forked from mrq/DL-Art-School
98 lines
5.2 KiB
Python
98 lines
5.2 KiB
Python
import torch
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from torch import nn
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from models.archs.SPSR_arch import ImageGradientNoPadding
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from models.archs.arch_util import ConvGnLelu, ExpansionBlock2, ConvGnSilu, ConjoinBlock, MultiConvBlock, \
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FinalUpsampleBlock2x
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from models.archs.spinenet_arch import SpineNet
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from utils.util import checkpoint
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class BasicEmbeddingPyramid(nn.Module):
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def __init__(self, use_norms=True):
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super(BasicEmbeddingPyramid, self).__init__()
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self.initial_process = ConvGnLelu(64, 64, kernel_size=1, bias=True, activation=True, norm=False)
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self.reducers = nn.ModuleList([ConvGnLelu(64, 128, stride=2, kernel_size=1, bias=False, activation=True, norm=False),
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ConvGnLelu(128, 128, kernel_size=3, bias=False, activation=True, norm=use_norms),
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ConvGnLelu(128, 256, stride=2, kernel_size=1, bias=False, activation=True, norm=False),
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ConvGnLelu(256, 256, kernel_size=3, bias=False, activation=True, norm=use_norms)])
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self.expanders = nn.ModuleList([ExpansionBlock2(256, 128, block=ConvGnLelu),
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ExpansionBlock2(128, 64, block=ConvGnLelu)])
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self.embedding_processor1 = ConvGnSilu(256, 128, kernel_size=1, bias=True, activation=True, norm=False)
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self.embedding_joiner1 = ConjoinBlock(128, block=ConvGnLelu, norm=use_norms)
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self.embedding_processor2 = ConvGnSilu(256, 256, kernel_size=1, bias=True, activation=True, norm=False)
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self.embedding_joiner2 = ConjoinBlock(256, block=ConvGnLelu, norm=use_norms)
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self.final_process = nn.Sequential(ConvGnLelu(128, 96, kernel_size=1, bias=False, activation=False, norm=False,
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weight_init_factor=.1),
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ConvGnLelu(96, 64, kernel_size=1, bias=False, activation=False, norm=False,
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weight_init_factor=.1),
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ConvGnLelu(64, 64, kernel_size=1, bias=False, activation=False, norm=False,
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weight_init_factor=.1),
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ConvGnLelu(64, 64, kernel_size=1, bias=False, activation=False, norm=False,
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weight_init_factor=.1))
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def forward(self, x, *embeddings):
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p = self.initial_process(x)
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identities = []
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for i in range(2):
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identities.append(p)
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p = self.reducers[i*2](p)
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p = self.reducers[i*2+1](p)
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if i == 0:
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p = self.embedding_joiner1(p, self.embedding_processor1(embeddings[0]))
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elif i == 1:
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p = self.embedding_joiner2(p, self.embedding_processor2(embeddings[1]))
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for i in range(2):
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p = self.expanders[i](p, identities[-(i+1)])
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x = self.final_process(torch.cat([x, p], dim=1))
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return x
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class ChainedEmbeddingGen(nn.Module):
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def __init__(self, depth=10):
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super(ChainedEmbeddingGen, self).__init__()
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
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self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
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self.upsample = FinalUpsampleBlock2x(64)
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def forward(self, x):
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emb = checkpoint(self.spine, x)
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fea = self.initial_conv(x)
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for block in self.blocks:
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fea = fea + checkpoint(block, fea, *emb)
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return checkpoint(self.upsample, fea),
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class ChainedEmbeddingGenWithStructure(nn.Module):
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def __init__(self, depth=10, recurrent=False):
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super(ChainedEmbeddingGenWithStructure, self).__init__()
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self.recurrent = recurrent
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if recurrent:
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self.initial_conv_rec = ConvGnLelu(6, 64, kernel_size=7, bias=True, norm=False, activation=False)
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else:
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
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self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
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self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)])
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self.structure_blocks = nn.ModuleList([ConvGnLelu(64, 64, kernel_size=3, bias=False, norm=False, activation=False, weight_init_factor=.1) for i in range(3)])
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self.structure_upsample = FinalUpsampleBlock2x(64)
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self.grad_extract = ImageGradientNoPadding()
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self.upsample = FinalUpsampleBlock2x(64)
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def forward(self, x):
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emb = checkpoint(self.spine, x)
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if self.recurrent:
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fea = self.initial_conv_rec(x)
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else:
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fea = self.initial_conv(x)
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grad = fea
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for i, block in enumerate(self.blocks):
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fea = fea + checkpoint(block, fea, *emb)
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if i < 3:
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structure_br = checkpoint(self.structure_joins[i], grad, fea)
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grad = grad + checkpoint(self.structure_blocks[i], structure_br)
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out = checkpoint(self.upsample, fea)
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return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out)
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