import os import torch import torchvision from torch import nn from models.archs.SPSR_arch import ImageGradientNoPadding from models.archs.arch_util import ConvGnLelu, ExpansionBlock2, ConvGnSilu, ConjoinBlock, MultiConvBlock, \ FinalUpsampleBlock2x, ReferenceJoinBlock 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, p class ChainedEmbeddingGen(nn.Module): def __init__(self, depth=10): 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(depth)]) self.upsample = FinalUpsampleBlock2x(64) def forward(self, x): fea = self.initial_conv(x) emb = checkpoint(self.spine, fea) for block in self.blocks: fea = fea + checkpoint(block, fea, *emb)[0] return checkpoint(self.upsample, fea), class ChainedEmbeddingGenWithStructure(nn.Module): def __init__(self, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2): super(ChainedEmbeddingGenWithStructure, self).__init__() self.recurrent = recurrent self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False) if recurrent: self.recurrent_nf = recurrent_nf self.recurrent_stride = recurrent_stride self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False) self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False) self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False) self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)]) self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)]) 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)]) self.structure_upsample = FinalUpsampleBlock2x(64) self.grad_extract = ImageGradientNoPadding() self.upsample = FinalUpsampleBlock2x(64) self.ref_join_std = 0 def forward(self, x, recurrent=None): fea = self.initial_conv(x) if self.recurrent: if recurrent is None: if self.recurrent_nf == 3: recurrent = torch.zeros_like(x) if self.recurrent_stride != 1: recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest') else: recurrent = torch.zeros_like(fea) rec = self.recurrent_process(recurrent) fea, recstd = self.recurrent_join(fea, rec) self.ref_join_std = recstd.item() emb = checkpoint(self.spine, fea) grad = fea for i, block in enumerate(self.blocks): fea = fea + checkpoint(block, fea, *emb)[0] if i < 3: structure_br = checkpoint(self.structure_joins[i], grad, fea) grad = grad + checkpoint(self.structure_blocks[i], structure_br) out = checkpoint(self.upsample, fea) return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out), fea def get_debug_values(self, step, net_name): return { 'ref_join_std': self.ref_join_std } # This is a structural block that learns to mute regions of a residual transformation given a signal. class OptionalPassthroughBlock(nn.Module): def __init__(self, nf, initial_bias=10): super(OptionalPassthroughBlock, self).__init__() self.switch_process = nn.Sequential(ConvGnLelu(nf, nf // 2, 1, activation=False, norm=False, bias=False), ConvGnLelu(nf // 2, nf // 4, 1, activation=False, norm=False, bias=False), ConvGnLelu(nf // 4, 1, 1, activation=False, norm=False, bias=False)) self.bias = nn.Parameter(torch.tensor(initial_bias, dtype=torch.float), requires_grad=True) self.activation = nn.Sigmoid() def forward(self, x, switch_signal): switch = self.switch_process(switch_signal) bypass_map = self.activation(self.bias + switch) return x * bypass_map, bypass_map class StructuredChainedEmbeddingGenWithBypass(nn.Module): def __init__(self, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2, bypass_bias=10): super(StructuredChainedEmbeddingGenWithBypass, self).__init__() self.recurrent = recurrent self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False) if recurrent: self.recurrent_nf = recurrent_nf self.recurrent_stride = recurrent_stride self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False) self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False) self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False) self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)]) self.bypasses = nn.ModuleList([OptionalPassthroughBlock(64, initial_bias=bypass_bias) for i in range(depth)]) self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)]) 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)]) self.structure_upsample = FinalUpsampleBlock2x(64) self.grad_extract = ImageGradientNoPadding() self.upsample = FinalUpsampleBlock2x(64) self.ref_join_std = 0 self.block_residual_means = [0 for _ in range(depth)] self.block_residual_stds = [0 for _ in range(depth)] self.bypass_maps = [] def forward(self, x, recurrent=None): fea = self.initial_conv(x) if self.recurrent: if recurrent is None: if self.recurrent_nf == 3: recurrent = torch.zeros_like(x) if self.recurrent_stride != 1: recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest') else: recurrent = torch.zeros_like(fea) rec = self.recurrent_process(recurrent) fea, recstd = self.recurrent_join(fea, rec) self.ref_join_std = recstd.item() emb = checkpoint(self.spine, fea) grad = fea self.bypass_maps = [] for i, block in enumerate(self.blocks): residual, context = checkpoint(block, fea, *emb) residual, bypass_map = checkpoint(self.bypasses[i], residual, context) fea = fea + residual self.bypass_maps.append(bypass_map.detach()) self.block_residual_means[i] = residual.mean().item() self.block_residual_stds[i] = residual.std().item() if i < 3: structure_br = checkpoint(self.structure_joins[i], grad, fea) grad = grad + checkpoint(self.structure_blocks[i], structure_br) out = checkpoint(self.upsample, fea) return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out), fea def visual_dbg(self, step, path): for i, bm in enumerate(self.bypass_maps): torchvision.utils.save_image(bm.cpu(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1))) def get_debug_values(self, step, net_name): biases = [b.bias.item() for b in self.bypasses] blk_stds, blk_means = {}, {} for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)): blk_stds['block_%i' % (i+1,)] = s blk_means['block_%i' % (i+1,)] = m return {'ref_join_std': self.ref_join_std, 'bypass_biases': sum(biases) / len(biases), 'blocks_std': blk_stds, 'blocks_mean': blk_means}