1946 lines
61 KiB
Python
1946 lines
61 KiB
Python
import os
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import random
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import cv2
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from iopaint.helper import (
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load_model,
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get_cache_path_by_url,
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norm_img,
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download_model,
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)
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from iopaint.schema import InpaintRequest
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from .base import InpaintModel
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from .utils import (
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setup_filter,
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Conv2dLayer,
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FullyConnectedLayer,
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conv2d_resample,
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bias_act,
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upsample2d,
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activation_funcs,
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MinibatchStdLayer,
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to_2tuple,
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normalize_2nd_moment,
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set_seed,
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)
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class ModulatedConv2d(nn.Module):
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def __init__(
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self,
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in_channels, # Number of input channels.
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out_channels, # Number of output channels.
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kernel_size, # Width and height of the convolution kernel.
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style_dim, # dimension of the style code
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demodulate=True, # perfrom demodulation
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up=1, # Integer upsampling factor.
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down=1, # Integer downsampling factor.
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resample_filter=[
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1,
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3,
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3,
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1,
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], # Low-pass filter to apply when resampling activations.
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conv_clamp=None, # Clamp the output to +-X, None = disable clamping.
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):
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super().__init__()
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self.demodulate = demodulate
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self.weight = torch.nn.Parameter(
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torch.randn([1, out_channels, in_channels, kernel_size, kernel_size])
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)
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
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self.padding = self.kernel_size // 2
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self.up = up
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self.down = down
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self.register_buffer("resample_filter", setup_filter(resample_filter))
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self.conv_clamp = conv_clamp
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self.affine = FullyConnectedLayer(style_dim, in_channels, bias_init=1)
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def forward(self, x, style):
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batch, in_channels, height, width = x.shape
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style = self.affine(style).view(batch, 1, in_channels, 1, 1)
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weight = self.weight * self.weight_gain * style
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if self.demodulate:
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decoefs = (weight.pow(2).sum(dim=[2, 3, 4]) + 1e-8).rsqrt()
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weight = weight * decoefs.view(batch, self.out_channels, 1, 1, 1)
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weight = weight.view(
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batch * self.out_channels, in_channels, self.kernel_size, self.kernel_size
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)
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x = x.view(1, batch * in_channels, height, width)
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x = conv2d_resample(
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x=x,
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w=weight,
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f=self.resample_filter,
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up=self.up,
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down=self.down,
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padding=self.padding,
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groups=batch,
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)
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out = x.view(batch, self.out_channels, *x.shape[2:])
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return out
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class StyleConv(torch.nn.Module):
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def __init__(
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self,
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in_channels, # Number of input channels.
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out_channels, # Number of output channels.
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style_dim, # Intermediate latent (W) dimensionality.
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resolution, # Resolution of this layer.
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kernel_size=3, # Convolution kernel size.
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up=1, # Integer upsampling factor.
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use_noise=False, # Enable noise input?
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activation="lrelu", # Activation function: 'relu', 'lrelu', etc.
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resample_filter=[
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1,
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3,
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3,
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1,
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], # Low-pass filter to apply when resampling activations.
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conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping.
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demodulate=True, # perform demodulation
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):
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super().__init__()
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self.conv = ModulatedConv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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style_dim=style_dim,
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demodulate=demodulate,
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up=up,
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resample_filter=resample_filter,
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conv_clamp=conv_clamp,
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)
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self.use_noise = use_noise
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self.resolution = resolution
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if use_noise:
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self.register_buffer("noise_const", torch.randn([resolution, resolution]))
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self.noise_strength = torch.nn.Parameter(torch.zeros([]))
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self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
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self.activation = activation
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self.act_gain = activation_funcs[activation].def_gain
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self.conv_clamp = conv_clamp
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def forward(self, x, style, noise_mode="random", gain=1):
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x = self.conv(x, style)
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assert noise_mode in ["random", "const", "none"]
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if self.use_noise:
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if noise_mode == "random":
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xh, xw = x.size()[-2:]
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noise = (
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torch.randn([x.shape[0], 1, xh, xw], device=x.device)
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* self.noise_strength
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)
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if noise_mode == "const":
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noise = self.noise_const * self.noise_strength
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x = x + noise
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act_gain = self.act_gain * gain
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act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
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out = bias_act(
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x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp
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)
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return out
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class ToRGB(torch.nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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style_dim,
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kernel_size=1,
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resample_filter=[1, 3, 3, 1],
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conv_clamp=None,
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demodulate=False,
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):
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super().__init__()
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self.conv = ModulatedConv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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style_dim=style_dim,
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demodulate=demodulate,
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resample_filter=resample_filter,
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conv_clamp=conv_clamp,
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)
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self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
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self.register_buffer("resample_filter", setup_filter(resample_filter))
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self.conv_clamp = conv_clamp
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def forward(self, x, style, skip=None):
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x = self.conv(x, style)
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out = bias_act(x, self.bias, clamp=self.conv_clamp)
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if skip is not None:
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if skip.shape != out.shape:
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skip = upsample2d(skip, self.resample_filter)
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out = out + skip
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return out
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def get_style_code(a, b):
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return torch.cat([a, b], dim=1)
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class DecBlockFirst(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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activation,
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style_dim,
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use_noise,
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demodulate,
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img_channels,
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):
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super().__init__()
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self.fc = FullyConnectedLayer(
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in_features=in_channels * 2,
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out_features=in_channels * 4**2,
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activation=activation,
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)
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self.conv = StyleConv(
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in_channels=in_channels,
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out_channels=out_channels,
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style_dim=style_dim,
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resolution=4,
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kernel_size=3,
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use_noise=use_noise,
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activation=activation,
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demodulate=demodulate,
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)
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self.toRGB = ToRGB(
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in_channels=out_channels,
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out_channels=img_channels,
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style_dim=style_dim,
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kernel_size=1,
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demodulate=False,
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)
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def forward(self, x, ws, gs, E_features, noise_mode="random"):
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x = self.fc(x).view(x.shape[0], -1, 4, 4)
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x = x + E_features[2]
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style = get_style_code(ws[:, 0], gs)
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x = self.conv(x, style, noise_mode=noise_mode)
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style = get_style_code(ws[:, 1], gs)
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img = self.toRGB(x, style, skip=None)
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return x, img
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class DecBlockFirstV2(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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activation,
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style_dim,
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use_noise,
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demodulate,
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img_channels,
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):
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super().__init__()
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self.conv0 = Conv2dLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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activation=activation,
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)
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self.conv1 = StyleConv(
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in_channels=in_channels,
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out_channels=out_channels,
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style_dim=style_dim,
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resolution=4,
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kernel_size=3,
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use_noise=use_noise,
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activation=activation,
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demodulate=demodulate,
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)
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self.toRGB = ToRGB(
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in_channels=out_channels,
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out_channels=img_channels,
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style_dim=style_dim,
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kernel_size=1,
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demodulate=False,
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)
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def forward(self, x, ws, gs, E_features, noise_mode="random"):
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# x = self.fc(x).view(x.shape[0], -1, 4, 4)
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x = self.conv0(x)
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x = x + E_features[2]
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style = get_style_code(ws[:, 0], gs)
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x = self.conv1(x, style, noise_mode=noise_mode)
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style = get_style_code(ws[:, 1], gs)
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img = self.toRGB(x, style, skip=None)
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return x, img
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class DecBlock(nn.Module):
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def __init__(
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self,
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res,
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in_channels,
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out_channels,
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activation,
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style_dim,
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use_noise,
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demodulate,
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img_channels,
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): # res = 2, ..., resolution_log2
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super().__init__()
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self.res = res
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self.conv0 = StyleConv(
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in_channels=in_channels,
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out_channels=out_channels,
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style_dim=style_dim,
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resolution=2**res,
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kernel_size=3,
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up=2,
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use_noise=use_noise,
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activation=activation,
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demodulate=demodulate,
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)
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self.conv1 = StyleConv(
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in_channels=out_channels,
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out_channels=out_channels,
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style_dim=style_dim,
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resolution=2**res,
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kernel_size=3,
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use_noise=use_noise,
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activation=activation,
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demodulate=demodulate,
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)
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self.toRGB = ToRGB(
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in_channels=out_channels,
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out_channels=img_channels,
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style_dim=style_dim,
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kernel_size=1,
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demodulate=False,
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)
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def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
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style = get_style_code(ws[:, self.res * 2 - 5], gs)
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x = self.conv0(x, style, noise_mode=noise_mode)
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x = x + E_features[self.res]
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style = get_style_code(ws[:, self.res * 2 - 4], gs)
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x = self.conv1(x, style, noise_mode=noise_mode)
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style = get_style_code(ws[:, self.res * 2 - 3], gs)
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img = self.toRGB(x, style, skip=img)
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return x, img
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class MappingNet(torch.nn.Module):
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def __init__(
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self,
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z_dim, # Input latent (Z) dimensionality, 0 = no latent.
