def get_mlvl_centers(locations, strides):
mlvl_centers = []
for location, stride in zip(locations, strides):
lx, ly = _tuple(location, 2)
sw, sh = _tuple(stride, 2)
centers = torch.zeros(lx, ly, 2)
centers[..., 0] = (
torch.arange(lx, dtype=torch.float).view(lx, 1).expand(lx, ly) * sw
+ sw // 2)
centers[..., 1] = (
torch.arange(ly, dtype=torch.float).view(1, ly).expand(lx, ly) * sh
+ sh // 2)
mlvl_centers.append(centers)
return mlvl_centers
def inference(self, *xs):
self.eval()
with torch.no_grad():
xs = self.forward(*xs)
preds = self._inference(*_tuple(xs))
self.train()
return preds
def inference(self, x):
self.eval()
with torch.no_grad():
preds = self.forward(x)
dets = self._inference(*_tuple(preds))
self.train()
return dets
def split_levels(levels, split_at=5):
levels = _tuple(levels)
lo = levels[0]
hi = levels[-1]
assert levels == tuple(range(lo, hi + 1))
basic_levels = tuple(range(lo, min(hi, split_at) + 1))
extra_levels = tuple(range(max(lo, split_at + 1), hi + 1))
return basic_levels, extra_levels
def region_proposal(self, x):
cs = self.backbone(x)
ps = self.fpn(*cs)
ps = _tuple(ps)
loc_p, cls_p = self.head(*ps)
rois = self._inference(detach(loc_p), detach(cls_p))
if self.training and self._e2e:
return ps, rois, loc_p, cls_p
else:
return ps, rois
def get_mlvl_centers(locations):
mlvl_centers = []
for location in locations:
lx, ly = _tuple(location, 2)
centers = torch.zeros(lx, ly, 2)
centers[...,
0] = torch.arange(lx, dtype=torch.float).view(lx, 1).expand(
lx, ly) + 0.5
centers[...,
1] = torch.arange(ly, dtype=torch.float).view(1, ly).expand(
lx, ly) + 0.5
mlvl_centers.append(centers)
return mlvl_centers
def __init__(self,
backbone,
num_anchors,
num_classes,
f_channels,
inference,
extra_levels=(6, )):
super().__init__()
self.num_classes = num_classes
self.backbone = backbone
self._inference = inference
stages = backbone.out_channels
self.extra_levels = _tuple(extra_levels)
self.extra_layers = nn.ModuleList([])
for l in self.extra_levels:
self.extra_layers.append(
Bottleneck(stages[-1], f_channels, stride=2))
stages.append(f_channels)
self.r_head = SSDHead(num_anchors,
1,
stages,
norm_layer='default',
lite=True)
self.tcbs = nn.ModuleList(
[TransferConnection(stages[-1], f_channels, last=True)])
for c in reversed(stages[:-1]):
self.tcbs.append(
TransferConnection(c, f_channels, norm_layer='default'))
self.d_head = SSDHead(num_anchors,
num_classes,
_tuple(f_channels, 3),
norm_layer='default',
lite=True)
def __init__(self,
backbone,
num_anchors=(4, 6, 6, 6, 6, 4),
num_classes=21,
f_channels=256,
extra_levels=(6, 7, 8),
pad_last=False,
lite=False):
super().__init__()
extra_levels = _tuple(extra_levels)
feature_levels = backbone.feature_levels + extra_levels
num_anchors = _tuple(num_anchors, len(feature_levels))
self.num_classes = num_classes
self.backbone = backbone
self.feature_levels = feature_levels
self.num_anchors = num_anchors
backbone_channels = backbone.out_channels
head_in_channels = list(backbone_channels)
if 6 in extra_levels:
self.layer6 = DownBlock(backbone_channels[-1],
2 * f_channels,
lite=lite)
head_in_channels.append(2 * f_channels)
if 7 in extra_levels:
self.layer7 = DownBlock(2 * f_channels, f_channels, lite=lite)
head_in_channels.append(f_channels)
if 8 in extra_levels:
padding = 1 if pad_last else 0
self.layer8 = DownBlock(f_channels,
f_channels,
padding=padding,
lite=lite)
head_in_channels.append(f_channels)
self.head = SSDHead(num_anchors,
num_classes,
head_in_channels,
lite=lite)
def __init__(self, num_anchors, num_classes, in_channels, lite=False, concat=True):
super().__init__()
self.num_classes = num_classes
self.concat = concat
num_anchors = _tuple(num_anchors, len(in_channels))
self.preds = nn.ModuleList([
nn.Sequential(
get_norm_layer('default', c),
Conv2d(c, n * (num_classes + 4), kernel_size=3, depthwise_separable=lite, mid_norm_layer='default')
)
for c, n in zip(in_channels, num_anchors)
])
for p in self.preds:
get_last_conv(p).bias.data[4:].fill_(inverse_sigmoid(0.01))
def center_crop(anns, size, output_size):
r"""
Crops the bounding boxes of the given PIL Image at the center.
Parameters
----------
anns : ``List[Dict]``
Sequences of annotation of objects, containing `bbox` of [l, t, w, h].
size : ``Sequence[int]``
Size of the original image.
output_size : ``Union[Number, Sequence[int]]``
Desired output size of the crop. If size is an int instead of sequence like (w, h),
a square crop (size, size) is made.
"""
output_size = _tuple(output_size, 2)
output_size = tuple(int(x) for x in output_size)
w, h = size
th, tw = output_size
upper = int(round((h - th) / 2.))
left = int(round((w - tw) / 2.))
return crop(anns, left, upper, th, tw)
def __init__(self,
backbone,
num_classes=10,
num_anchors=3,
f_channels=256,
extra_levels=(6, 7),
num_head_layers=4):
super().__init__()
assert tuple(backbone.feature_levels) == (
3, 4, 5), "Feature levels of backbone must be (3,4,5)"
self.num_classes = num_classes
self.backbone = backbone
backbone_channels = backbone.out_channels
self.extra_levels = _tuple(extra_levels)
if 6 in extra_levels:
self.layer6 = Conv2d(backbone_channels[-1],
f_channels,
kernel_size=3,
stride=2,
norm_layer='default')
if 7 in extra_levels:
assert 6 in extra_levels
self.layer7 = nn.Sequential(
get_activation('default'),
Conv2d(f_channels,
f_channels,
kernel_size=3,
stride=2,
norm_layer='default'))
self.fpn = FPN(backbone, f_channels)
self.head = RetinaHead(f_channels,
num_anchors,
num_classes,
num_layers=num_head_layers)
def forward(self, *xs):
for module in self._modules.values():
xs = module(*_tuple(xs))
return xs
