forked from mrq/DL-Art-School
65 lines
3.5 KiB
Python
65 lines
3.5 KiB
Python
|
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),
|