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c_dim, # Conditioning label (C) dimensionality, 0 = no label.
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w_dim, # Intermediate latent (W) dimensionality.
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num_ws, # Number of intermediate latents to output, None = do not broadcast.
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num_layers=8, # Number of mapping layers.
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embed_features=None, # Label embedding dimensionality, None = same as w_dim.
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layer_features=None, # Number of intermediate features in the mapping layers, None = same as w_dim.
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activation="lrelu", # Activation function: 'relu', 'lrelu', etc.
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lr_multiplier=0.01, # Learning rate multiplier for the mapping layers.
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w_avg_beta=0.995, # Decay for tracking the moving average of W during training, None = do not track.
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torch_dtype=torch.float32,
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):
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super().__init__()
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self.z_dim = z_dim
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self.c_dim = c_dim
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self.w_dim = w_dim
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self.num_ws = num_ws
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self.num_layers = num_layers
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self.w_avg_beta = w_avg_beta
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self.torch_dtype = torch_dtype
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if embed_features is None:
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embed_features = w_dim
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if c_dim == 0:
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embed_features = 0
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if layer_features is None:
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layer_features = w_dim
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features_list = (
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[z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
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)
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if c_dim > 0:
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self.embed = FullyConnectedLayer(c_dim, embed_features)
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for idx in range(num_layers):
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in_features = features_list[idx]
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out_features = features_list[idx + 1]
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layer = FullyConnectedLayer(
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in_features,
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out_features,
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activation=activation,
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lr_multiplier=lr_multiplier,
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)
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setattr(self, f"fc{idx}", layer)
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if num_ws is not None and w_avg_beta is not None:
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self.register_buffer("w_avg", torch.zeros([w_dim]))
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def forward(
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self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False
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):
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# Embed, normalize, and concat inputs.
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x = None
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if self.z_dim > 0:
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x = normalize_2nd_moment(z)
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if self.c_dim > 0:
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y = normalize_2nd_moment(self.embed(c))
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x = torch.cat([x, y], dim=1) if x is not None else y
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# Main layers.
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for idx in range(self.num_layers):
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layer = getattr(self, f"fc{idx}")
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x = layer(x)
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# Update moving average of W.
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if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
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self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
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# Broadcast.
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if self.num_ws is not None:
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x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
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# Apply truncation.
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if truncation_psi != 1:
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assert self.w_avg_beta is not None
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if self.num_ws is None or truncation_cutoff is None:
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x = self.w_avg.lerp(x, truncation_psi)
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else:
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x[:, :truncation_cutoff] = self.w_avg.lerp(
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x[:, :truncation_cutoff], truncation_psi
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)
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return x
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class DisFromRGB(nn.Module):
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def __init__(
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self, in_channels, out_channels, activation
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): # res = 2, ..., resolution_log2
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super().__init__()
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self.conv = Conv2dLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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activation=activation,
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)
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def forward(self, x):
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return self.conv(x)
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class DisBlock(nn.Module):
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def __init__(
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self, in_channels, out_channels, activation
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): # res = 2, ..., resolution_log2
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super().__init__()
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self.conv0 = Conv2dLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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activation=activation,
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)
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self.conv1 = Conv2dLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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down=2,
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activation=activation,
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)
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self.skip = Conv2dLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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down=2,
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bias=False,
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)
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def forward(self, x):
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skip = self.skip(x, gain=np.sqrt(0.5))
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x = self.conv0(x)
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x = self.conv1(x, gain=np.sqrt(0.5))
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out = skip + x
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return out
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class Discriminator(torch.nn.Module):
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def __init__(
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self,
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c_dim, # Conditioning label (C) dimensionality.
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img_resolution, # Input resolution.
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img_channels, # Number of input color channels.
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channel_base=32768, # Overall multiplier for the number of channels.
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channel_max=512, # Maximum number of channels in any layer.
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channel_decay=1,
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cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
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activation="lrelu",
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mbstd_group_size=4, # Group size for the minibatch standard deviation layer, None = entire minibatch.
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mbstd_num_channels=1, # Number of features for the minibatch standard deviation layer, 0 = disable.
