197 lines
11 KiB
Python
197 lines
11 KiB
Python
import os
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import torch
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import torchvision
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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, ReferenceJoinBlock
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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, p
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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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fea = self.initial_conv(x)
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emb = checkpoint(self.spine, fea)
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for block in self.blocks:
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fea = fea + checkpoint(block, fea, *emb)[0]
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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, recurrent_nf=3, recurrent_stride=2):
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super(ChainedEmbeddingGenWithStructure, self).__init__()
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self.recurrent = recurrent
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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if recurrent:
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self.recurrent_nf = recurrent_nf
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self.recurrent_stride = recurrent_stride
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self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=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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self.ref_join_std = 0
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def forward(self, x, recurrent=None):
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fea = self.initial_conv(x)
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if self.recurrent:
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if recurrent is None:
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if self.recurrent_nf == 3:
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recurrent = torch.zeros_like(x)
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if self.recurrent_stride != 1:
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recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest')
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else:
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recurrent = torch.zeros_like(fea)
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rec = self.recurrent_process(recurrent)
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fea, recstd = self.recurrent_join(fea, rec)
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self.ref_join_std = recstd.item()
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emb = checkpoint(self.spine, fea)
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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)[0]
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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), fea
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def get_debug_values(self, step, net_name):
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return { 'ref_join_std': self.ref_join_std }
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# This is a structural block that learns to mute regions of a residual transformation given a signal.
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class OptionalPassthroughBlock(nn.Module):
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def __init__(self, nf, initial_bias=10):
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super(OptionalPassthroughBlock, self).__init__()
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self.switch_process = nn.Sequential(ConvGnLelu(nf, nf // 2, 1, activation=False, norm=False, bias=False),
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ConvGnLelu(nf // 2, nf // 4, 1, activation=False, norm=False, bias=False),
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ConvGnLelu(nf // 4, 1, 1, activation=False, norm=False, bias=False))
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self.bias = nn.Parameter(torch.tensor(initial_bias, dtype=torch.float), requires_grad=True)
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self.activation = nn.Sigmoid()
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def forward(self, x, switch_signal):
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switch = self.switch_process(switch_signal)
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bypass_map = self.activation(self.bias + switch)
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return x * bypass_map, bypass_map
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class StructuredChainedEmbeddingGenWithBypass(nn.Module):
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def __init__(self, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2, bypass_bias=10):
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super(StructuredChainedEmbeddingGenWithBypass, self).__init__()
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self.recurrent = recurrent
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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if recurrent:
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self.recurrent_nf = recurrent_nf
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self.recurrent_stride = recurrent_stride
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self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=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.bypasses = nn.ModuleList([OptionalPassthroughBlock(64, initial_bias=bypass_bias) 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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self.ref_join_std = 0
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self.block_residual_means = [0 for _ in range(depth)]
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self.block_residual_stds = [0 for _ in range(depth)]
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self.bypass_maps = []
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def forward(self, x, recurrent=None):
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fea = self.initial_conv(x)
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if self.recurrent:
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if recurrent is None:
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if self.recurrent_nf == 3:
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recurrent = torch.zeros_like(x)
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if self.recurrent_stride != 1:
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recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest')
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else:
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recurrent = torch.zeros_like(fea)
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rec = self.recurrent_process(recurrent)
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fea, recstd = self.recurrent_join(fea, rec)
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self.ref_join_std = recstd.item()
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emb = checkpoint(self.spine, fea)
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grad = fea
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self.bypass_maps = []
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for i, block in enumerate(self.blocks):
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residual, context = checkpoint(block, fea, *emb)
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residual, bypass_map = checkpoint(self.bypasses[i], residual, context)
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fea = fea + residual
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self.bypass_maps.append(bypass_map.detach())
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self.block_residual_means[i] = residual.mean().item()
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self.block_residual_stds[i] = residual.std().item()
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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), fea
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def visual_dbg(self, step, path):
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for i, bm in enumerate(self.bypass_maps):
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torchvision.utils.save_image(bm.cpu(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
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def get_debug_values(self, step, net_name):
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biases = [b.bias.item() for b in self.bypasses]
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blk_stds, blk_means = {}, {}
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for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)):
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blk_stds['block_%i' % (i+1,)] = s
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blk_means['block_%i' % (i+1,)] = m
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return {'ref_join_std': self.ref_join_std, 'bypass_biases': sum(biases) / len(biases),
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'blocks_std': blk_stds, 'blocks_mean': blk_means}
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