IOPaint/lama_cleaner/model/mat.py

import os
import random

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint

from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.model.utils import setup_filter, Conv2dLayer, FullyConnectedLayer, conv2d_resample, bias_act, \
    upsample2d, activation_funcs, MinibatchStdLayer, to_2tuple, normalize_2nd_moment
from lama_cleaner.schema import Config


class ModulatedConv2d(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.
                 style_dim,  # dimension of the style code
                 demodulate=True,  # perfrom demodulation
                 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.
                 ):
        super().__init__()
        self.demodulate = demodulate

        self.weight = torch.nn.Parameter(torch.randn([1, out_channels, in_channels, kernel_size, kernel_size]))
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
        self.padding = self.kernel_size // 2
        self.up = up
        self.down = down
        self.register_buffer('resample_filter', setup_filter(resample_filter))
        self.conv_clamp = conv_clamp

        self.affine = FullyConnectedLayer(style_dim, in_channels, bias_init=1)

    def forward(self, x, style):
        batch, in_channels, height, width = x.shape
        style = self.affine(style).view(batch, 1, in_channels, 1, 1)
        weight = self.weight * self.weight_gain * style

        if self.demodulate:
            decoefs = (weight.pow(2).sum(dim=[2, 3, 4]) + 1e-8).rsqrt()
            weight = weight * decoefs.view(batch, self.out_channels, 1, 1, 1)

        weight = weight.view(batch * self.out_channels, in_channels, self.kernel_size, self.kernel_size)
        x = x.view(1, batch * in_channels, height, width)
        x = conv2d_resample(x=x, w=weight, f=self.resample_filter, up=self.up, down=self.down,
                            padding=self.padding, groups=batch)
        out = x.view(batch, self.out_channels, *x.shape[2:])

        return out


class StyleConv(torch.nn.Module):
    def __init__(self,
                 in_channels,  # Number of input channels.
                 out_channels,  # Number of output channels.
                 style_dim,  # Intermediate latent (W) dimensionality.
                 resolution,  # Resolution of this layer.
                 kernel_size=3,  # Convolution kernel size.
                 up=1,  # Integer upsampling factor.
                 use_noise=False,  # Enable noise input?
                 activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
                 resample_filter=[1, 3, 3, 1],  # Low-pass filter to apply when resampling activations.
                 conv_clamp=None,  # Clamp the output of convolution layers to +-X, None = disable clamping.
                 demodulate=True,  # perform demodulation
                 ):
        super().__init__()

        self.conv = ModulatedConv2d(in_channels=in_channels,
                                    out_channels=out_channels,
                                    kernel_size=kernel_size,
                                    style_dim=style_dim,
                                    demodulate=demodulate,
                                    up=up,
                                    resample_filter=resample_filter,
                                    conv_clamp=conv_clamp)

        self.use_noise = use_noise
        self.resolution = resolution
        if use_noise:
            self.register_buffer('noise_const', torch.randn([resolution, resolution]))
            self.noise_strength = torch.nn.Parameter(torch.zeros([]))

        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
        self.activation = activation
        self.act_gain = activation_funcs[activation].def_gain
        self.conv_clamp = conv_clamp

    def forward(self, x, style, noise_mode='random', gain=1):
        x = self.conv(x, style)

        assert noise_mode in ['random', 'const', 'none']

        if self.use_noise:
            if noise_mode == 'random':
                xh, xw = x.size()[-2:]
                noise = torch.randn([x.shape[0], 1, xh, xw], device=x.device) \
                        * self.noise_strength
            if noise_mode == 'const':
                noise = self.noise_const * self.noise_strength
            x = x + noise

        act_gain = self.act_gain * gain
        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
        out = bias_act(x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp)

        return out


class ToRGB(torch.nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 style_dim,
                 kernel_size=1,
                 resample_filter=[1, 3, 3, 1],
                 conv_clamp=None,
                 demodulate=False):
        super().__init__()

        self.conv = ModulatedConv2d(in_channels=in_channels,
                                    out_channels=out_channels,
                                    kernel_size=kernel_size,
                                    style_dim=style_dim,
                                    demodulate=demodulate,
                                    resample_filter=resample_filter,
                                    conv_clamp=conv_clamp)
        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
        self.register_buffer('resample_filter', setup_filter(resample_filter))
        self.conv_clamp = conv_clamp

    def forward(self, x, style, skip=None):
        x = self.conv(x, style)
        out = bias_act(x, self.bias, clamp=self.conv_clamp)

        if skip is not None:
            if skip.shape != out.shape:
                skip = upsample2d(skip, self.resample_filter)
            out = out + skip

        return out


def get_style_code(a, b):
    return torch.cat([a, b], dim=1)


class DecBlockFirst(nn.Module):
    def __init__(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
        super().__init__()
        self.fc = FullyConnectedLayer(in_features=in_channels * 2,
                                      out_features=in_channels * 4 ** 2,
                                      activation=activation)
        self.conv = StyleConv(in_channels=in_channels,
                              out_channels=out_channels,
                              style_dim=style_dim,
                              resolution=4,
                              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 = x + E_features[2]
        style = get_style_code(ws[:, 0], gs)
        x = self.conv(x, style, noise_mode=noise_mode)
        style = get_style_code(ws[:, 1], gs)
        img = self.toRGB(x, style, skip=None)