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):
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super().__init__()
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self.c_dim = c_dim
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self.img_resolution = img_resolution
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self.img_channels = img_channels
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resolution_log2 = int(np.log2(img_resolution))
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assert img_resolution == 2**resolution_log2 and img_resolution >= 4
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self.resolution_log2 = resolution_log2
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def nf(stage):
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return np.clip(
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int(channel_base / 2 ** (stage * channel_decay)), 1, channel_max
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)
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if cmap_dim == None:
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cmap_dim = nf(2)
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if c_dim == 0:
|
|
cmap_dim = 0
|
|
self.cmap_dim = cmap_dim
|
|
|
|
if c_dim > 0:
|
|
self.mapping = MappingNet(
|
|
z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None
|
|
)
|
|
|
|
Dis = [DisFromRGB(img_channels + 1, nf(resolution_log2), activation)]
|
|
for res in range(resolution_log2, 2, -1):
|
|
Dis.append(DisBlock(nf(res), nf(res - 1), activation))
|
|
|
|
if mbstd_num_channels > 0:
|
|
Dis.append(
|
|
MinibatchStdLayer(
|
|
group_size=mbstd_group_size, num_channels=mbstd_num_channels
|
|
)
|
|
)
|
|
Dis.append(
|
|
Conv2dLayer(
|
|
nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation
|
|
)
|
|
)
|
|
self.Dis = nn.Sequential(*Dis)
|
|
|
|
self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation)
|
|
self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
|
|
|
|
def forward(self, images_in, masks_in, c):
|
|
x = torch.cat([masks_in - 0.5, images_in], dim=1)
|
|
x = self.Dis(x)
|
|
x = self.fc1(self.fc0(x.flatten(start_dim=1)))
|
|
|
|
if self.c_dim > 0:
|
|
cmap = self.mapping(None, c)
|
|
|
|
if self.cmap_dim > 0:
|
|
x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
|
|
|
|
return x
|
|
|
|
|
|
def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512):
|
|
NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512}
|
|
return NF[2**stage]
|
|
|
|
|
|
class Mlp(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_features,
|
|
hidden_features=None,
|
|
out_features=None,
|
|
act_layer=nn.GELU,
|
|
drop=0.0,
|
|
):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_features = hidden_features or in_features
|
|
self.fc1 = FullyConnectedLayer(
|
|
in_features=in_features, out_features=hidden_features, activation="lrelu"
|
|
)
|
|
self.fc2 = FullyConnectedLayer(
|
|
in_features=hidden_features, out_features=out_features
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.fc1(x)
|
|
x = self.fc2(x)
|
|
return x
|
|
|
|
|
|
def window_partition(x, window_size):
|
|
"""
|
|
Args:
|
|
x: (B, H, W, C)
|
|
window_size (int): window size
|
|
Returns:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
"""
|
|
B, H, W, C = x.shape
|
|
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
|
windows = (
|
|
x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
|
)
|
|
return windows
|
|
|
|
|
|
def window_reverse(windows, window_size: int, H: int, W: int):
|
|
"""
|
|
Args:
|
|
windows: (num_windows*B, window_size, window_size, C)
|
|
window_size (int): Window size
|
|
H (int): Height of image
|
|
W (int): Width of image
|
|
Returns:
|
|
x: (B, H, W, C)
|
|
"""
|
|
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
|
# B = windows.shape[0] / (H * W / window_size / window_size)
|
|
x = windows.view(
|
|
B, H // window_size, W // window_size, window_size, window_size, -1
|
|
)
|
|
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
|
return x
|
|
|
|
|
|
class Conv2dLayerPartial(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels, # Number of input channels.
|
|
out_channels, # Number of output channels.
|
|
kernel_size, # Width and height of the convolution kernel.
|
|
bias=True, # Apply additive bias before the activation function?
|
|
activation="linear", # Activation function: 'relu', 'lrelu', etc.
|
|
up=1, # Integer upsampling factor.
|
|
down=1, # Integer downsampling factor.
|
|
resample_filter=[
|
|
1,
|
|
3,
|
|
3,
|
|
1,
|
|
], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp=None, # Clamp the output to +-X, None = disable clamping.
|
|
trainable=True, # Update the weights of this layer during training?
|
|
):
|
|
super().__init__()
|
|
self.conv = Conv2dLayer(
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
bias,
|
|
activation,
|
|
up,
|
|
down,
|
|
resample_filter,
|
|
conv_clamp,
|
|
trainable,
|
|
)
|
|
|
|
self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size)
|
|
self.slide_winsize = kernel_size**2
|
|
self.stride = down
|
|
self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0
|
|
|
|
def forward(self, x, mask=None):
|
|
if mask is not None:
|
|
with torch.no_grad():
|
|
if self.weight_maskUpdater.type() != x.type():
|
|
self.weight_maskUpdater = self.weight_maskUpdater.to(x)
|
|
update_mask = F.conv2d(
|
|
mask,
|
|
self.weight_maskUpdater,
|
|
bias=None,
|
|
stride=self.stride,
|
|
padding=self.padding,
|
|
)
|
|
mask_ratio = self.slide_winsize / (update_mask.to(torch.float32) + 1e-8)
|
|
update_mask = torch.clamp(update_mask, 0, 1) # 0 or 1
|
|
mask_ratio = torch.mul(mask_ratio, update_mask).to(x.dtype)
|
|
x = self.conv(x)
|
|
x = torch.mul(x, mask_ratio)
|
|
return x, update_mask
|
|
else:
|
|
x = self.conv(x)
|
|
return x, None
|
|
|
|
|
|
class WindowAttention(nn.Module):
|
|
r"""Window based multi-head self attention (W-MSA) module with relative position bias.
|
|
It supports both of shifted and non-shifted window.
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
window_size (tuple[int]): The height and width of the window.
|
|
num_heads (int): Number of attention heads.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
|
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
|
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
window_size,
|
|
num_heads,
|
|
down_ratio=1,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
attn_drop=0.0,
|
|
proj_drop=0.0,
|
|
):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.window_size = window_size # Wh, Ww
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.q = FullyConnectedLayer(in_features=dim, out_features=dim)
|
|
self.k = FullyConnectedLayer(in_features=dim, out_features=dim)
|
|
self.v = FullyConnectedLayer(in_features=dim, out_features=dim)
|
|
self.proj = FullyConnectedLayer(in_features=dim, out_features=dim)
|
|
|
|
self.softmax = nn.Softmax(dim=-1)
|
|
|
|
def forward(self, x, mask_windows=None, mask=None):
|
|
"""
|
|
Args:
|
|
x: input features with shape of (num_windows*B, N, C)
|
|
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
|
"""
|
|
B_, N, C = x.shape
|
|
norm_x = F.normalize(x, p=2.0, dim=-1, eps=torch.finfo(x.dtype).eps)
|
|
q = (
|
|
self.q(norm_x)
|
|
.reshape(B_, N, self.num_heads, C // self.num_heads)
|
|
.permute(0, 2, 1, 3)
|
|
)
|
|
k = (
|
|
self.k(norm_x)
|
|
.view(B_, -1, self.num_heads, C // self.num_heads)
|
|
.permute(0, 2, 3, 1)
|
|
)
|
|
v = (
|
|
self.v(x)
|
|
.view(B_, -1, self.num_heads, C // self.num_heads)
|
|
.permute(0, 2, 1, 3)
|
|
)
|
|
|
|
attn = (q @ k) * self.scale
|
|
|
|
if mask is not None:
|
|
nW = mask.shape[0]
|
|
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
|
|
1
|
|
).unsqueeze(0)
|
|
attn = attn.view(-1, self.num_heads, N, N)
|
|
|
|
if mask_windows is not None:
|
|
attn_mask_windows = mask_windows.squeeze(-1).unsqueeze(1).unsqueeze(1)
|
|
attn = attn + attn_mask_windows.masked_fill(
|
|
attn_mask_windows == 0, float(-100.0)
|
|
).masked_fill(attn_mask_windows == 1, float(0.0))
|
|
with torch.no_grad():
|
|
mask_windows = torch.clamp(
|
|
torch.sum(mask_windows, dim=1, keepdim=True), 0, 1
|
|
).repeat(1, N, 1)
|
|
|
|
attn = self.softmax(attn)
|
|
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
|
x = self.proj(x)
|
|
return x, mask_windows
|
|
|
|
|
|
class SwinTransformerBlock(nn.Module):
|
|
r"""Swin Transformer Block.
