DL-Art-School/codes/models/archs/srflow/FlowUpsamplerNet.py

import numpy as np
import torch
from torch import nn as nn

import models.archs.srflow.Split
from models.archs.srflow import flow, thops, Split
from models.archs.srflow.Split import Split2d
from models.archs.srflow.glow_arch import f_conv2d_bias
from models.archs.srflow.FlowStep import FlowStep
from utils.util import opt_get
import torchvision


class FlowUpsamplerNet(nn.Module):
    def __init__(self, image_shape, hidden_channels, scale,
                 rrdb_blocks,
                 actnorm_scale=1.0,
                 flow_permutation='invconv',
                 flow_coupling="affine",
                 LU_decomposed=False, K=16, L=3,
                 norm_opt=None,
                 n_bypass_channels=None):

        super().__init__()

        self.layers = nn.ModuleList()
        self.output_shapes = []
        self.L = L
        self.K = K
        self.scale=scale
        if isinstance(self.K, int):
            self.K = [K for K in [K, ] * (self.L + 1)]

        H, W, self.C = image_shape
        self.image_shape = image_shape
        self.check_image_shape()

        if scale == 16:
            self.levelToName = {
                0: 'fea_up16',
                1: 'fea_up8',
                2: 'fea_up4',
                3: 'fea_up2',
                4: 'fea_up1',
            }

        if scale == 8:
            self.levelToName = {
                0: 'fea_up8',
                1: 'fea_up4',
                2: 'fea_up2',
                3: 'fea_up1',
                4: 'fea_up0'
            }

        elif scale == 4:
            self.levelToName = {
                0: 'fea_up4',
                1: 'fea_up2',
                2: 'fea_up1',
                3: 'fea_up0',
                4: 'fea_up-1'
            }

        affineInCh = self.get_affineInCh(rrdb_blocks)

        conditional_channels = {}
        n_rrdb = self.get_n_rrdb_channels(rrdb_blocks)
        conditional_channels[0] = n_rrdb
        for level in range(1, self.L + 1):
            # Level 1 gets conditionals from 2, 3, 4 => L - level
            # Level 2 gets conditionals from 3, 4
            # Level 3 gets conditionals from 4
            # Level 4 gets conditionals from None
            n_bypass = 0 if n_bypass_channels is None else (self.L - level) * n_bypass_channels
            conditional_channels[level] = n_rrdb + n_bypass

        # Upsampler
        for level in range(1, self.L + 1):
            # 1. Squeeze
            H, W = self.arch_squeeze(H, W)

            # 2. K FlowStep
            self.arch_additionalFlowAffine(H, LU_decomposed, W, actnorm_scale, hidden_channels)
            self.arch_FlowStep(H, self.K[level], LU_decomposed, W, actnorm_scale, affineInCh, flow_coupling,
                               flow_permutation,
                               hidden_channels, norm_opt,
                               n_conditional_channels=conditional_channels[level])
            # Split
            self.arch_split(H, W, level, self.L)

        self.f = f_conv2d_bias(affineInCh, 2 * 3 * 64 // 2 // 2)
        self.H = H
        self.W = W

    def get_n_rrdb_channels(self, blocks):
        n_rrdb = 64 if blocks is None else (len(blocks) + 1) * 64
        return n_rrdb

    def arch_FlowStep(self, H, K, LU_decomposed, W, actnorm_scale, affineInCh, flow_coupling, flow_permutation,
                      hidden_channels, normOpt, n_conditional_channels=None, condAff=None):
        if condAff is not None:
            condAff['in_channels_rrdb'] = n_conditional_channels

        for k in range(K):
            position_name = self.get_position_name(H, self.scale)
            if normOpt: normOpt['position'] = position_name

            self.layers.append(
                FlowStep(in_channels=self.C,
                         hidden_channels=hidden_channels,
                         actnorm_scale=actnorm_scale,
                         flow_permutation=flow_permutation,
                         flow_coupling=flow_coupling,
                         acOpt=condAff,
                         position=position_name,
                         LU_decomposed=LU_decomposed, idx=k, normOpt=normOpt))
            self.output_shapes.append(
                [-1, self.C, H, W])

    def arch_split(self, H, W, L, levels, split_flow=True, correct_splits=False, logs_eps=0, consume_ratio=.5, split_conditional=False, cond_channels=None, split_type='Split2d'):
        correction = 0 if correct_splits else 1
        if split_flow and L < levels - correction:
            logs_eps = logs_eps
            consume_ratio = consume_ratio
            position_name = self.get_position_name(H, self.scale)
            position = position_name if split_conditional else None
            cond_channels = 0 if cond_channels is None else cond_channels

            if split_type == 'Split2d':
                split = Split.Split2d(num_channels=self.C, logs_eps=logs_eps, position=position,
                                                     cond_channels=cond_channels, consume_ratio=consume_ratio)
            self.layers.append(split)
            self.output_shapes.append([-1, split.num_channels_pass, H, W])
            self.C = split.num_channels_pass

