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This commit is contained in:
James Betker 2020-12-18 09:24:31 -07:00
parent 5640e4efe4
commit d875ca8342
35 changed files with 80 additions and 80 deletions
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2
codes/data/byol_attachment.py
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@ -15,7 +15,7 @@ import torch.nn.functional as F
from tqdm import tqdm
from data import create_dataset
from models.archs.arch_util import PixelUnshuffle
from models.arch_util import PixelUnshuffle
from utils.util import opt_get

2
codes/data/stylegan2_dataset.py
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@ -9,7 +9,7 @@ from torchvision import transforms
import torch.nn as nn
from pathlib import Path
import models.archs.stylegan.stylegan2_lucidrains as sg2
import models.stylegan.stylegan2_lucidrains as sg2
def convert_transparent_to_rgb(image):

4
codes/models/ProgressiveSrg_arch.py
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@ -1,9 +1,9 @@
import models.archs.SwitchedResidualGenerator_arch as srg
import models.SwitchedResidualGenerator_arch as srg
import torch
import torch.nn as nn
from switched_conv.switched_conv_util import save_attention_to_image
from switched_conv.switched_conv import compute_attention_specificity
from models.archs.arch_util import ConvGnLelu, ExpansionBlock, MultiConvBlock
from models.arch_util import ConvGnLelu, ExpansionBlock, MultiConvBlock
import functools
import torch.nn.functional as F

2
codes/models/RRDBNet_arch.py
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@ -5,7 +5,7 @@ import torch.nn as nn
import torch.nn.functional as F
import torchvision
from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
from models.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
from utils.util import checkpoint, sequential_checkpoint

8
codes/models/SwitchedResidualGenerator_arch.py
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@ -1,14 +1,14 @@
import torch
from torch import nn
from switched_conv.switched_conv import BareConvSwitch, compute_attention_specificity, AttentionNorm
from models.switched_conv.switched_conv import BareConvSwitch, compute_attention_specificity, AttentionNorm
import torch.nn.functional as F
import functools
from collections import OrderedDict
from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2, ConvGnLelu, MultiConvBlock, \
from models.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2, ConvGnLelu, MultiConvBlock, \
SiLU, UpconvBlock, ReferenceJoinBlock
from switched_conv.switched_conv_util import save_attention_to_image_rgb
from models.switched_conv.switched_conv_util import save_attention_to_image_rgb
import os
from models.archs.spinenet_arch import SpineNet
from models.spinenet_arch import SpineNet
import torchvision
from utils.util import checkpoint

2
codes/models/byol/byol_structural.py
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@ -5,7 +5,7 @@ import torch.nn.functional as F
from torch import nn
from data.byol_attachment import reconstructed_shared_regions
from models.archs.byol.byol_model_wrapper import singleton, EMA, get_module_device, set_requires_grad, \
from models.byol.byol_model_wrapper import singleton, EMA, get_module_device, set_requires_grad, \
update_moving_average
from utils.util import checkpoint

6
codes/models/discriminator_vgg_arch.py
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@ -1,10 +1,10 @@
import torch
import torch.nn as nn
from models.archs.RRDBNet_arch import RRDB, RRDBWithBypass
from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
from models.RRDBNet_arch import RRDB, RRDBWithBypass
from models.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
import torch.nn.functional as F
from models.archs.SwitchedResidualGenerator_arch import gather_2d
from models.SwitchedResidualGenerator_arch import gather_2d
from utils.util import checkpoint

2
codes/models/spinenet_arch.py
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@ -6,7 +6,7 @@ import torch.nn.functional as F
from torch.nn.init import kaiming_normal
from torchvision.models.resnet import BasicBlock, Bottleneck
from models.archs.arch_util import ConvGnSilu, ConvBnSilu, ConvBnRelu
from models.arch_util import ConvGnSilu, ConvBnSilu, ConvBnRelu
def constant_init(module, val, bias=0):

2
codes/models/srflow_orig/FlowActNorms.py → codes/models/srflow/FlowActNorms.py
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@ -1,7 +1,7 @@
import torch
from torch import nn as nn
from models.srflow_orig import thops
from models.srflow import thops
class _ActNorm(nn.Module):