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
input_resolution (tuple[int]): Input resulotion.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): Window size.
|
|
shift_size (int): Shift size for SW-MSA.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
|
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
input_resolution,
|
|
num_heads,
|
|
down_ratio=1,
|
|
window_size=7,
|
|
shift_size=0,
|
|
mlp_ratio=4.0,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.0,
|
|
attn_drop=0.0,
|
|
drop_path=0.0,
|
|
act_layer=nn.GELU,
|
|
norm_layer=nn.LayerNorm,
|
|
):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
self.num_heads = num_heads
|
|
self.window_size = window_size
|
|
self.shift_size = shift_size
|
|
self.mlp_ratio = mlp_ratio
|
|
if min(self.input_resolution) <= self.window_size:
|
|
# if window size is larger than input resolution, we don't partition windows
|
|
self.shift_size = 0
|
|
self.window_size = min(self.input_resolution)
|
|
assert (
|
|
0 <= self.shift_size < self.window_size
|
|
), "shift_size must in 0-window_size"
|
|
|
|
if self.shift_size > 0:
|
|
down_ratio = 1
|
|
self.attn = WindowAttention(
|
|
dim,
|
|
window_size=to_2tuple(self.window_size),
|
|
num_heads=num_heads,
|
|
down_ratio=down_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop,
|
|
)
|
|
|
|
self.fuse = FullyConnectedLayer(
|
|
in_features=dim * 2, out_features=dim, activation="lrelu"
|
|
)
|
|
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(
|
|
in_features=dim,
|
|
hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop,
|
|
)
|
|
|
|
if self.shift_size > 0:
|
|
attn_mask = self.calculate_mask(self.input_resolution)
|
|
else:
|
|
attn_mask = None
|
|
|
|
self.register_buffer("attn_mask", attn_mask)
|
|
|
|
def calculate_mask(self, x_size):
|
|
# calculate attention mask for SW-MSA
|
|
H, W = x_size
|
|
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
|
h_slices = (
|
|
slice(0, -self.window_size),
|
|
slice(-self.window_size, -self.shift_size),
|
|
slice(-self.shift_size, None),
|
|
)
|
|
w_slices = (
|
|
slice(0, -self.window_size),
|
|
slice(-self.window_size, -self.shift_size),
|
|
slice(-self.shift_size, None),
|
|
)
|
|
cnt = 0
|
|
for h in h_slices:
|
|
for w in w_slices:
|
|
img_mask[:, h, w, :] = cnt
|
|
cnt += 1
|
|
|
|
mask_windows = window_partition(
|
|
img_mask, self.window_size
|
|
) # nW, window_size, window_size, 1
|
|
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
|
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
|
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
|
|
attn_mask == 0, float(0.0)
|
|
)
|
|
|
|
return attn_mask
|
|
|
|
def forward(self, x, x_size, mask=None):
|
|
# H, W = self.input_resolution
|
|
H, W = x_size
|
|
B, L, C = x.shape
|
|
# assert L == H * W, "input feature has wrong size"
|
|
|
|
shortcut = x
|
|
x = x.view(B, H, W, C)
|
|
if mask is not None:
|
|
mask = mask.view(B, H, W, 1)
|
|
|
|
# cyclic shift
|
|
if self.shift_size > 0:
|
|
shifted_x = torch.roll(
|
|
x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
|
|
)
|
|
if mask is not None:
|
|
shifted_mask = torch.roll(
|
|
mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
|
|
)
|
|
else:
|
|
shifted_x = x
|
|
if mask is not None:
|
|
shifted_mask = mask
|
|
|
|
# partition windows
|
|
x_windows = window_partition(
|
|
shifted_x, self.window_size
|
|
) # nW*B, window_size, window_size, C
|
|
x_windows = x_windows.view(
|
|
-1, self.window_size * self.window_size, C
|
|
) # nW*B, window_size*window_size, C
|
|
if mask is not None:
|
|
mask_windows = window_partition(shifted_mask, self.window_size)
|
|
mask_windows = mask_windows.view(-1, self.window_size * self.window_size, 1)
|
|
else:
|
|
mask_windows = None
|
|
|
|
# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
|
|
if self.input_resolution == x_size:
|
|
attn_windows, mask_windows = self.attn(
|
|
x_windows, mask_windows, mask=self.attn_mask
|
|
) # nW*B, window_size*window_size, C
|
|
else:
|
|
attn_windows, mask_windows = self.attn(
|
|
x_windows,
|
|
mask_windows,
|
|
mask=self.calculate_mask(x_size).to(x.dtype).to(x.device),
|
|
) # nW*B, window_size*window_size, C
|
|
|
|
# merge windows
|
|
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
|
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
|
if mask is not None:
|
|
mask_windows = mask_windows.view(-1, self.window_size, self.window_size, 1)
|
|
shifted_mask = window_reverse(mask_windows, self.window_size, H, W)
|
|
|
|
# reverse cyclic shift
|
|
if self.shift_size > 0:
|
|
x = torch.roll(
|
|
shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
|
|
)
|
|
if mask is not None:
|
|
mask = torch.roll(
|
|
shifted_mask, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
|
|
)
|
|
else:
|
|
x = shifted_x
|
|
if mask is not None:
|
|
mask = shifted_mask
|
|
x = x.view(B, H * W, C)
|
|
if mask is not None:
|
|
mask = mask.view(B, H * W, 1)
|
|
|
|
# FFN
|
|
x = self.fuse(torch.cat([shortcut, x], dim=-1))
|
|
x = self.mlp(x)
|
|
|
|
return x, mask
|
|
|
|
|
|
class PatchMerging(nn.Module):
|
|
def __init__(self, in_channels, out_channels, down=2):
|
|
super().__init__()
|
|
self.conv = Conv2dLayerPartial(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
activation="lrelu",
|
|
down=down,
|
|
)
|
|
self.down = down
|
|
|
|
def forward(self, x, x_size, mask=None):
|
|
x = token2feature(x, x_size)
|
|
if mask is not None:
|
|
mask = token2feature(mask, x_size)
|
|
x, mask = self.conv(x, mask)
|
|
if self.down != 1:
|
|
ratio = 1 / self.down
|
|
x_size = (int(x_size[0] * ratio), int(x_size[1] * ratio))
|
|
x = feature2token(x)
|
|
if mask is not None:
|
|
mask = feature2token(mask)
|
|
return x, x_size, mask
|
|
|
|
|
|
class PatchUpsampling(nn.Module):
|
|
def __init__(self, in_channels, out_channels, up=2):
|
|
super().__init__()
|
|
self.conv = Conv2dLayerPartial(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
activation="lrelu",
|
|
up=up,
|
|
)
|
|
self.up = up
|
|
|
|
def forward(self, x, x_size, mask=None):
|
|
x = token2feature(x, x_size)
|
|
if mask is not None:
|
|
mask = token2feature(mask, x_size)
|
|
x, mask = self.conv(x, mask)
|
|
if self.up != 1:
|
|
x_size = (int(x_size[0] * self.up), int(x_size[1] * self.up))
|
|
x = feature2token(x)
|
|
if mask is not None:
|
|
mask = feature2token(mask)
|
|
return x, x_size, mask
|
|
|
|
|
|
class BasicLayer(nn.Module):
|
|
"""A basic Swin Transformer layer for one stage.
