# Copyright (c) Facebook, Inc. and its affiliates. """ MIT License Copyright (c) 2019 Microsoft Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import torch import torch.nn as nn import torch.nn.functional as F from detectron2.layers import ShapeSpec from detectron2.modeling.backbone import BACKBONE_REGISTRY from detectron2.modeling.backbone.backbone import Backbone from .hrnet import build_pose_hrnet_backbone class HRFPN(Backbone): """HRFPN (High Resolution Feature Pyramids) Transforms outputs of HRNet backbone so they are suitable for the ROI_heads arXiv: https://arxiv.org/abs/1904.04514 Adapted from https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/hrfpn.py Args: bottom_up: (list) output of HRNet in_features (list): names of the input features (output of HRNet) in_channels (list): number of channels for each branch out_channels (int): output channels of feature pyramids n_out_features (int): number of output stages pooling (str): pooling for generating feature pyramids (from {MAX, AVG}) share_conv (bool): Have one conv per output, or share one with all the outputs """ def __init__( self, bottom_up, in_features, n_out_features, in_channels, out_channels, pooling="AVG", share_conv=False, ): super(HRFPN, self).__init__() assert isinstance(in_channels, list) self.bottom_up = bottom_up self.in_features = in_features self.n_out_features = n_out_features self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.share_conv = share_conv if self.share_conv: self.fpn_conv = nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1 ) else: self.fpn_conv = nn.ModuleList() for _ in range(self.n_out_features): self.fpn_conv.append( nn.Conv2d( in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1, ) ) # Custom change: Replaces a simple bilinear interpolation self.interp_conv = nn.ModuleList() for i in range(len(self.in_features)): self.interp_conv.append( nn.Sequential( nn.ConvTranspose2d( in_channels=in_channels[i], out_channels=in_channels[i], kernel_size=4, stride=2**i, padding=0, output_padding=0, bias=False, ), nn.BatchNorm2d(in_channels[i], momentum=0.1), nn.ReLU(inplace=True), ) ) # Custom change: Replaces a couple (reduction conv + pooling) by one conv self.reduction_pooling_conv = nn.ModuleList() for i in range(self.n_out_features): self.reduction_pooling_conv.append( nn.Sequential( nn.Conv2d(sum(in_channels), out_channels, kernel_size=2**i, stride=2**i), nn.BatchNorm2d(out_channels, momentum=0.1), nn.ReLU(inplace=True), ) ) if pooling == "MAX": self.pooling = F.max_pool2d else: self.pooling = F.avg_pool2d self._out_features = [] self._out_feature_channels = {} self._out_feature_strides = {} for i in range(self.n_out_features): self._out_features.append("p%d" % (i + 1)) self._out_feature_channels.update({self._out_features[-1]: self.out_channels}) self._out_feature_strides.update({self._out_features[-1]: 2 ** (i + 2)}) # default init_weights for conv(msra) and norm in ConvModule def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, a=1) nn.init.constant_(m.bias, 0) def forward(self, inputs): bottom_up_features = self.bottom_up(inputs) assert len(bottom_up_features) == len(self.in_features) inputs = [bottom_up_features[f] for f in self.in_features] outs = [] for i in range(len(inputs)): outs.append(self.interp_conv[i](inputs[i])) shape_2 = min(o.shape[2] for o in outs) shape_3 = min(o.shape[3] for o in outs) out = torch.cat([o[:, :, :shape_2, :shape_3] for o in outs], dim=1) outs = [] for i in range(self.n_out_features): outs.append(self.reduction_pooling_conv[i](out)) for i in range(len(outs)): # Make shapes consistent outs[-1 - i] = outs[-1 - i][ :, :, : outs[-1].shape[2] * 2**i, : outs[-1].shape[3] * 2**i ] outputs = [] for i in range(len(outs)): if self.share_conv: outputs.append(self.fpn_conv(outs[i])) else: outputs.append(self.fpn_conv[i](outs[i])) assert len(self._out_features) == len(outputs) return dict(zip(self._out_features, outputs)) @BACKBONE_REGISTRY.register() def build_hrfpn_backbone(cfg, input_shape: ShapeSpec) -> HRFPN: in_channels = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS in_features = ["p%d" % (i + 1) for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES)] n_out_features = len(cfg.MODEL.ROI_HEADS.IN_FEATURES) out_channels = cfg.MODEL.HRNET.HRFPN.OUT_CHANNELS hrnet = build_pose_hrnet_backbone(cfg, input_shape) hrfpn = HRFPN( hrnet, in_features, n_out_features, in_channels, out_channels, pooling="AVG", share_conv=False, ) return hrfpn