4
codes/models/srflow_orig/FlowAffineCouplingsAblation.py → codes/models/srflow/FlowAffineCouplingsAblation.py
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@ -1,8 +1,8 @@
import torch
from torch import nn as nn
from models.srflow_orig import thops
from models.archs.srflow_orig.flow import Conv2d, Conv2dZeros
from models.srflow import thops
from models.srflow.flow import Conv2d, Conv2dZeros
from utils.util import opt_get

10
codes/models/srflow_orig/FlowStep.py → codes/models/srflow/FlowStep.py
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@ -1,7 +1,7 @@
import torch
from torch import nn as nn
import models.archs.srflow_orig.Permutations
import models.srflow.Permutations
def getConditional(rrdbResults, position):
@ -44,17 +44,17 @@ class FlowStep(nn.Module):
self.acOpt = acOpt
# 1. actnorm
self.actnorm = models.archs.srflow_orig.FlowActNorms.ActNorm2d(in_channels, actnorm_scale)
self.actnorm = models.srflow.FlowActNorms.ActNorm2d(in_channels, actnorm_scale)
# 2. permute
if flow_permutation == "invconv":
self.invconv = models.archs.srflow_orig.Permutations.InvertibleConv1x1(
self.invconv = models.srflow.Permutations.InvertibleConv1x1(
in_channels, LU_decomposed=LU_decomposed)
# 3. coupling
if flow_coupling == "CondAffineSeparatedAndCond":
self.affine = models.archs.srflow_orig.FlowAffineCouplingsAblation.CondAffineSeparatedAndCond(in_channels=in_channels,
opt=opt)
self.affine = models.srflow.FlowAffineCouplingsAblation.CondAffineSeparatedAndCond(in_channels=in_channels,
opt=opt)
elif flow_coupling == "noCoupling":
pass
else:

16
codes/models/srflow_orig/FlowUpsamplerNet.py → codes/models/srflow/FlowUpsamplerNet.py
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@ -2,12 +2,12 @@ import numpy as np
import torch
from torch import nn as nn
import models.archs.srflow_orig.Split
from models.archs.srflow_orig import flow
from models.srflow_orig import thops
from models.archs.srflow_orig.Split import Split2d
from models.archs.srflow_orig.glow_arch import f_conv2d_bias
from models.archs.srflow_orig.FlowStep import FlowStep
import models.srflow.Split
from models.srflow import flow
from models.srflow import thops
from models.srflow.Split import Split2d
from models.srflow.glow_arch import f_conv2d_bias
from models.srflow.FlowStep import FlowStep
from utils.util import opt_get, checkpoint
@ -146,8 +146,8 @@ class FlowUpsamplerNet(nn.Module):
t = opt_get(opt, ['networks', 'generator','flow', 'split', 'type'], 'Split2d')
if t == 'Split2d':
split = models.archs.srflow_orig.Split.Split2d(num_channels=self.C, logs_eps=logs_eps, position=position,
cond_channels=cond_channels, consume_ratio=consume_ratio, opt=opt)
split = models.srflow.Split.Split2d(num_channels=self.C, logs_eps=logs_eps, position=position,
cond_channels=cond_channels, consume_ratio=consume_ratio, opt=opt)
self.layers.append(split)
self.output_shapes.append([-1, split.num_channels_pass, H, W])
self.C = split.num_channels_pass

2
codes/models/srflow_orig/Permutations.py → codes/models/srflow/Permutations.py
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@ -3,7 +3,7 @@ import torch
from torch import nn as nn
from torch.nn import functional as F
from models.srflow_orig import thops
from models.srflow import thops
class InvertibleConv1x1(nn.Module):

4
codes/models/srflow_orig/RRDBNet_arch.py → codes/models/srflow/RRDBNet_arch.py
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@ -2,8 +2,8 @@ import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.archs.srflow_orig.module_util as mutil
from models.archs.arch_util import default_init_weights, ConvGnSilu, ConvGnLelu
import models.srflow.module_util as mutil
from models.arch_util import default_init_weights, ConvGnSilu, ConvGnLelu
from utils.util import opt_get

