Add unet with latent guide
This is a diffusion network that uses both a LQ image and a reference sample HQ image that is compressed into a latent vector to perform upsampling The hope is that we can steer the upsampling network with sample images.
This commit is contained in:
James Betker
parent
0ded106562
commit
46e9f62be0
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@ -208,21 +208,27 @@ class ImageFolderDataset:
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# This assumes the output format generated by the tiled image generation scripts included with DLAS. Specifically,
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# all image read by this dataset are assumed to be in subfolders with other tiles from the same source image. When
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# this option is set, another random image from the same folder is selected and returned as the alt image.
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sel_path = self.image_paths[item]
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other_images = random.shuffle(os.listdir(sel_path))
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sel_path = os.path.dirname(self.image_paths[item])
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other_images = os.listdir(sel_path)
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# Assume that the directory contains at least <image>, <ref.jpg>, <centers.pt>
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if len(other_images) <= 3:
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alt_hq = hq # This is a fallback in case an alt image can't be found.
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else:
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for oi in other_images:
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if oi == sel_path or 'ref.' in oi or 'centers.pt' in oi:
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continue
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alt_hq = util.read_img(None, oi, rgb=True)
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alt_hs = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
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out_dict['has_alt'] = True
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try:
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if len(other_images) <= 3:
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alt_hq = hq # This is a fallback in case an alt image can't be found.
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else:
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random.shuffle(other_images)
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for oi in other_images:
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if oi == os.path.basename(self.image_paths[item]) or 'ref.' in oi or 'centers.pt' in oi:
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continue
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alt_hq = util.read_img(None, os.path.join(sel_path, oi), rgb=True)
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alt_hs = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
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except:
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alt_hq = hq
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print(f"Error with {self.image_paths[item]}")
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out_dict['has_alt'] = True
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out_dict['alt_hq'] = alt_hq
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if not self.skip_lq:
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lqs, ent = self.synthesize_lq(for_lq)
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ls = lqs[0]
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@ -263,13 +269,14 @@ if __name__ == '__main__':
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'scale': 2,
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'corrupt_before_downsize': True,
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'fetch_alt_image': False,
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'fetch_alt_tiled_image': True,
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'disable_flip': True,
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'fixed_corruptions': [ 'jpeg-medium' ],
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'num_corrupts_per_image': 0,
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'corruption_blur_scale': 0
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}
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ds = DataLoader(ImageFolderDataset(opt), shuffle=True, num_workers=4, batch_size=64)
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ds = DataLoader(ImageFolderDataset(opt), shuffle=True, num_workers=0, batch_size=64)
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import os
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output_path = 'F:\\tmp'
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os.makedirs(output_path, exist_ok=True)
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@ -1,6 +1,7 @@
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from abc import abstractmethod
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import math
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from typing import Union, Type, Callable, Optional, List
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import numpy as np
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import torch
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@ -8,6 +9,10 @@ import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision # For debugging, not actually used.
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from kornia.augmentation import ColorJitter
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from torch import Tensor
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from torchvision.models import resnet50
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from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1
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from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
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from models.diffusion.nn import (
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@ -677,231 +682,159 @@ class SuperResModel(UNetModel):
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corruption_factor = torch.zeros((b, self.num_corruptions, new_height, new_width), dtype=torch.float, device=x.device)
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upsampled = torch.cat([upsampled, corruption_factor], dim=1)
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x = th.cat([x, upsampled], dim=1)
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res = super().forward(x, timesteps, latent, **kwargs)
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res = super().forward(x, latent, timesteps, **kwargs)
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return res
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class EncoderUNetModel(nn.Module):
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"""
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The half UNet model with attention and timestep embedding.
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For usage, see UNet.
