import torch from torch import nn from models.archs.arch_util import ConvGnLelu, ExpansionBlock2, ConvGnSilu, ConjoinBlock, MultiConvBlock, \ FinalUpsampleBlock2x from models.archs.spinenet_arch import SpineNet from utils.util import checkpoint class BasicEmbeddingPyramid(nn.Module): def __init__(self, use_norms=True): super(BasicEmbeddingPyramid, self).__init__() self.initial_process = ConvGnLelu(64, 64, kernel_size=1, bias=True, activation=True, norm=False) self.reducers = nn.ModuleList([ConvGnLelu(64, 128, stride=2, kernel_size=1, bias=False, activation=True, norm=False), ConvGnLelu(128, 128, kernel_size=3, bias=False, activation=True, norm=use_norms), ConvGnLelu(128, 256, stride=2, kernel_size=1, bias=False, activation=True, norm=False), ConvGnLelu(256, 256, kernel_size=3, bias=False, activation=True, norm=use_norms)]) self.expanders = nn.ModuleList([ExpansionBlock2(256, 128, block=ConvGnLelu), ExpansionBlock2(128, 64, block=ConvGnLelu)]) self.embedding_processor1 = ConvGnSilu(256, 128, kernel_size=1, bias=True, activation=True, norm=False) self.embedding_joiner1 = ConjoinBlock(128, block=ConvGnLelu, norm=use_norms) self.embedding_processor2 = ConvGnSilu(256, 256, kernel_size=1, bias=True, activation=True, norm=False) self.embedding_joiner2 = ConjoinBlock(256, block=ConvGnLelu, norm=use_norms) self.final_process = nn.Sequential(ConvGnLelu(128, 96, kernel_size=1, bias=False, activation=False, norm=False, weight_init_factor=.1), ConvGnLelu(96, 64, kernel_size=1, bias=False, activation=False, norm=False, weight_init_factor=.1), ConvGnLelu(64, 64, kernel_size=1, bias=False, activation=False, norm=False, weight_init_factor=.1), ConvGnLelu(64, 64, kernel_size=1, bias=False, activation=False, norm=False, weight_init_factor=.1)) def forward(self, x, *embeddings): p = self.initial_process(x) identities = [] for i in range(2): identities.append(p) p = self.reducers[i*2](p) p = self.reducers[i*2+1](p) if i == 0: p = self.embedding_joiner1(p, self.embedding_processor1(embeddings[0])) elif i == 1: p = self.embedding_joiner2(p, self.embedding_processor2(embeddings[1])) for i in range(2): p = self.expanders[i](p, identities[-(i+1)]) x = self.final_process(torch.cat([x, p], dim=1)) return x class ChainedEmbeddingGen(nn.Module): def __init__(self): super(ChainedEmbeddingGen, self).__init__() self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False) self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False) self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(5)]) self.upsample = FinalUpsampleBlock2x(64) def forward(self, x): emb = checkpoint(self.spine, x) fea = self.initial_conv(x) for block in self.blocks: fea = fea + checkpoint(block, fea, *emb) return checkpoint(self.upsample, fea),