292 lines
13 KiB
Python
292 lines
13 KiB
Python
import numpy as np
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import torch
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from torch import nn as nn
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import models.image_generation.srflow.Split
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from models.image_generation.srflow import flow
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from models.image_generation.srflow.Split import Split2d
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from models.image_generation.srflow.glow_arch import f_conv2d_bias
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from models.image_generation.srflow.FlowStep import FlowStep
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from utils.util import opt_get, checkpoint
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class FlowUpsamplerNet(nn.Module):
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def __init__(self, image_shape, hidden_channels, K, L=None,
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actnorm_scale=1.0,
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flow_permutation=None,
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flow_coupling="affine",
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LU_decomposed=False, opt=None):
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super().__init__()
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self.layers = nn.ModuleList()
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self.output_shapes = []
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self.L = opt_get(opt, ['networks', 'generator','flow', 'L'])
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self.K = opt_get(opt, ['networks', 'generator','flow', 'K'])
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self.patch_sz = opt_get(opt, ['networks', 'generator', 'flow', 'patch_size'], 160)
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if isinstance(self.K, int):
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self.K = [K for K in [K, ] * (self.L + 1)]
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self.opt = opt
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H, W, self.C = image_shape
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self.check_image_shape()
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if opt_get(self.opt, ['networks', 'generator', 'flow_scale']) == 16:
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self.levelToName = {
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0: 'fea_up16',
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1: 'fea_up8',
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2: 'fea_up4',
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3: 'fea_up2',
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4: 'fea_up1',
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}
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if opt_get(self.opt, ['networks', 'generator', 'flow_scale']) == 8:
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self.levelToName = {
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0: 'fea_up8',
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1: 'fea_up4',
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2: 'fea_up2',
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3: 'fea_up1',
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4: 'fea_up0'
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}
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elif opt_get(self.opt, ['networks', 'generator', 'flow_scale']) == 4:
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self.levelToName = {
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0: 'fea_up4',
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1: 'fea_up2',
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2: 'fea_up1',
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3: 'fea_up0',
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4: 'fea_up-1'
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}
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affineInCh = self.get_affineInCh(opt_get)
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flow_permutation = self.get_flow_permutation(flow_permutation, opt)
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normOpt = opt_get(opt, ['networks', 'generator','flow', 'norm'])
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conditional_channels = {}
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n_rrdb = self.get_n_rrdb_channels(opt, opt_get)
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n_bypass_channels = opt_get(opt, ['networks', 'generator','flow', 'levelConditional', 'n_channels'])
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conditional_channels[0] = n_rrdb
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for level in range(1, self.L + 1):
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# Level 1 gets conditionals from 2, 3, 4 => L - level
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# Level 2 gets conditionals from 3, 4
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# Level 3 gets conditionals from 4
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# Level 4 gets conditionals from None
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n_bypass = 0 if n_bypass_channels is None else (self.L - level) * n_bypass_channels
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conditional_channels[level] = n_rrdb + n_bypass
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# Upsampler
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for level in range(1, self.L + 1):
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# 1. Squeeze
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H, W = self.arch_squeeze(H, W)
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# 2. K FlowStep
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self.arch_additionalFlowAffine(H, LU_decomposed, W, actnorm_scale, hidden_channels, opt)
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self.arch_FlowStep(H, self.K[level], LU_decomposed, W, actnorm_scale, affineInCh, flow_coupling,
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flow_permutation,
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hidden_channels, normOpt, opt, opt_get,
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n_conditinal_channels=conditional_channels[level])
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# Split
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self.arch_split(H, W, level, self.L, opt, opt_get)
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if opt_get(opt, ['networks', 'generator','flow', 'split', 'enable']):
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self.f = f_conv2d_bias(affineInCh, 2 * 3 * 64 // 2 // 2)
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else:
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self.f = f_conv2d_bias(affineInCh, 2 * 3 * 64)
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self.H = H
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self.W = W
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self.scaleH = self.patch_sz / H
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self.scaleW = self.patch_sz / W
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def get_n_rrdb_channels(self, opt, opt_get):
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blocks = opt_get(opt, ['networks', 'generator','flow', 'stackRRDB', 'blocks'])