8
codes/models/srflow_orig/SRFlowNet_arch.py → codes/models/srflow/SRFlowNet_arch.py
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@ -4,10 +4,10 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.archs.srflow_orig.RRDBNet_arch import RRDBNet
from models.archs.srflow_orig.FlowUpsamplerNet import FlowUpsamplerNet
import models.srflow_orig.thops as thops
import models.archs.srflow_orig.flow as flow
from models.srflow.RRDBNet_arch import RRDBNet
from models.srflow.FlowUpsamplerNet import FlowUpsamplerNet
import models.srflow.thops as thops
import models.srflow.flow as flow
from utils.util import opt_get

4
codes/models/srflow_orig/Split.py → codes/models/srflow/Split.py
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@ -1,8 +1,8 @@
import torch
from torch import nn as nn
from models.srflow_orig import thops
from models.archs.srflow_orig.flow import Conv2dZeros, GaussianDiag
from models.srflow import thops
from models.srflow.flow import Conv2dZeros, GaussianDiag
from utils.util import opt_get

0
codes/models/srflow_orig/__init__.py → codes/models/srflow/__init__.py
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2
codes/models/srflow_orig/flow.py → codes/models/srflow/flow.py
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@ -3,7 +3,7 @@ import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.archs.srflow_orig.FlowActNorms import ActNorm2d
from models.srflow.FlowActNorms import ActNorm2d
from . import thops

0
codes/models/srflow_orig/glow_arch.py → codes/models/srflow/glow_arch.py
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0
codes/models/srflow_orig/module_util.py → codes/models/srflow/module_util.py
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0
codes/models/srflow_orig/thops.py → codes/models/srflow/thops.py
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6
codes/models/srg2_classic.py
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@ -8,9 +8,9 @@ import torch.nn.functional as F
import functools
from collections import OrderedDict
from models.archs.SwitchedResidualGenerator_arch import HalvingProcessingBlock, ConfigurableSwitchComputer
from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, MultiConvBlock
from switched_conv.switched_conv import BareConvSwitch, AttentionNorm
from models.SwitchedResidualGenerator_arch import HalvingProcessingBlock, ConfigurableSwitchComputer
from models.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, MultiConvBlock
from models.switched_conv.switched_conv import BareConvSwitch, AttentionNorm
from utils.util import checkpoint

2
codes/models/stylegan/__init__.py
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@ -1,4 +1,4 @@
import models.archs.stylegan.stylegan2_lucidrains as stylegan2
import models.stylegan.stylegan2_lucidrains as stylegan2
def create_stylegan2_loss(opt_loss, env):

2
codes/models/tecogan/teco_resgen.py
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@ -5,7 +5,7 @@ import torch.nn as nn
import torchvision
from utils.util import sequential_checkpoint
from models.archs.arch_util import ConvGnSilu, make_layer
from models.arch_util import ConvGnSilu, make_layer
class TecoResblock(nn.Module):

2
codes/scripts/byol_extract_wrapped_model.py
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@ -1,6 +1,6 @@
import torch
from models.archs.spinenet_arch import SpineNet
from models.spinenet_arch import SpineNet
if __name__ == '__main__':
pretrained_path = '../../experiments/train_sbyol_512unsupervised_restart/models/48000_generator.pth'

2
codes/scripts/byol_spinenet_playground.py
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@ -13,7 +13,7 @@ import numpy as np
import utils
from data.image_folder_dataset import ImageFolderDataset
from models.archs.spinenet_arch import SpineNet
from models.spinenet_arch import SpineNet
# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved

2
codes/scripts/stylegan2/convert_weights_rosinality.py
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@ -13,7 +13,7 @@ import torch
import numpy as np
from torchvision import utils
from models.archs.stylegan.stylegan2_rosinality import Generator, Discriminator
from models.stylegan.stylegan2_rosinality import Generator, Discriminator
# Converts from the TF state_dict input provided into the vars originally expected from the rosinality converter.

