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# Copyright (c) Meta Platforms, Inc. and affiliates
from torchvision import models
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
import torch.nn.functional as F
from detectron2.modeling.backbone.fpn import FPN
class MNASNetBackbone(Backbone):
def __init__(self, cfg, input_shape, pretrained=True):
super().__init__()
base = models.mnasnet1_0(pretrained)
base = base.layers
self.base = base
self._out_feature_channels = {'p2': 24, 'p3': 40, 'p4': 96, 'p5': 320, 'p6': 320}
self._out_feature_strides ={'p2': 4, 'p3': 8, 'p4': 16, 'p5': 32, 'p6': 64}
self._out_features = ['p2', 'p3', 'p4', 'p5', 'p6']
def forward(self, x):
outputs = {}
p2 = self.base[0:9](x)
p3 = self.base[9](p2)
p4 = self.base[10:12](p3)
p5 = self.base[12:14](p4)
p6 = F.max_pool2d(p5, kernel_size=1, stride=2, padding=0)
outputs['p2'] = p2
outputs['p3'] = p3
outputs['p4'] = p4
outputs['p5'] = p5
outputs['p6'] = p6
return outputs
@BACKBONE_REGISTRY.register()
def build_mnasnet_fpn_backbone(cfg, input_shape: ShapeSpec, priors=None):
"""
Args:
cfg: a detectron2 CfgNode
Returns:
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
"""
imagenet_pretrain = cfg.MODEL.WEIGHTS_PRETRAIN + cfg.MODEL.WEIGHTS == ''
bottom_up = MNASNetBackbone(cfg, input_shape, pretrained=imagenet_pretrain)
in_features = cfg.MODEL.FPN.IN_FEATURES
out_channels = cfg.MODEL.FPN.OUT_CHANNELS
backbone = FPN(
bottom_up=bottom_up,
in_features=in_features,
out_channels=out_channels,
norm=cfg.MODEL.FPN.NORM,
fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
)
return backbone
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