('use_landmarks', self.use_landmarks)
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)
def postprocess(net_out, threshold, ctx_id, im_scale, im_info):
# im_info = [640, 640]
flip = False
decay4 = 0.5
vote = False
fpn_keys = []
anchor_cfg = None
bbox_stds = [1.0, 1.0, 1.0, 1.0]
# im_scale = 1.0
landmark_std = 1.0
nms_threshold = 0.4
proposals_list = []
scores_list = []
landmarks_list = []
strides_list = []
use_landmarks = True
if ctx_id >= 0:
nms = gpu_nms_wrapper(nms_threshold, ctx_id)
else:
nms = cpu_nms_wrapper(nms_threshold)
use_landmarks = True
_ratio = (1., )
_feat_stride_fpn = [32, 16, 8]
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
},
}
for s in _feat_stride_fpn:
fpn_keys.append('stride%s' % s)
dense_anchor = False
_anchors_fpn = dict(
zip(fpn_keys,
generate_anchors_fpn(dense_anchor=dense_anchor, cfg=anchor_cfg)))
for k in _anchors_fpn:
v = _anchors_fpn[k].astype(np.float32)
_anchors_fpn[k] = v
_num_anchors = dict(
zip(fpn_keys, [anchors.shape[0] for anchors in _anchors_fpn.values()]))
sym_idx = 0
for _idx, s in enumerate(_feat_stride_fpn):
# print(sym_idx)
_key = 'stride%s' % s
# print(_key)
stride = int(s)
scores = net_out[sym_idx] #.asnumpy()
scores = scores[:, _num_anchors['stride%s' % s]:, :, :]
bbox_deltas = net_out[sym_idx + 1] # .asnumpy()
height, width = bbox_deltas.shape[2], bbox_deltas.shape[3]
A = _num_anchors['stride%s' % s]
K = height * width
anchors_fpn = _anchors_fpn['stride%s' % s]
anchors = anchors_plane(height, width, stride, anchors_fpn)
anchors = anchors.reshape((K * A, 4))
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1))
bbox_pred_len = bbox_deltas.shape[3] // A
bbox_deltas = bbox_deltas.reshape((-1, bbox_pred_len))
bbox_deltas[:, 0::4] = bbox_deltas[:, 0::4] * bbox_stds[0]
bbox_deltas[:, 1::4] = bbox_deltas[:, 1::4] * bbox_stds[1]
bbox_deltas[:, 2::4] = bbox_deltas[:, 2::4] * bbox_stds[2]
bbox_deltas[:, 3::4] = bbox_deltas[:, 3::4] * bbox_stds[3]
proposals = bbox_pred(anchors, bbox_deltas)
proposals = clip_boxes(proposals, im_info[:2])
if stride == 4 and decay4 < 1.0:
scores *= decay4
scores_ravel = scores.ravel()
order = np.where(scores_ravel >= threshold)[0]
proposals = proposals[order, :]
scores = scores[order]
if flip:
oldx1 = proposals[:, 0].copy()
oldx2 = proposals[:, 2].copy()
proposals[:, 0] = im.shape[1] - oldx2 - 1
proposals[:, 2] = im.shape[1] - oldx1 - 1
#proposals[:,0:4] /= im_scale
#print(proposals[:,0])
proposals[:, 0] /= im_scale[0]
#print(pp)
proposals[:, 1] /= im_scale[1]
proposals[:, 2] /= im_scale[0]
proposals[:, 3] /= im_scale[1]
#print(proposals[:,0])
proposals_list.append(proposals)
scores_list.append(scores)
if nms_threshold < 0.0:
_strides = np.empty(shape=(scores.shape), dtype=np.float32)
_strides.fill(stride)
strides_list.append(_strides)
if not vote and use_landmarks:
landmark_deltas = net_out[sym_idx + 2] #.asnumpy()
# print(landmark_deltas)
landmark_pred_len = landmark_deltas.shape[1] // A
landmark_deltas = landmark_deltas.transpose((0, 2, 3, 1)).reshape(
(-1, 5, landmark_pred_len // 5))
landmark_deltas *= landmark_std
landmarks = landmark_pred(anchors, landmark_deltas)
landmarks = landmarks[order, :]
if flip:
landmarks[:, :, 0] = im.shape[1] - landmarks[:, :, 0] - 1
order = [1, 0, 2, 4, 3]
flandmarks = landmarks.copy()
for idx, a in enumerate(order):
flandmarks[:, idx, :] = landmarks[:, a, :]
landmarks = flandmarks
landmarks[:, :, 0:2] /= im_scale
landmarks_list.append(landmarks)
if use_landmarks:
sym_idx += 3
else:
sym_idx += 2
proposals = np.vstack(proposals_list)
landmarks = None