4
codes/trainer/custom_training_components/stereoscopic.py
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@ -1,7 +1,7 @@
import torch
from torch.cuda.amp import autocast
from models.archs.flownet2.networks import Resample2d
from models.archs.flownet2 import flow2img
from models.flownet2.networks import Resample2d
from models.flownet2 import flow2img
from trainer.injectors import Injector

4
codes/trainer/custom_training_components/tecogan_losses.py
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@ -1,8 +1,8 @@
from torch.cuda.amp import autocast
from models.archs.stylegan.stylegan2_lucidrains import gradient_penalty
from models.stylegan.stylegan2_lucidrains import gradient_penalty
from trainer.losses import ConfigurableLoss, GANLoss, extract_params_from_state, get_basic_criterion_for_name
from models.archs.flownet2.networks import Resample2d
from models.flownet2.networks import Resample2d
from trainer.injectors import Injector
import torch
import torch.nn.functional as F

2
codes/trainer/eval/__init__.py
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@ -1,6 +1,6 @@
from trainer.eval.flow_gaussian_nll import FlowGaussianNll
from trainer.eval.sr_style import SrStyleTransferEvaluator
from trainer.eval import StyleTransferEvaluator
from trainer.eval.style import StyleTransferEvaluator
def create_evaluator(model, opt_eval, env):

2
codes/trainer/eval/flow_gaussian_nll.py
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@ -6,7 +6,7 @@ import trainer.eval.evaluator as evaluator
# Evaluate how close to true Gaussian a flow network predicts in a "normal" pass given a LQ/HQ image pair.
from data.image_folder_dataset import ImageFolderDataset
from models.archs.srflow_orig.flow import GaussianDiag
from models.srflow.flow import GaussianDiag
class FlowGaussianNll(evaluator.Evaluator):

4
codes/trainer/injectors.py
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@ -19,7 +19,7 @@ def create_injector(opt_inject, env):
from trainer.custom_training_components import create_stereoscopic_injector
return create_stereoscopic_injector(opt_inject, env)
elif 'igpt' in type:
from models.archs.transformers.igpt import gpt2
from models.transformers.igpt import gpt2
return gpt2.create_injector(opt_inject, env)
elif type == 'generator':
return ImageGeneratorInjector(opt_inject, env)
@ -372,7 +372,7 @@ class MultiFrameCombiner(Injector):
self.in_hq_key = opt['in_hq']
self.out_lq_key = opt['out']
self.out_hq_key = opt['out_hq']
from models.archs.flownet2.networks import Resample2d
from models.flownet2.networks import Resample2d
self.resampler = Resample2d()
def combine(self, state):

4
codes/trainer/losses.py
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@ -14,7 +14,7 @@ def create_loss(opt_loss, env):
from trainer.custom_training_components import create_teco_loss
return create_teco_loss(opt_loss, env)
elif 'stylegan2_' in type:
from models.archs.stylegan import create_stylegan2_loss
from models.stylegan import create_stylegan2_loss
return create_stylegan2_loss(opt_loss, env)
elif type == 'crossentropy':
return CrossEntropy(opt_loss, env)
@ -311,7 +311,7 @@ class DiscriminatorGanLoss(ConfigurableLoss):
if self.gradient_penalty:
# Apply gradient penalty. TODO: migrate this elsewhere.
from models.archs.stylegan.stylegan2_lucidrains import gradient_penalty
from models.stylegan.stylegan2_lucidrains import gradient_penalty
assert len(real) == 1 # Grad penalty doesn't currently support multi-input discriminators.
gp = gradient_penalty(real[0], d_real)
self.metrics.append(("gradient_penalty", gp.clone().detach()))