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
input_resolution (tuple[int]): Input resolution.
|
|
depth (int): Number of blocks.
|
|
num_heads (int): Number of attention heads.
|
|
window_size (int): Local window size.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
|
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
|
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop (float, optional): Dropout rate. Default: 0.0
|
|
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
|
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
|
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
|
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
input_resolution,
|
|
depth,
|
|
num_heads,
|
|
window_size,
|
|
down_ratio=1,
|
|
mlp_ratio=2.0,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop=0.0,
|
|
attn_drop=0.0,
|
|
drop_path=0.0,
|
|
norm_layer=nn.LayerNorm,
|
|
downsample=None,
|
|
use_checkpoint=False,
|
|
):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.input_resolution = input_resolution
|
|
self.depth = depth
|
|
self.use_checkpoint = use_checkpoint
|
|
|
|
# patch merging layer
|
|
if downsample is not None:
|
|
# self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
|
self.downsample = downsample
|
|
else:
|
|
self.downsample = None
|
|
|
|
# build blocks
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
SwinTransformerBlock(
|
|
dim=dim,
|
|
input_resolution=input_resolution,
|
|
num_heads=num_heads,
|
|
down_ratio=down_ratio,
|
|
window_size=window_size,
|
|
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop,
|
|
attn_drop=attn_drop,
|
|
drop_path=drop_path[i]
|
|
if isinstance(drop_path, list)
|
|
else drop_path,
|
|
norm_layer=norm_layer,
|
|
)
|
|
for i in range(depth)
|
|
]
|
|
)
|
|
|
|
self.conv = Conv2dLayerPartial(
|
|
in_channels=dim, out_channels=dim, kernel_size=3, activation="lrelu"
|
|
)
|
|
|
|
def forward(self, x, x_size, mask=None):
|
|
if self.downsample is not None:
|
|
x, x_size, mask = self.downsample(x, x_size, mask)
|
|
identity = x
|
|
for blk in self.blocks:
|
|
if self.use_checkpoint:
|
|
x, mask = checkpoint.checkpoint(blk, x, x_size, mask)
|
|
else:
|
|
x, mask = blk(x, x_size, mask)
|
|
if mask is not None:
|
|
mask = token2feature(mask, x_size)
|
|
x, mask = self.conv(token2feature(x, x_size), mask)
|
|
x = feature2token(x) + identity
|
|
if mask is not None:
|
|
mask = feature2token(mask)
|
|
return x, x_size, mask
|
|
|
|
|
|
class ToToken(nn.Module):
|
|
def __init__(self, in_channels=3, dim=128, kernel_size=5, stride=1):
|
|
super().__init__()
|
|
|
|
self.proj = Conv2dLayerPartial(
|
|
in_channels=in_channels,
|
|
out_channels=dim,
|
|
kernel_size=kernel_size,
|
|
activation="lrelu",
|
|
)
|
|
|
|
def forward(self, x, mask):
|
|
x, mask = self.proj(x, mask)
|
|
|
|
return x, mask
|
|
|
|
|
|
class EncFromRGB(nn.Module):
|
|
def __init__(
|
|
self, in_channels, out_channels, activation
|
|
): # res = 2, ..., resolution_log2
|
|
super().__init__()
|
|
self.conv0 = Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
activation=activation,
|
|
)
|
|
self.conv1 = Conv2dLayer(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.conv0(x)
|
|
x = self.conv1(x)
|
|
|
|
return x
|
|
|
|
|
|
class ConvBlockDown(nn.Module):
|
|
def __init__(
|
|
self, in_channels, out_channels, activation
|
|
): # res = 2, ..., resolution_log
|
|
super().__init__()
|
|
|
|
self.conv0 = Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
down=2,
|
|
)
|
|
self.conv1 = Conv2dLayer(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
)
|
|
|
|
def forward(self, x):
|
|
x = self.conv0(x)
|
|
x = self.conv1(x)
|
|
|
|
return x
|
|
|
|
|
|
def token2feature(x, x_size):
|
|
B, N, C = x.shape
|
|
h, w = x_size
|
|
x = x.permute(0, 2, 1).reshape(B, C, h, w)
|
|
return x
|
|
|
|
|
|
def feature2token(x):
|
|
B, C, H, W = x.shape
|
|
x = x.view(B, C, -1).transpose(1, 2)
|
|
return x
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
res_log2,
|
|
img_channels,
|
|
activation,
|
|
patch_size=5,
|
|
channels=16,
|
|
drop_path_rate=0.1,
|
|
):
|
|
super().__init__()
|
|
|
|
self.resolution = []
|
|
|
|
for idx, i in enumerate(range(res_log2, 3, -1)): # from input size to 16x16
|
|
res = 2**i
|
|
self.resolution.append(res)
|
|
if i == res_log2:
|
|
block = EncFromRGB(img_channels * 2 + 1, nf(i), activation)
|
|
else:
|
|
block = ConvBlockDown(nf(i + 1), nf(i), activation)
|
|
setattr(self, "EncConv_Block_%dx%d" % (res, res), block)
|
|
|
|
def forward(self, x):
|
|
out = {}
|
|
for res in self.resolution:
|
|
res_log2 = int(np.log2(res))
|
|
x = getattr(self, "EncConv_Block_%dx%d" % (res, res))(x)
|
|
out[res_log2] = x
|
|
|
|
return out
|
|
|
|
|
|
class ToStyle(nn.Module):
|
|
def __init__(self, in_channels, out_channels, activation, drop_rate):
|
|
super().__init__()
|
|
self.conv = nn.Sequential(
|
|
Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
down=2,
|
|
),
|
|
Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
down=2,
|
|
),
|
|
Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
down=2,
|
|
),
|
|
)
|
|
|
|
self.pool = nn.AdaptiveAvgPool2d(1)
|
|
self.fc = FullyConnectedLayer(
|
|
in_features=in_channels, out_features=out_channels, activation=activation
|
|
)
|
|
# self.dropout = nn.Dropout(drop_rate)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.pool(x)
|
|
x = self.fc(x.flatten(start_dim=1))
|
|
# x = self.dropout(x)
|
|
|
|
return x
|
|
|
|
|
|
class DecBlockFirstV2(nn.Module):
|
|
def __init__(
|
|
self,
|
|
res,
|
|
in_channels,
|
|
out_channels,
|
|
activation,
|
|
style_dim,
|
|
use_noise,
|
|
demodulate,
|
|
img_channels,
|
|
):
|
|
super().__init__()
|
|
self.res = res
|
|
|
|
self.conv0 = Conv2dLayer(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
)
|
|
self.conv1 = StyleConv(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
style_dim=style_dim,
|
|
resolution=2**res,
|
|
kernel_size=3,
|
|
use_noise=use_noise,