    def arch_additionalFlowAffine(self, H, LU_decomposed, W, actnorm_scale, hidden_channels, additionalFlowNoAffine=2):
        for _ in range(additionalFlowNoAffine):
            self.layers.append(
                FlowStep(in_channels=self.C,
                         hidden_channels=hidden_channels,
                         actnorm_scale=actnorm_scale,
                         flow_permutation='invconv',
                         flow_coupling='noCoupling',
                         LU_decomposed=LU_decomposed))
            self.output_shapes.append(
                [-1, self.C, H, W])

    def arch_squeeze(self, H, W):
        self.C, H, W = self.C * 4, H // 2, W // 2
        self.layers.append(flow.SqueezeLayer(factor=2))
        self.output_shapes.append([-1, self.C, H, W])
        return H, W

    def get_affineInCh(self, rrdb_blocks):
        affineInCh = (len(rrdb_blocks) + 1) * 64
        return affineInCh

    def check_image_shape(self):
        assert self.C == 1 or self.C == 3, ("image_shape should be HWC, like (64, 64, 3)"
                                            "self.C == 1 or self.C == 3")

    def forward(self, gt=None, rrdbResults=None, z=None, epses=None, logdet=0., reverse=False, eps_std=None,
                y_onehot=None):

        if reverse:
            epses_copy = [eps for eps in epses] if isinstance(epses, list) else epses

            sr, logdet = self.decode(rrdbResults, z, eps_std, epses=epses_copy, logdet=logdet, y_onehot=y_onehot)
            return sr, logdet
        else:
            assert gt is not None
            assert rrdbResults is not None
            z, logdet = self.encode(gt, rrdbResults, logdet=logdet, epses=epses, y_onehot=y_onehot)

            return z, logdet

    def encode(self, gt, rrdbResults, logdet=0.0, epses=None, y_onehot=None):
        fl_fea = gt
        reverse = False
        level_conditionals = {}
        bypasses = {}

        L = self.L

        for level in range(1, L + 1):
            bypasses[level] = torch.nn.functional.interpolate(gt, scale_factor=2 ** -level, mode='bilinear', align_corners=False)

        for layer, shape in zip(self.layers, self.output_shapes):
            size = shape[2]
            level = int(np.log(self.image_shape[0] / size) / np.log(2))

            if level > 0 and level not in level_conditionals.keys():
                level_conditionals[level] = rrdbResults[self.levelToName[level]]

            level_conditionals[level] = rrdbResults[self.levelToName[level]]

            if isinstance(layer, FlowStep):
                fl_fea, logdet = layer(fl_fea, logdet, reverse=reverse, rrdbResults=level_conditionals[level])
            elif isinstance(layer, Split2d):
                fl_fea, logdet = self.forward_split2d(epses, fl_fea, layer, logdet, reverse, level_conditionals[level],
                                                      y_onehot=y_onehot)
            else:
                fl_fea, logdet = layer(fl_fea, logdet, reverse=reverse)

        z = fl_fea

        if not isinstance(epses, list):
            return z, logdet

        epses.append(z)
        return epses, logdet

    def forward_preFlow(self, fl_fea, logdet, reverse):
        if hasattr(self, 'preFlow'):
            for l in self.preFlow:
                fl_fea, logdet = l(fl_fea, logdet, reverse=reverse)
        return fl_fea, logdet

    def forward_split2d(self, epses, fl_fea, layer, logdet, reverse, rrdbResults, y_onehot=None):
        ft = None if layer.position is None else rrdbResults[layer.position]
        fl_fea, logdet, eps = layer(fl_fea, logdet, reverse=reverse, eps=epses, ft=ft, y_onehot=y_onehot)

        if isinstance(epses, list):
            epses.append(eps)
        return fl_fea, logdet

    def decode(self, rrdbResults, z, eps_std=None, epses=None, logdet=0.0, y_onehot=None):
        z = epses.pop() if isinstance(epses, list) else z

        fl_fea = z
        # debug.imwrite("fl_fea", fl_fea)
        bypasses = {}
        level_conditionals = {}
        for level in range(self.L + 1):
            level_conditionals[level] = rrdbResults[self.levelToName[level]]

        for layer, shape in zip(reversed(self.layers), reversed(self.output_shapes)):
            size = shape[2]
            level = int(np.log(self.H / size) / np.log(2))

            if isinstance(layer, Split2d):
                fl_fea, logdet = self.forward_split2d_reverse(eps_std, epses, fl_fea, layer,
                                                              rrdbResults[self.levelToName[level]], logdet=logdet,
                                                              y_onehot=y_onehot)
            elif isinstance(layer, FlowStep):
                fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True, rrdbResults=level_conditionals[level])
            else:
                fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True)

        sr = fl_fea

        assert sr.shape[1] == 3
        return sr, logdet

    def forward_split2d_reverse(self, eps_std, epses, fl_fea, layer, rrdbResults, logdet, y_onehot=None):
        ft = None if layer.position is None else rrdbResults[layer.position]
        fl_fea, logdet = layer(fl_fea, logdet=logdet, reverse=True,
                               eps=epses.pop() if isinstance(epses, list) else None,
                               eps_std=eps_std, ft=ft, y_onehot=y_onehot)
        return fl_fea, logdet


    def get_position_name(self, H, scale):
        downscale_factor = self.image_shape[0] // H
        position_name = 'fea_up{}'.format(scale / downscale_factor)
        return position_name