40
codes/trainer/networks.py
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@ -6,16 +6,16 @@ import munch
import torch
import torchvision
from munch import munchify
import models.archs.stylegan.stylegan2_lucidrains as stylegan2
import models.stylegan.stylegan2_lucidrains as stylegan2
import models.archs.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
import models.archs.discriminator_vgg_arch as SRGAN_arch
import models.archs.feature_arch as feature_arch
from models.archs import srg2_classic
from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
from models.archs.tecogan.teco_resgen import TecoGen
import models.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.RRDBNet_arch as RRDBNet_arch
import models.SwitchedResidualGenerator_arch as SwitchedGen_arch
import models.discriminator_vgg_arch as SRGAN_arch
import models.feature_arch as feature_arch
from models import srg2_classic
from models.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
from models.tecogan.teco_resgen import TecoGen
from utils.util import opt_get
logger = logging.getLogger('base')
@ -30,7 +30,7 @@ def define_G(opt, opt_net, scale=None):
if which_model == 'RRDBNetBypass':
block = RRDBNet_arch.RRDBWithBypass
elif which_model == 'RRDBNetLambda':
from models.archs.lambda_rrdb import LambdaRRDB
from models.lambda_rrdb import LambdaRRDB
block = LambdaRRDB
else:
block = RRDBNet_arch.RRDB
@ -62,7 +62,7 @@ def define_G(opt, opt_net, scale=None):
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "flownet2":
from models.archs.flownet2 import FlowNet2
from models.flownet2 import FlowNet2
ld = 'load_path' in opt_net.keys()
args = munch.Munch({'fp16': False, 'rgb_max': 1.0, 'checkpoint': not ld})
netG = FlowNet2(args)
@ -85,12 +85,12 @@ def define_G(opt, opt_net, scale=None):
netG = stylegan2.StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'],
style_depth=opt_net['style_depth'], structure_input=is_structured,
attn_layers=attn)
elif which_model == 'srflow_orig':
from models.archs.srflow_orig import SRFlowNet_arch
elif which_model == 'srflow':
from models.srflow import SRFlowNet_arch
netG = SRFlowNet_arch.SRFlowNet(in_nc=3, out_nc=3, nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
K=opt_net['K'], opt=opt)
elif which_model == 'rrdb_latent_wrapper':
from models.archs.srflow_orig.RRDBNet_arch import RRDBLatentWrapper
from models.srflow.RRDBNet_arch import RRDBLatentWrapper
netG = RRDBLatentWrapper(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], with_bypass=opt_net['with_bypass'],
blocks=opt_net['blocks_for_latent'], scale=opt_net['scale'], pretrain_rrdb_path=opt_net['pretrain_path'])
@ -100,29 +100,29 @@ def define_G(opt, opt_net, scale=None):
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], scale=opt_net['scale'],
headless=True, output_mode=output_mode)
elif which_model == 'rrdb_srflow':
from models.archs.srflow_orig.RRDBNet_arch import RRDBNet
from models.srflow.RRDBNet_arch import RRDBNet
netG = RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
initial_conv_stride=opt_net['initial_stride'])
elif which_model == 'igpt2':
from models.archs.transformers.igpt.gpt2 import iGPT2
from models.transformers.igpt.gpt2 import iGPT2
netG = iGPT2(opt_net['embed_dim'], opt_net['num_heads'], opt_net['num_layers'], opt_net['num_pixels'] ** 2, opt_net['num_vocab'], centroids_file=opt_net['centroids_file'])
elif which_model == 'byol':
from models.archs.byol.byol_model_wrapper import BYOL
from models.byol.byol_model_wrapper import BYOL
subnet = define_G(opt, opt_net['subnet'])
netG = BYOL(subnet, opt_net['image_size'], opt_net['hidden_layer'],
structural_mlp=opt_get(opt_net, ['use_structural_mlp'], False))
elif which_model == 'structural_byol':
from models.archs.byol.byol_structural import StructuralBYOL
from models.byol.byol_structural import StructuralBYOL
subnet = define_G(opt, opt_net['subnet'])
netG = StructuralBYOL(subnet, opt_net['image_size'], opt_net['hidden_layer'],
pretrained_state_dict=opt_get(opt_net, ["pretrained_path"]),
freeze_until=opt_get(opt_net, ['freeze_until'], 0))
elif which_model == 'spinenet':
from models.archs.spinenet_arch import SpineNet
from models.spinenet_arch import SpineNet
netG = SpineNet(str(opt_net['arch']), in_channels=3, use_input_norm=opt_net['use_input_norm'])
elif which_model == 'spinenet_with_logits':
from models.archs.spinenet_arch import SpinenetWithLogits
from models.spinenet_arch import SpinenetWithLogits
netG = SpinenetWithLogits(str(opt_net['arch']), opt_net['output_to_attach'], opt_net['num_labels'],
in_channels=3, use_input_norm=opt_net['use_input_norm'])
else:

4
codes/utils/numeric_stability.py
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@ -1,7 +1,7 @@
import torch
from torch import nn
import models.archs.SwitchedResidualGenerator_arch as srg
import models.archs.discriminator_vgg_arch as disc
import models.SwitchedResidualGenerator_arch as srg
import models.discriminator_vgg_arch as disc
import functools
blacklisted_modules = [nn.Conv2d, nn.ReLU, nn.LeakyReLU, nn.BatchNorm2d, nn.Softmax]