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"""
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class ResNetEncoder(nn.Module):
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def __init__(
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self,
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image_size,
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in_channels,
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model_channels,
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out_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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pool="adaptive",
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):
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block: Type[Union[BasicBlock, Bottleneck]] = Bottleneck,
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layers: List[int] = [3, 4, 6, 3],
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depth: int = 4,
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output_dim: int = 512,
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zero_init_residual: bool = False,
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groups: int = 1,
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width_per_group: int = 64,
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replace_stride_with_dilation: Optional[List[bool]] = None,
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norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(ResNetEncoder, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = norm_layer(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.depth = depth
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
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dilate=replace_stride_with_dilation[0])
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f=128
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if self.depth > 2:
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
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dilate=replace_stride_with_dilation[1])
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f=256
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if self.depth > 3:
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
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dilate=replace_stride_with_dilation[2])
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f=512
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(f * block.expansion, output_dim)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
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def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
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stride: int = 1, dilate: bool = False) -> nn.Sequential:
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer))
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return nn.Sequential(*layers)
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def _forward_impl(self, x: Tensor) -> Tensor:
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# See note [TorchScript super()]
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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if self.depth > 2:
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x = self.layer3(x)
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if self.depth > 3:
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x = self.layer4(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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def forward(self, x: Tensor) -> Tensor:
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return self._forward_impl(x)
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class UnetWithBuiltInLatentEncoder(nn.Module):
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def __init__(self, **kwargs):
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depth_map = {
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256: 4,
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128: 3,
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64: 2
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}
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super().__init__()
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self.encoder = ResNetEncoder(depth=depth_map[kwargs['image_size']])
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self.lq_jitter = ColorJitter(.05, .05, .05, .05)
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self.unet = SuperResModel(**kwargs)
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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def forward(self, x, timesteps, alt_hq, low_res=None, **kwargs):
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latent = self.encoder(alt_hq)
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low_res = self.lq_jitter((low_res+1)/2)*2-1
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return self.unet(x, timesteps, latent, low_res, **kwargs)
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.dtype = th.float16 if use_fp16 else th.float32
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for _ in range(num_res_blocks):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=mult * model_channels,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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layers.append(
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AttentionBlock(
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ch,
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num_heads=num_heads,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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ds *= 2
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self._feature_size += ch
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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AttentionBlock(
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ch,
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num_heads=num_heads,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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)
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self._feature_size += ch
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self.pool = pool
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if pool == "adaptive":
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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nn.AdaptiveAvgPool2d((1, 1)),
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zero_module(conv_nd(dims, ch, out_channels, 1)),
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nn.Flatten(),
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)
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elif pool == "attention":
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assert num_head_channels != -1
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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AttentionPool2d(
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(image_size // ds), ch, num_head_channels, out_channels
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),
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)
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elif pool == "spatial":
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self.out = nn.Sequential(
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nn.Linear(self._feature_size, 2048),
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nn.ReLU(),
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nn.Linear(2048, self.out_channels),
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)
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elif pool == "spatial_v2":
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self.out = nn.Sequential(
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nn.Linear(self._feature_size, 2048),
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normalization(2048),
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nn.SiLU(),
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nn.Linear(2048, self.out_channels),
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)
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else:
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raise NotImplementedError(f"Unexpected {pool} pooling")
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def convert_to_fp16(self):
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"""
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Convert the torso of the model to float16.
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"""
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self.input_blocks.apply(convert_module_to_f16)
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self.middle_block.apply(convert_module_to_f16)
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def convert_to_fp32(self):
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"""
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Convert the torso of the model to float32.
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"""
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self.input_blocks.apply(convert_module_to_f32)
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self.middle_block.apply(convert_module_to_f32)
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def forward(self, x, timesteps):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:return: an [N x K] Tensor of outputs.
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"""
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emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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results = []
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h = x.type(self.dtype)
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for module in self.input_blocks:
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h = module(h, emb)
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if self.pool.startswith("spatial"):
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results.append(h.type(x.dtype).mean(dim=(2, 3)))
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h = self.middle_block(h, emb)
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if self.pool.startswith("spatial"):
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results.append(h.type(x.dtype).mean(dim=(2, 3)))
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h = th.cat(results, axis=-1)
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return self.out(h)
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else:
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h = h.type(x.dtype)
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return self.out(h)
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@register_model
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def register_unet_diffusion(opt_net, opt):
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return SuperResModel(**opt_net['args'])
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def register_unet_diffusion_latent_guide(opt_net, opt):
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return UnetWithBuiltInLatentEncoder(**opt_net['args'])
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if __name__ == '__main__':
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attention_ds = []
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for res in "16,8".split(","):
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attention_ds.append(128 // int(res))
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srm = SuperResModel(image_size=128, in_channels=3, model_channels=64, out_channels=3, num_res_blocks=1, attention_resolutions=attention_ds, num_heads=4,
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srm = UnetWithBuiltInLatentEncoder(image_size=64, in_channels=3, model_channels=64, out_channels=3, num_res_blocks=1, attention_resolutions=attention_ds, num_heads=4,
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num_heads_upsample=-1, use_scale_shift_norm=True)
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x = torch.randn(1,3,128,128)
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x = torch.randn(1,3,64,64)
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alt_x = torch.randn(1,3,64,64)
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l = torch.randn(1,3,32,32)
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ts = torch.LongTensor([555])
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y = srm(x, ts, low_res=l)
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y = srm(x, ts, alt_x, low_res=l)
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print(y.shape, y.mean(), y.std(), y.min(), y.max())
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|
|
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|
|
@ -299,7 +299,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
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||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_quality_detectors/train_resnet_blur.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_latent_unet_diffusion_sm.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user