Source code for torchvision.ops.feature_pyramid_network
from collections import OrderedDict
from typing import Tuple, List, Dict, Callable, Optional
import torch.nn.functional as F
from torch import nn, Tensor
from ..ops.misc import Conv2dNormActivation
from ..utils import _log_api_usage_once
class ExtraFPNBlock(nn.Module):
"""
Base class for the extra block in the FPN.
Args:
results (List[Tensor]): the result of the FPN
x (List[Tensor]): the original feature maps
names (List[str]): the names for each one of the
original feature maps
Returns:
results (List[Tensor]): the extended set of results
of the FPN
names (List[str]): the extended set of names for the results
"""
def forward(
self,
results: List[Tensor],
x: List[Tensor],
names: List[str],
) -> Tuple[List[Tensor], List[str]]:
pass
[docs]class FeaturePyramidNetwork(nn.Module):
"""
Module that adds a FPN from on top of a set of feature maps. This is based on
`"Feature Pyramid Network for Object Detection" <https://arxiv.org/abs/1612.03144>`_.
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.
Args:
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]))]
"""
_version = 2
def __init__(
self,
in_channels_list: List[int],
out_channels: int,
extra_blocks: Optional[ExtraFPNBlock] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None,
):
super().__init__()
_log_api_usage_once(self)
self.inner_blocks = nn.ModuleList()
self.layer_blocks = nn.ModuleList()
for in_channels in in_channels_list:
if in_channels == 0:
raise ValueError("in_channels=0 is currently not supported")
inner_block_module = Conv2dNormActivation(
in_channels, out_channels, kernel_size=1, padding=0, norm_layer=norm_layer, activation_layer=None
)
layer_block_module = Conv2dNormActivation(
out_channels, out_channels, kernel_size=3, norm_layer=norm_layer, activation_layer=None
)
self.inner_blocks.append(inner_block_module)
self.layer_blocks.append(layer_block_module)
# initialize parameters now to avoid modifying the initialization of top_blocks
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if extra_blocks is not None:
if not isinstance(extra_blocks, ExtraFPNBlock):
raise TypeError(f"extra_blocks should be of type ExtraFPNBlock not {type(extra_blocks)}")
self.extra_blocks = extra_blocks
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
version = local_metadata.get("version", None)
if version is None or version < 2:
num_blocks = len(self.inner_blocks)
for block in ["inner_blocks", "layer_blocks"]:
for i in range(num_blocks):
for type in ["weight", "bias"]:
old_key = f"{prefix}{block}.{i}.{type}"
new_key = f"{prefix}{block}.{i}.0.{type}"
if old_key in state_dict:
state_dict[new_key] = state_dict.pop(old_key)
super()._load_from_state_dict(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
)
[docs] def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor:
"""
This is equivalent to self.inner_blocks[idx](x),
but torchscript doesn't support this yet
"""
num_blocks = len(self.inner_blocks)
if idx < 0:
idx += num_blocks
out = x
for i, module in enumerate(self.inner_blocks):
if i == idx:
out = module(x)
return out
[docs] def get_result_from_layer_blocks(self, x: Tensor, idx: int) -> Tensor:
"""
This is equivalent to self.layer_blocks[idx](x),
but torchscript doesn't support this yet
"""
num_blocks = len(self.layer_blocks)
if idx < 0:
idx += num_blocks
out = x
for i, module in enumerate(self.layer_blocks):
if i == idx:
out = module(x)
return out
[docs] def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]:
"""
Computes the FPN for a set of feature maps.
Args:
x (OrderedDict[Tensor]): feature maps for each feature level.
Returns:
results (OrderedDict[Tensor]): feature maps after FPN layers.
They are ordered from highest resolution first.
"""
# unpack OrderedDict into two lists for easier handling
names = list(x.keys())
x = list(x.values())
last_inner = self.get_result_from_inner_blocks(x[-1], -1)
results = []
results.append(self.get_result_from_layer_blocks(last_inner, -1))
for idx in range(len(x) - 2, -1, -1):
inner_lateral = self.get_result_from_inner_blocks(x[idx], idx)
feat_shape = inner_lateral.shape[-2:]
inner_top_down = F.interpolate(last_inner, size=feat_shape, mode="nearest")
last_inner = inner_lateral + inner_top_down
results.insert(0, self.get_result_from_layer_blocks(last_inner, idx))
if self.extra_blocks is not None:
results, names = self.extra_blocks(results, x, names)
# make it back an OrderedDict
out = OrderedDict([(k, v) for k, v in zip(names, results)])
return out
class LastLevelMaxPool(ExtraFPNBlock):
"""
Applies a max_pool2d on top of the last feature map
"""
def forward(
self,
x: List[Tensor],
y: List[Tensor],
names: List[str],
) -> Tuple[List[Tensor], List[str]]:
names.append("pool")
x.append(F.max_pool2d(x[-1], 1, 2, 0))
return x, names
class LastLevelP6P7(ExtraFPNBlock):
"""
This module is used in RetinaNet to generate extra layers, P6 and P7.
"""
def __init__(self, in_channels: int, out_channels: int):
super().__init__()
self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
for module in [self.p6, self.p7]:
nn.init.kaiming_uniform_(module.weight, a=1)
nn.init.constant_(module.bias, 0)
self.use_P5 = in_channels == out_channels
def forward(
self,
p: List[Tensor],
c: List[Tensor],
names: List[str],
) -> Tuple[List[Tensor], List[str]]:
p5, c5 = p[-1], c[-1]
x = p5 if self.use_P5 else c5
p6 = self.p6(x)
p7 = self.p7(F.relu(p6))
p.extend([p6, p7])
names.extend(["p6", "p7"])
return p, names