|
|
activation=activation,
|
|
demodulate=demodulate,
|
|
)
|
|
self.toRGB = ToRGB(
|
|
in_channels=out_channels,
|
|
out_channels=img_channels,
|
|
style_dim=style_dim,
|
|
kernel_size=1,
|
|
demodulate=False,
|
|
)
|
|
|
|
def forward(self, x, ws, gs, E_features, noise_mode="random"):
|
|
# x = self.fc(x).view(x.shape[0], -1, 4, 4)
|
|
x = self.conv0(x)
|
|
x = x + E_features[self.res]
|
|
style = get_style_code(ws[:, 0], gs)
|
|
x = self.conv1(x, style, noise_mode=noise_mode)
|
|
style = get_style_code(ws[:, 1], gs)
|
|
img = self.toRGB(x, style, skip=None)
|
|
|
|
return x, img
|
|
|
|
|
|
class DecBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
res,
|
|
in_channels,
|
|
out_channels,
|
|
activation,
|
|
style_dim,
|
|
use_noise,
|
|
demodulate,
|
|
img_channels,
|
|
): # res = 4, ..., resolution_log2
|
|
super().__init__()
|
|
self.res = res
|
|
|
|
self.conv0 = StyleConv(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
style_dim=style_dim,
|
|
resolution=2**res,
|
|
kernel_size=3,
|
|
up=2,
|
|
use_noise=use_noise,
|
|
activation=activation,
|
|
demodulate=demodulate,
|
|
)
|
|
self.conv1 = StyleConv(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
style_dim=style_dim,
|
|
resolution=2**res,
|
|
kernel_size=3,
|
|
use_noise=use_noise,
|
|
activation=activation,
|
|
demodulate=demodulate,
|
|
)
|
|
self.toRGB = ToRGB(
|
|
in_channels=out_channels,
|
|
out_channels=img_channels,
|
|
style_dim=style_dim,
|
|
kernel_size=1,
|
|
demodulate=False,
|
|
)
|
|
|
|
def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
|
|
style = get_style_code(ws[:, self.res * 2 - 9], gs)
|
|
x = self.conv0(x, style, noise_mode=noise_mode)
|
|
x = x + E_features[self.res]
|
|
style = get_style_code(ws[:, self.res * 2 - 8], gs)
|
|
x = self.conv1(x, style, noise_mode=noise_mode)
|
|
style = get_style_code(ws[:, self.res * 2 - 7], gs)
|
|
img = self.toRGB(x, style, skip=img)
|
|
|
|
return x, img
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(
|
|
self, res_log2, activation, style_dim, use_noise, demodulate, img_channels
|
|
):
|
|
super().__init__()
|
|
self.Dec_16x16 = DecBlockFirstV2(
|
|
4, nf(4), nf(4), activation, style_dim, use_noise, demodulate, img_channels
|
|
)
|
|
for res in range(5, res_log2 + 1):
|
|
setattr(
|
|
self,
|
|
"Dec_%dx%d" % (2**res, 2**res),
|
|
DecBlock(
|
|
res,
|
|
nf(res - 1),
|
|
nf(res),
|
|
activation,
|
|
style_dim,
|
|
use_noise,
|
|
demodulate,
|
|
img_channels,
|
|
),
|
|
)
|
|
self.res_log2 = res_log2
|
|
|
|
def forward(self, x, ws, gs, E_features, noise_mode="random"):
|
|
x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode)
|
|
for res in range(5, self.res_log2 + 1):
|
|
block = getattr(self, "Dec_%dx%d" % (2**res, 2**res))
|
|
x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode)
|
|
|
|
return img
|
|
|
|
|
|
class DecStyleBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
res,
|
|
in_channels,
|
|
out_channels,
|
|
activation,
|
|
style_dim,
|
|
use_noise,
|
|
demodulate,
|
|
img_channels,
|
|
):
|
|
super().__init__()
|
|
self.res = res
|
|
|
|
self.conv0 = StyleConv(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
style_dim=style_dim,
|
|
resolution=2**res,
|
|
kernel_size=3,
|
|
up=2,
|
|
use_noise=use_noise,
|
|
activation=activation,
|
|
demodulate=demodulate,
|
|
)
|
|
self.conv1 = StyleConv(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
style_dim=style_dim,
|
|
resolution=2**res,
|
|
kernel_size=3,
|
|
use_noise=use_noise,
|
|
activation=activation,
|
|
demodulate=demodulate,
|
|
)
|
|
self.toRGB = ToRGB(
|
|
in_channels=out_channels,
|
|
out_channels=img_channels,
|
|
style_dim=style_dim,
|
|
kernel_size=1,
|
|
demodulate=False,
|
|
)
|
|
|
|
def forward(self, x, img, style, skip, noise_mode="random"):
|
|
x = self.conv0(x, style, noise_mode=noise_mode)
|
|
x = x + skip
|
|
x = self.conv1(x, style, noise_mode=noise_mode)
|
|
img = self.toRGB(x, style, skip=img)
|
|
|
|
return x, img
|
|
|
|
|
|
class FirstStage(nn.Module):
|
|
def __init__(
|
|
self,
|
|
img_channels,
|
|
img_resolution=256,
|
|
dim=180,
|
|
w_dim=512,
|
|
use_noise=False,
|
|
demodulate=True,
|
|
activation="lrelu",
|
|
):
|
|
super().__init__()
|
|
res = 64
|
|
|
|
self.conv_first = Conv2dLayerPartial(
|
|
in_channels=img_channels + 1,
|
|
out_channels=dim,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
)
|
|
self.enc_conv = nn.ModuleList()
|
|
down_time = int(np.log2(img_resolution // res))
|
|
# 根据图片尺寸构建 swim transformer 的层数
|
|
for i in range(down_time): # from input size to 64
|
|
self.enc_conv.append(
|
|
Conv2dLayerPartial(
|
|
in_channels=dim,
|
|
out_channels=dim,
|
|
kernel_size=3,
|
|
down=2,
|
|
activation=activation,
|
|
)
|
|
)
|
|
|
|
# from 64 -> 16 -> 64
|
|
depths = [2, 3, 4, 3, 2]
|
|
ratios = [1, 1 / 2, 1 / 2, 2, 2]
|
|
num_heads = 6
|
|
window_sizes = [8, 16, 16, 16, 8]
|
|
drop_path_rate = 0.1
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
|
|
|
|
self.tran = nn.ModuleList()
|
|
for i, depth in enumerate(depths):
|
|
res = int(res * ratios[i])
|
|
if ratios[i] < 1:
|
|
merge = PatchMerging(dim, dim, down=int(1 / ratios[i]))
|
|
elif ratios[i] > 1:
|
|
merge = PatchUpsampling(dim, dim, up=ratios[i])
|
|
else:
|
|
merge = None
|
|
self.tran.append(
|
|
BasicLayer(
|
|
dim=dim,
|
|
input_resolution=[res, res],
|
|
depth=depth,
|
|
num_heads=num_heads,
|
|
window_size=window_sizes[i],
|
|
drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
|
|
downsample=merge,
|
|
)
|
|
)
|
|
|
|
# global style
|
|
down_conv = []
|
|
for i in range(int(np.log2(16))):
|
|
down_conv.append(
|
|
Conv2dLayer(
|
|
in_channels=dim,
|
|
out_channels=dim,
|
|
kernel_size=3,
|
|
down=2,
|
|
activation=activation,
|
|
)
|
|
)
|
|
down_conv.append(nn.AdaptiveAvgPool2d((1, 1)))
|
|
self.down_conv = nn.Sequential(*down_conv)
|
|
self.to_style = FullyConnectedLayer(
|
|
in_features=dim, out_features=dim * 2, activation=activation
|
|
)
|
|