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n_rrdb = 64 if blocks is None else (len(blocks) + 1) * 64
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return n_rrdb
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def arch_FlowStep(self, H, K, LU_decomposed, W, actnorm_scale, affineInCh, flow_coupling, flow_permutation,
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hidden_channels, normOpt, opt, opt_get, n_conditinal_channels=None):
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condAff = self.get_condAffSetting(opt, opt_get)
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if condAff is not None:
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condAff['in_channels_rrdb'] = n_conditinal_channels
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for k in range(K):
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position_name = self.get_position_name(H, opt_get(self.opt, ['networks', 'generator', 'flow_scale']))
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if normOpt: normOpt['position'] = position_name
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self.layers.append(
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FlowStep(in_channels=self.C,
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hidden_channels=hidden_channels,
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actnorm_scale=actnorm_scale,
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flow_permutation=flow_permutation,
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flow_coupling=flow_coupling,
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acOpt=condAff,
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position=position_name,
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LU_decomposed=LU_decomposed, opt=opt, idx=k, normOpt=normOpt))
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self.output_shapes.append(
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[-1, self.C, H, W])
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def get_condAffSetting(self, opt, opt_get):
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condAff = opt_get(opt, ['networks', 'generator','flow', 'condAff']) or None
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condAff = opt_get(opt, ['networks', 'generator','flow', 'condFtAffine']) or condAff
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return condAff
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def arch_split(self, H, W, L, levels, opt, opt_get):
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correct_splits = opt_get(opt, ['networks', 'generator','flow', 'split', 'correct_splits'], False)
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correction = 0 if correct_splits else 1
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if opt_get(opt, ['networks', 'generator','flow', 'split', 'enable']) and L < levels - correction:
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logs_eps = opt_get(opt, ['networks', 'generator','flow', 'split', 'logs_eps']) or 0
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consume_ratio = opt_get(opt, ['networks', 'generator','flow', 'split', 'consume_ratio']) or 0.5
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position_name = self.get_position_name(H, opt_get(self.opt, ['networks', 'generator', 'flow_scale']))
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position = position_name if opt_get(opt, ['networks', 'generator','flow', 'split', 'conditional']) else None
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cond_channels = opt_get(opt, ['networks', 'generator','flow', 'split', 'cond_channels'])
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cond_channels = 0 if cond_channels is None else cond_channels
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t = opt_get(opt, ['networks', 'generator','flow', 'split', 'type'], 'Split2d')
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if t == 'Split2d':
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split = models.image_generation.srflow.Split.Split2d(num_channels=self.C, logs_eps=logs_eps, position=position,
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cond_channels=cond_channels, consume_ratio=consume_ratio, opt=opt)
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self.layers.append(split)
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self.output_shapes.append([-1, split.num_channels_pass, H, W])
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self.C = split.num_channels_pass
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def arch_additionalFlowAffine(self, H, LU_decomposed, W, actnorm_scale, hidden_channels, opt):
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if 'additionalFlowNoAffine' in opt['networks']['generator']['flow']:
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n_additionalFlowNoAffine = int(opt['networks']['generator']['flow']['additionalFlowNoAffine'])
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for _ in range(n_additionalFlowNoAffine):
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self.layers.append(
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FlowStep(in_channels=self.C,
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hidden_channels=hidden_channels,
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actnorm_scale=actnorm_scale,
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flow_permutation='invconv',
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flow_coupling='noCoupling',
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LU_decomposed=LU_decomposed, opt=opt))
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self.output_shapes.append(
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[-1, self.C, H, W])
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def arch_squeeze(self, H, W):
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self.C, H, W = self.C * 4, H // 2, W // 2
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self.layers.append(flow.SqueezeLayer(factor=2))
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self.output_shapes.append([-1, self.C, H, W])
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return H, W
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def get_flow_permutation(self, flow_permutation, opt):
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flow_permutation = opt['networks']['generator']['flow'].get('flow_permutation', 'invconv')
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return flow_permutation
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def get_affineInCh(self, opt_get):
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affineInCh = opt_get(self.opt, ['networks', 'generator','flow', 'stackRRDB', 'blocks']) or []
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affineInCh = (len(affineInCh) + 1) * 64
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return affineInCh
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def check_image_shape(self):
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assert self.C == 1 or self.C == 3, ("image_shape should be HWC, like (64, 64, 3)"
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"self.C == 1 or self.C == 3")
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def forward(self, gt=None, rrdbResults=None, z=None, epses=None, logdet=0., reverse=False, eps_std=None,
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y_onehot=None):
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if reverse:
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epses_copy = [eps for eps in epses] if isinstance(epses, list) else epses