        return x, img


class DecBlockFirstV2(nn.Module):
    def __init__(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
        super().__init__()
        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=4,
                               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[2]
        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 = 2, ..., 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 - 5], gs)
        x = self.conv0(x, style, noise_mode=noise_mode)
        x = x + E_features[self.res]
        style = get_style_code(ws[:, self.res * 2 - 4], gs)
        x = self.conv1(x, style, noise_mode=noise_mode)
        style = get_style_code(ws[:, self.res * 2 - 3], gs)
        img = self.toRGB(x, style, skip=img)

        return x, img


class MappingNet(torch.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.
                 num_ws,  # Number of intermediate latents to output, None = do not broadcast.
                 num_layers=8,  # Number of mapping layers.
                 embed_features=None,  # Label embedding dimensionality, None = same as w_dim.
                 layer_features=None,  # Number of intermediate features in the mapping layers, None = same as w_dim.
                 activation='lrelu',  # Activation function: 'relu', 'lrelu', etc.
                 lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
                 w_avg_beta=0.995,  # Decay for tracking the moving average of W during training, None = do not track.
                 ):
        super().__init__()
        self.z_dim = z_dim
        self.c_dim = c_dim
        self.w_dim = w_dim
        self.num_ws = num_ws
        self.num_layers = num_layers
        self.w_avg_beta = w_avg_beta

        if embed_features is None:
            embed_features = w_dim
        if c_dim == 0:
            embed_features = 0
        if layer_features is None:
            layer_features = w_dim
        features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]

        if c_dim > 0:
            self.embed = FullyConnectedLayer(c_dim, embed_features)
        for idx in range(num_layers):
            in_features = features_list[idx]
            out_features = features_list[idx + 1]
            layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
            setattr(self, f'fc{idx}', layer)

        if num_ws is not None and w_avg_beta is not None:
            self.register_buffer('w_avg', torch.zeros([w_dim]))

    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False):
        # Embed, normalize, and concat inputs.
        x = None
        with torch.autograd.profiler.record_function('input'):
            if self.z_dim > 0:
                x = normalize_2nd_moment(z.to(torch.float32))
            if self.c_dim > 0:
                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
                x = torch.cat([x, y], dim=1) if x is not None else y

        # Main layers.
        for idx in range(self.num_layers):
            layer = getattr(self, f'fc{idx}')
            x = layer(x)

        # Update moving average of W.
        if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
            with torch.autograd.profiler.record_function('update_w_avg'):
                self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))

        # Broadcast.
        if self.num_ws is not None:
            with torch.autograd.profiler.record_function('broadcast'):
                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])

        # Apply truncation.
        if truncation_psi != 1:
            with torch.autograd.profiler.record_function('truncate'):
                assert self.w_avg_beta is not None
                if self.num_ws is None or truncation_cutoff is None:
                    x = self.w_avg.lerp(x, truncation_psi)
                else:
                    x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)

        return x


class DisFromRGB(nn.Module):
    def __init__(self, in_channels, out_channels, activation):  # res = 2, ..., resolution_log2
        super().__init__()
        self.conv = Conv2dLayer(in_channels=in_channels,
                                out_channels=out_channels,
                                kernel_size=1,
                                activation=activation,
                                )

    def forward(self, x):
        return self.conv(x)


class DisBlock(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=in_channels,
                                 kernel_size=3,
                                 activation=activation,
                                 )
        self.conv1 = Conv2dLayer(in_channels=in_channels,
                                 out_channels=out_channels,
                                 kernel_size=3,
                                 down=2,
                                 activation=activation,
                                 )
        self.skip = Conv2dLayer(in_channels=in_channels,
                                out_channels=out_channels,
                                kernel_size=1,
                                down=2,
                                bias=False,
                                )

    def forward(self, x):
        skip = self.skip(x, gain=np.sqrt(0.5))
        x = self.conv0(x)
        x = self.conv1(x, gain=np.sqrt(0.5))
        out = skip + x

        return out


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

        def nf(stage):
            return np.clip(int(channel_base / 2 ** (stage * channel_decay)), 1, channel_max)

        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)

    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.):
        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 + 1e-8)
                update_mask = torch.clamp(update_mask, 0, 1)  # 0 or 1
                mask_ratio = torch.mul(mask_ratio, update_mask)
            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.,
                 proj_drop=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)
        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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=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.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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=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",
)


class MAT(InpaintModel):
    min_size = 512
    pad_mod = 512
    pad_to_square = True

    def init_model(self, device):
        seed = 240  # pick up a random number
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)

        G = Generator(z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3)
        self.model = load_model(G, MAT_MODEL_URL, device)
        self.z = torch.from_numpy(np.random.randn(1, G.z_dim)).to(device)  # [1., 512]
        self.label = torch.zeros([1, self.model.c_dim], device=device)

    @staticmethod
    def is_downloaded() -> bool:
        return os.path.exists(get_cache_path_by_url(MAT_MODEL_URL))

    def forward(self, image, mask, config: Config):
        """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.device)
        mask = torch.from_numpy(mask).unsqueeze(0).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