FeaturePyramidNetwork — Torchvision main documentation

Module that adds a FPN from on top of a set of feature maps. This is based on
“Feature Pyramid Network for Object Detection”.

The feature maps are currently supposed to be in increasing depth
order.

The input to the model is expected to be an OrderedDict[Tensor], containing
the feature maps on top of which the FPN will be added.

Parameters :

in_channels_list (list[int]) – number of channels for each feature map that
is passed to the module
out_channels (int) – number of channels of the FPN representation
extra_blocks (ExtraFPNBlock or None) – if provided, extra operations will
be performed. It is expected to take the fpn features, the original
features and the names of the original features as input, and returns
a new list of feature maps and their corresponding names
norm_layer (callable, optional) – Module specifying the normalization layer to use. Default: None

Examples:


>>> 
m
 =
 torchvision
.
ops
.
FeaturePyramidNetwork
([
10
,
 20
,
 30
],
 5
)
>>> 
# get some dummy data
>>> 
x
 =
 OrderedDict
()
>>> 
x
[
'feat0'
]
 =
 torch
.
rand
(
1
,
 10
,
 64
,
 64
)
>>> 
x
[
'feat2'
]
 =
 torch
.
rand
(
1
,
 20
,
 16
,
 16
)
>>> 
x
[
'feat3'
]
 =
 torch
.
rand
(
1
,
 30
,
 8
,
 8
)
>>> 
# compute the FPN on top of x
>>> 
output
 =
 m
(
x
)
>>> 
print
([(
k
,
 v
.
shape
)
 for
 k
,
 v
 in
 output
.
items
()])
>>> 
# returns
>>> 
  [(
'feat0'
,
 torch
.
Size
([
1
,
 5
,
 64
,
 64
])),
>>> 
   (
'feat2'
,
 torch
.
Size
([
1
,
 5
,
 16
,
 16
])),
>>> 
   (
'feat3'
,
 torch
.
Size
([
1
,
 5
,
 8
,
 8
]))]

forward ( x : Dict [ str , Tensor ] ) → Dict [ str , Tensor ] [source] ¶

Computes the FPN for a set of feature maps.

Parameters :

x (OrderedDict[Tensor]) – feature maps for each feature level.

Returns :

feature maps after FPN layers.: They are ordered from the highest resolution first.

Return type :

results (OrderedDict[Tensor])

get_result_from_inner_blocks ( x : Tensor idx : int ) → Tensor [source] ¶: This is equivalent to self.inner_blocks[idx](x),
but torchscript doesn’t support this yet

get_result_from_layer_blocks ( x : Tensor idx : int ) → Tensor [source] ¶: This is equivalent to self.layer_blocks[idx](x),
but torchscript doesn’t support this yet