self.ws_style = FullyConnectedLayer(
|
|
in_features=w_dim, out_features=dim, activation=activation
|
|
)
|
|
self.to_square = FullyConnectedLayer(
|
|
in_features=dim, out_features=16 * 16, activation=activation
|
|
)
|
|
|
|
style_dim = dim * 3
|
|
self.dec_conv = nn.ModuleList()
|
|
for i in range(down_time): # from 64 to input size
|
|
res = res * 2
|
|
self.dec_conv.append(
|
|
DecStyleBlock(
|
|
res,
|
|
dim,
|
|
dim,
|
|
activation,
|
|
style_dim,
|
|
use_noise,
|
|
demodulate,
|
|
img_channels,
|
|
)
|
|
)
|
|
|
|
def forward(self, images_in, masks_in, ws, noise_mode="random"):
|
|
x = torch.cat([masks_in - 0.5, images_in * masks_in], dim=1)
|
|
|
|
skips = []
|
|
x, mask = self.conv_first(x, masks_in) # input size
|
|
skips.append(x)
|
|
for i, block in enumerate(self.enc_conv): # input size to 64
|
|
x, mask = block(x, mask)
|
|
if i != len(self.enc_conv) - 1:
|
|
skips.append(x)
|
|
|
|
x_size = x.size()[-2:]
|
|
x = feature2token(x)
|
|
mask = feature2token(mask)
|
|
mid = len(self.tran) // 2
|
|
for i, block in enumerate(self.tran): # 64 to 16
|
|
if i < mid:
|
|
x, x_size, mask = block(x, x_size, mask)
|
|
skips.append(x)
|
|
elif i > mid:
|
|
x, x_size, mask = block(x, x_size, None)
|
|
x = x + skips[mid - i]
|
|
else:
|
|
x, x_size, mask = block(x, x_size, None)
|
|
|
|
mul_map = torch.ones_like(x) * 0.5
|
|
mul_map = F.dropout(mul_map, training=True)
|
|
ws = self.ws_style(ws[:, -1])
|
|
add_n = self.to_square(ws).unsqueeze(1)
|
|
add_n = (
|
|
F.interpolate(
|
|
add_n, size=x.size(1), mode="linear", align_corners=False
|
|
)
|
|
.squeeze(1)
|
|
.unsqueeze(-1)
|
|
)
|
|
x = x * mul_map + add_n * (1 - mul_map)
|
|
gs = self.to_style(
|
|
self.down_conv(token2feature(x, x_size)).flatten(start_dim=1)
|
|
)
|
|
style = torch.cat([gs, ws], dim=1)
|
|
|
|
x = token2feature(x, x_size).contiguous()
|
|
img = None
|
|
for i, block in enumerate(self.dec_conv):
|
|
x, img = block(
|
|
x, img, style, skips[len(self.dec_conv) - i - 1], noise_mode=noise_mode
|
|
)
|
|
|
|
# ensemble
|
|
img = img * (1 - masks_in) + images_in * masks_in
|
|
|
|
return img
|
|
|
|
|
|
class SynthesisNet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output image resolution.
|
|
img_channels=3, # Number of color channels.
|
|
channel_base=32768, # Overall multiplier for the number of channels.
|
|
channel_decay=1.0,
|
|
channel_max=512, # Maximum number of channels in any layer.
|
|
activation="lrelu", # Activation function: 'relu', 'lrelu', etc.
|
|
drop_rate=0.5,
|
|
use_noise=False,
|
|
demodulate=True,
|
|
):
|
|
super().__init__()
|
|
resolution_log2 = int(np.log2(img_resolution))
|
|
assert img_resolution == 2**resolution_log2 and img_resolution >= 4
|
|
|
|
self.num_layers = resolution_log2 * 2 - 3 * 2
|
|
self.img_resolution = img_resolution
|
|
self.resolution_log2 = resolution_log2
|
|
|
|
# first stage
|
|
self.first_stage = FirstStage(
|
|
img_channels,
|
|
img_resolution=img_resolution,
|
|
w_dim=w_dim,
|
|
use_noise=False,
|
|
demodulate=demodulate,
|
|
)
|
|
|
|
# second stage
|
|
self.enc = Encoder(
|
|
resolution_log2, img_channels, activation, patch_size=5, channels=16
|
|
)
|
|
self.to_square = FullyConnectedLayer(
|
|
in_features=w_dim, out_features=16 * 16, activation=activation
|
|
)
|
|
self.to_style = ToStyle(
|
|
in_channels=nf(4),
|
|
out_channels=nf(2) * 2,
|
|
activation=activation,
|
|
drop_rate=drop_rate,
|
|
)
|
|
style_dim = w_dim + nf(2) * 2
|
|
self.dec = Decoder(
|
|
resolution_log2, activation, style_dim, use_noise, demodulate, img_channels
|
|
)
|
|
|
|
def forward(self, images_in, masks_in, ws, noise_mode="random", return_stg1=False):
|
|
out_stg1 = self.first_stage(images_in, masks_in, ws, noise_mode=noise_mode)
|
|
|
|
# encoder
|
|
x = images_in * masks_in + out_stg1 * (1 - masks_in)
|
|
x = torch.cat([masks_in - 0.5, x, images_in * masks_in], dim=1)
|
|
E_features = self.enc(x)
|
|
|
|
fea_16 = E_features[4]
|
|
mul_map = torch.ones_like(fea_16) * 0.5
|
|
mul_map = F.dropout(mul_map, training=True)
|
|
add_n = self.to_square(ws[:, 0]).view(-1, 16, 16).unsqueeze(1)
|
|
add_n = F.interpolate(
|
|
add_n, size=fea_16.size()[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
fea_16 = fea_16 * mul_map + add_n * (1 - mul_map)
|
|
E_features[4] = fea_16
|
|
|
|
# style
|
|
gs = self.to_style(fea_16)
|
|
|
|
# decoder
|
|
img = self.dec(fea_16, ws, gs, E_features, noise_mode=noise_mode)
|
|
|
|
# ensemble
|
|
img = img * (1 - masks_in) + images_in * masks_in
|
|
|
|
if not return_stg1:
|
|
return img
|
|
else:
|
|
return img, out_stg1
|
|
|
|
|
|
class Generator(nn.Module):
|
|
def __init__(
|
|
self,
|
|
z_dim, # Input latent (Z) dimensionality, 0 = no latent.
|
|
c_dim, # Conditioning label (C) dimensionality, 0 = no label.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # resolution of generated image
|
|
img_channels, # Number of input color channels.
|
|
synthesis_kwargs={}, # Arguments for SynthesisNetwork.
|
|
mapping_kwargs={}, # Arguments for MappingNetwork.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
|
|
self.synthesis = SynthesisNet(
|
|
w_dim=w_dim,
|
|
img_resolution=img_resolution,
|
|
img_channels=img_channels,
|
|
**synthesis_kwargs,
|
|
)
|
|
self.mapping = MappingNet(
|
|
z_dim=z_dim,
|
|
c_dim=c_dim,
|
|
w_dim=w_dim,
|
|
num_ws=self.synthesis.num_layers,
|
|
**mapping_kwargs,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
images_in,
|
|
masks_in,
|
|
z,
|
|
c,
|
|
truncation_psi=1,
|
|
truncation_cutoff=None,
|
|
skip_w_avg_update=False,
|
|
noise_mode="none",
|
|
return_stg1=False,
|
|
):
|
|
ws = self.mapping(
|
|
z,
|
|
c,
|
|
truncation_psi=truncation_psi,
|
|
truncation_cutoff=truncation_cutoff,
|
|
skip_w_avg_update=skip_w_avg_update,
|
|
)
|
|
img = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode)
|
|
return img
|
|
|
|
|
|
class Discriminator(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
c_dim, # Conditioning label (C) dimensionality.