if proposals.shape[0] == 0:
if use_landmarks:
landmarks = np.zeros((0, 5, 2))
if nms_threshold < 0.0:
return np.zeros((0, 6)), landmarks
else:
return np.zeros((0, 5)), landmarks
scores = np.vstack(scores_list)
scores_ravel = scores.ravel()
order = scores_ravel.argsort()[::-1]
proposals = proposals[order, :]
scores = scores[order]
if nms_threshold < 0.0:
strides = np.vstack(strides_list)
strides = strides[order]
if not vote and use_landmarks:
landmarks = np.vstack(landmarks_list)
landmarks = landmarks[order].astype(np.float32, copy=False)
if nms_threshold > 0.0:
pre_det = np.hstack((proposals[:, 0:4], scores)).astype(np.float32,
copy=False)
if not vote:
keep = nms(pre_det)
det = np.hstack((pre_det, proposals[:, 4:]))
det = det[keep, :]
if use_landmarks:
landmarks = landmarks[keep]
else:
det = np.hstack((pre_det, proposals[:, 4:]))
det = bbox_vote(det, nms_threshold)
elif nms_threshold < 0.0:
det = np.hstack((proposals[:,
0:4], scores, strides)).astype(np.float32,
copy=False)
else:
det = np.hstack((proposals[:, 0:4], scores)).astype(np.float32,
copy=False)
return det, landmarks
def __init__(self,
network,
gpu_id=0,
nms=0.3,
nocrop=False,
decay4=0.5,
vote=False):
self.network = network
self.gpu_id = gpu_id
self.decay4 = decay4
self.nms_threshold = nms
self.vote = vote
self.nocrop = nocrop
self.debug = False
self.fpn_keys = []
self.anchor_cfg = None
self.use_landmarks = True
self.sess = None
self.net = None
# pixel_means=[0.0, 0.0, 0.0]
pixel_means = config.PIXEL_MEANS
pixel_stds = [1.0, 1.0, 1.0]
pixel_scale = 1.0
self.preprocess = False
dense_anchor = False
_ratio = (1., )
image_size = (640, 640)
self._feat_stride_fpn = config.RPN_FEAT_STRIDE
self.anchor_cfg = config.RPN_ANCHOR_CFG
for s in self._feat_stride_fpn:
self.fpn_keys.append('stride%s' % s)
self._anchors_fpn = dict(
zip(
self.fpn_keys,
generate_anchors_fpn(dense_anchor=dense_anchor,
cfg=self.anchor_cfg)))
for k in self._anchors_fpn:
v = self._anchors_fpn[k].astype(np.float32)
self._anchors_fpn[k] = v
'''
{'stride32': array([[-248., -248., 263., 263.],[-120., -120., 135., 135.]], dtype=float32),
'stride16': array([[-56., -56., 71., 71.],[-24., -24., 39., 39.]], dtype=float32),
'stride8': array([[-8., -8., 23., 23.],[ 0., 0., 15., 15.]], dtype=float32)}
'''
# print('Retinaface: self.anchor_fpn=', self._anchors_fpn)
self._num_anchors = dict(
zip(self.fpn_keys,
[anchors.shape[0] for anchors in self._anchors_fpn.values()]))
#{'stride32': 2, 'stride16': 2, 'stride8': 2}
# print('Retinaface: self._num_anchors=', self._num_anchors)
if self.gpu_id >= 0:
self.nms = gpu_nms_wrapper(self.nms_threshold, self.gpu_id)
else:
self.nms = cpu_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)
self.build_net()
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 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
},
}
if self.debug:
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(dense_anchor=dense_anchor,
cfg=self.anchor_cfg)))
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()]))
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
if self.ctx_id >= 0:
self.ctx = mx.gpu(self.ctx_id)
self.nms = gpu_nms_wrapper(self.nms_threshold, self.ctx_id)
else:
self.ctx = mx.cpu()
self.nms = cpu_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)
if self.debug:
print('means', self.pixel_means)
self.use_landmarks = False
if len(sym) // len(self._feat_stride_fpn) == 3:
self.use_landmarks = True
if self.debug:
print('use_landmarks', self.use_landmarks)
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)