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sr, logdet = self.decode(rrdbResults, z, eps_std, epses=epses_copy, logdet=logdet, y_onehot=y_onehot)
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return sr, logdet
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else:
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assert gt is not None
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assert rrdbResults is not None
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z, logdet = self.encode(gt, rrdbResults, logdet=logdet, epses=epses, y_onehot=y_onehot)
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return z, logdet
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def encode(self, gt, rrdbResults, logdet=0.0, epses=None, y_onehot=None):
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fl_fea = gt
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reverse = False
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level_conditionals = {}
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bypasses = {}
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L = opt_get(self.opt, ['networks', 'generator','flow', 'L'])
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for level in range(1, L + 1):
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bypasses[level] = torch.nn.functional.interpolate(gt, scale_factor=2 ** -level, mode='bilinear', align_corners=False)
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for layer, shape in zip(self.layers, self.output_shapes):
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size = shape[2]
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level = int(np.log(self.patch_sz / size) / np.log(2))
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if level > 0 and level not in level_conditionals.keys():
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level_conditionals[level] = rrdbResults[self.levelToName[level]]
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level_conditionals[level] = rrdbResults[self.levelToName[level]]
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if isinstance(layer, FlowStep):
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fl_fea, logdet = checkpoint(layer, fl_fea, logdet, level_conditionals[level])
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elif isinstance(layer, Split2d):
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fl_fea, logdet = self.forward_split2d(epses, fl_fea, layer, logdet, reverse, level_conditionals[level],
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y_onehot=y_onehot)
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else:
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fl_fea, logdet = layer(fl_fea, logdet, reverse=reverse)
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z = fl_fea
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if not isinstance(epses, list):
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return z, logdet
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epses.append(z)
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return epses, logdet
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def forward_preFlow(self, fl_fea, logdet, reverse):
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if hasattr(self, 'preFlow'):
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for l in self.preFlow:
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fl_fea, logdet = l(fl_fea, logdet, reverse=reverse)
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return fl_fea, logdet
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def forward_split2d(self, epses, fl_fea, layer, logdet, reverse, rrdbResults, y_onehot=None):
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ft = None if layer.position is None else rrdbResults[layer.position]
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fl_fea, logdet, eps = layer(fl_fea, logdet, reverse=reverse, eps=epses, ft=ft, y_onehot=y_onehot)
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epses.append(eps)
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return fl_fea, logdet
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def decode(self, rrdbResults, z, eps_std=None, epses=None, logdet=0.0, y_onehot=None):
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z = epses.pop() if isinstance(epses, list) else z
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fl_fea = z
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# debug.imwrite("fl_fea", fl_fea)
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bypasses = {}
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level_conditionals = {}
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if not opt_get(self.opt, ['networks', 'generator','flow', 'levelConditional', 'conditional']) == True:
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for level in range(self.L + 1):
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level_conditionals[level] = rrdbResults[self.levelToName[level]]
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for layer, shape in zip(reversed(self.layers), reversed(self.output_shapes)):
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size = shape[2]
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level = int(np.log(self.patch_sz / size) / np.log(2))
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# size = fl_fea.shape[2]
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# level = int(np.log(160 / size) / np.log(2))
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if isinstance(layer, Split2d):
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fl_fea, logdet = self.forward_split2d_reverse(eps_std, epses, fl_fea, layer,
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rrdbResults[self.levelToName[level]], logdet=logdet,
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y_onehot=y_onehot)
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elif isinstance(layer, FlowStep):
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fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True, rrdbResults=level_conditionals[level])
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else:
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fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True)
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sr = fl_fea
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assert sr.shape[1] == 3
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return sr, logdet
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def forward_split2d_reverse(self, eps_std, epses, fl_fea, layer, rrdbResults, logdet, y_onehot=None):
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ft = None if layer.position is None else rrdbResults[layer.position]
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fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True,
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eps=epses.pop() if isinstance(epses, list) else None,
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eps_std=eps_std, ft=ft, y_onehot=y_onehot)
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return fl_fea, logdet
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def get_position_name(self, H, scale):
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downscale_factor = self.patch_sz // H
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position_name = 'fea_up{}'.format(scale / downscale_factor)
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return position_name
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