forked from mrq/DL-Art-School
270 lines
15 KiB
Python
270 lines
15 KiB
Python
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, in_nc=3):
|
|
super(ChainedEmbeddingGen, self).__init__()
|
|
self.initial_conv = ConvGnLelu(in_nc, 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, out_nc=in_nc)
|
|
|
|
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, in_nc=3, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2):
|
|
super(ChainedEmbeddingGenWithStructure, self).__init__()
|
|
self.recurrent = recurrent
|
|
self.initial_conv = ConvGnLelu(in_nc, 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().float(), 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}
|
|
|
|
|
|
class MultifacetedChainedEmbeddingGen(nn.Module):
|
|
def __init__(self, depth=10, scale=2):
|
|
super(MultifacetedChainedEmbeddingGen, self).__init__()
|
|
assert scale == 2 or scale == 4
|
|
|
|
self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
|
|
|
|
self.teco_recurrent_process = ConvGnLelu(3, 64, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
|
|
self.teco_recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
|
|
|
|
self.prog_recurrent_process = ConvGnLelu(64, 64, kernel_size=3, stride=1, norm=False, bias=True, activation=False)
|
|
self.prog_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=0) 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, scale=scale)
|
|
self.grad_extract = ImageGradientNoPadding()
|
|
self.upsample = FinalUpsampleBlock2x(64, scale=scale)
|
|
|
|
self.teco_ref_std = 0
|
|
self.prog_ref_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, teco_recurrent=None, prog_recurrent=None):
|
|
fea = self.initial_conv(x)
|
|
|
|
# Integrate recurrence inputs.
|
|
if teco_recurrent is not None:
|
|
teco_rec = self.teco_recurrent_process(teco_recurrent)
|
|
fea, std = self.teco_recurrent_join(fea, teco_rec)
|
|
self.teco_ref_std = std.item()
|
|
elif prog_recurrent is not None:
|
|
prog_rec = self.prog_recurrent_process(prog_recurrent)
|
|
prog_rec, std = self.prog_recurrent_join(fea, prog_rec)
|
|
self.prog_ref_std = std.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().float(), 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 {'teco_std': self.teco_ref_std,
|
|
'prog_std': self.prog_ref_std,
|
|
'bypass_biases': sum(biases) / len(biases),
|
|
'blocks_std': blk_stds, 'blocks_mean': blk_means}
|