YOLO-Tutorial-v2/odlab/models/neck/fpn.py

import torch.nn as nn
import torch.nn.functional as F

from utils import weight_init


# ------------------ Basic Feature Pyramid Network ------------------
class BasicFPN(nn.Module):
    def __init__(self, 
                 in_dims=[512, 1024, 2048],
                 out_dim=256,
                 p6_feat=False,
                 p7_feat=False,
                 from_c5=False,
                 ):
        super().__init__()
        # ------------------ Basic parameters -------------------
        self.p6_feat = p6_feat
        self.p7_feat = p7_feat
        self.from_c5 = from_c5

        # ------------------ Network parameters -------------------
        ## latter layers
        self.input_projs = nn.ModuleList()
        self.smooth_layers = nn.ModuleList()
        for in_dim in in_dims[::-1]:
            self.input_projs.append(nn.Conv2d(in_dim, out_dim, kernel_size=1))
            self.smooth_layers.append(nn.Conv2d(out_dim, out_dim, kernel_size=3, padding=1))

        ## P6/P7 layers
        if p6_feat:
            if from_c5:
                self.p6_conv = nn.Conv2d(in_dims[-1], out_dim, kernel_size=3, stride=2, padding=1)
            else: # from p5
                self.p6_conv = nn.Conv2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1)
        if p7_feat:
            self.p7_conv = nn.Sequential(
                nn.ReLU(inplace=True),
                nn.Conv2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1)
            )

        self._init_weight()

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                weight_init.c2_xavier_fill(m)

    def forward(self, feats):
        """
            feats: (List of Tensor) [C3, C4, C5], C_i ∈ R^(B x C_i x H_i x W_i)
        """
        outputs = []
        # [C3, C4, C5] -> [C5, C4, C3]
        feats = feats[::-1]
        top_level_feat = feats[0]
        prev_feat = self.input_projs[0](top_level_feat)
        outputs.append(self.smooth_layers[0](prev_feat))

        for feat, input_proj, smooth_layer in zip(feats[1:], self.input_projs[1:], self.smooth_layers[1:]):
            feat = input_proj(feat)
            top_down_feat = F.interpolate(prev_feat, size=feat.shape[2:], mode='nearest')
            prev_feat = feat + top_down_feat
            outputs.insert(0, smooth_layer(prev_feat))

        if self.p6_feat:
            if self.from_c5:
                p6_feat = self.p6_conv(feats[0])
            else:
                p6_feat = self.p6_conv(outputs[-1])
            # [P3, P4, P5] -> [P3, P4, P5, P6]
            outputs.append(p6_feat)

            if self.p7_feat:
                p7_feat = self.p7_conv(p6_feat)
                # [P3, P4, P5, P6] -> [P3, P4, P5, P6, P7]
                outputs.append(p7_feat)

        # [P3, P4, P5] or [P3, P4, P5, P6, P7]
        return outputs