|
|
img_resolution, # Input resolution.
|
|
img_channels, # Number of input color channels.
|
|
channel_base=32768, # Overall multiplier for the number of channels.
|
|
channel_max=512, # Maximum number of channels in any layer.
|
|
channel_decay=1,
|
|
cmap_dim=None, # Dimensionality of mapped conditioning label, None = default.
|
|
activation="lrelu",
|
|
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, None = entire minibatch.
|
|
mbstd_num_channels=1, # Number of features for the minibatch standard deviation layer, 0 = disable.
|
|
):
|
|
super().__init__()
|
|
self.c_dim = c_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
|
|
resolution_log2 = int(np.log2(img_resolution))
|
|
assert img_resolution == 2**resolution_log2 and img_resolution >= 4
|
|
self.resolution_log2 = resolution_log2
|
|
|
|
if cmap_dim == None:
|
|
cmap_dim = nf(2)
|
|
if c_dim == 0:
|
|
cmap_dim = 0
|
|
self.cmap_dim = cmap_dim
|
|
|
|
if c_dim > 0:
|
|
self.mapping = MappingNet(
|
|
z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None
|
|
)
|
|
|
|
Dis = [DisFromRGB(img_channels + 1, nf(resolution_log2), activation)]
|
|
for res in range(resolution_log2, 2, -1):
|
|
Dis.append(DisBlock(nf(res), nf(res - 1), activation))
|
|
|
|
if mbstd_num_channels > 0:
|
|
Dis.append(
|
|
MinibatchStdLayer(
|
|
group_size=mbstd_group_size, num_channels=mbstd_num_channels
|
|
)
|
|
)
|
|
Dis.append(
|
|
Conv2dLayer(
|
|
nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation
|
|
)
|
|
)
|
|
self.Dis = nn.Sequential(*Dis)
|
|
|
|
self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation)
|
|
self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
|
|
|
|
# for 64x64
|
|
Dis_stg1 = [DisFromRGB(img_channels + 1, nf(resolution_log2) // 2, activation)]
|
|
for res in range(resolution_log2, 2, -1):
|
|
Dis_stg1.append(DisBlock(nf(res) // 2, nf(res - 1) // 2, activation))
|
|
|
|
if mbstd_num_channels > 0:
|
|
Dis_stg1.append(
|
|
MinibatchStdLayer(
|
|
group_size=mbstd_group_size, num_channels=mbstd_num_channels
|
|
)
|
|
)
|
|
Dis_stg1.append(
|
|
Conv2dLayer(
|
|
nf(2) // 2 + mbstd_num_channels,
|
|
nf(2) // 2,
|
|
kernel_size=3,
|
|
activation=activation,
|
|
)
|
|
)
|
|
self.Dis_stg1 = nn.Sequential(*Dis_stg1)
|
|
|
|
self.fc0_stg1 = FullyConnectedLayer(
|
|
nf(2) // 2 * 4**2, nf(2) // 2, activation=activation
|
|
)
|
|
self.fc1_stg1 = FullyConnectedLayer(
|
|
nf(2) // 2, 1 if cmap_dim == 0 else cmap_dim
|
|
)
|
|
|
|
def forward(self, images_in, masks_in, images_stg1, c):
|
|
x = self.Dis(torch.cat([masks_in - 0.5, images_in], dim=1))
|
|
x = self.fc1(self.fc0(x.flatten(start_dim=1)))
|
|
|
|
x_stg1 = self.Dis_stg1(torch.cat([masks_in - 0.5, images_stg1], dim=1))
|
|
x_stg1 = self.fc1_stg1(self.fc0_stg1(x_stg1.flatten(start_dim=1)))
|
|
|
|
if self.c_dim > 0:
|
|
cmap = self.mapping(None, c)
|
|
|
|
if self.cmap_dim > 0:
|
|
x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
|
|
x_stg1 = (x_stg1 * cmap).sum(dim=1, keepdim=True) * (
|
|
1 / np.sqrt(self.cmap_dim)
|
|
)
|
|
|
|
return x, x_stg1
|
|
|
|
|
|
MAT_MODEL_URL = os.environ.get(
|
|
"MAT_MODEL_URL",
|
|
"https://github.com/Sanster/models/releases/download/add_mat/Places_512_FullData_G.pth",
|
|
)
|
|
|
|
MAT_MODEL_MD5 = os.environ.get("MAT_MODEL_MD5", "8ca927835fa3f5e21d65ffcb165377ed")
|
|
|
|
|
|
class MAT(InpaintModel):
|
|
name = "mat"
|
|
min_size = 512
|
|
pad_mod = 512
|
|
pad_to_square = True
|
|
is_erase_model = True
|
|
|
|
def init_model(self, device, **kwargs):
|
|
seed = 240 # pick up a random number
|
|
set_seed(seed)
|
|
|
|
fp16 = not kwargs.get("no_half", False)
|
|
use_gpu = "cuda" in str(device) and torch.cuda.is_available()
|
|
self.torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
|
|
|
|
G = Generator(
|
|
z_dim=512,
|
|
c_dim=0,
|
|
w_dim=512,
|
|
img_resolution=512,
|
|
img_channels=3,
|
|
mapping_kwargs={"torch_dtype": self.torch_dtype},
|
|
).to(self.torch_dtype)
|
|
# fmt: off
|
|
self.model = load_model(G, MAT_MODEL_URL, device, MAT_MODEL_MD5)
|
|
self.z = torch.from_numpy(np.random.randn(1, G.z_dim)).to(self.torch_dtype).to(device)
|
|
self.label = torch.zeros([1, self.model.c_dim], device=device).to(self.torch_dtype)
|
|
# fmt: on
|
|
|
|
@staticmethod
|
|
def download():
|
|
download_model(MAT_MODEL_URL, MAT_MODEL_MD5)
|
|
|
|
@staticmethod
|
|
def is_downloaded() -> bool:
|
|
return os.path.exists(get_cache_path_by_url(MAT_MODEL_URL))
|
|
|
|
def forward(self, image, mask, config: InpaintRequest):
|
|
"""Input images and output images have same size
|
|
images: [H, W, C] RGB
|
|
masks: [H, W] mask area == 255
|
|
return: BGR IMAGE
|
|
"""
|
|
|
|
image = norm_img(image) # [0, 1]
|
|
image = image * 2 - 1 # [0, 1] -> [-1, 1]
|
|
|
|
mask = (mask > 127) * 255
|
|
mask = 255 - mask
|
|
mask = norm_img(mask)
|
|
|
|
image = (
|
|
torch.from_numpy(image).unsqueeze(0).to(self.torch_dtype).to(self.device)
|
|
)
|
|
mask = torch.from_numpy(mask).unsqueeze(0).to(self.torch_dtype).to(self.device)
|
|
|
|
output = self.model(
|
|
image, mask, self.z, self.label, truncation_psi=1, noise_mode="none"
|
|
)
|
|
output = (
|
|
(output.permute(0, 2, 3, 1) * 127.5 + 127.5)
|
|
.round()
|
|
.clamp(0, 255)
|
|
.to(torch.uint8)
|
|
)
|
|
output = output[0].cpu().numpy()
|
|
cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
|
return cur_res
|