import numpy as np import math import sys sys.path.insert(0, '../') import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from torch_utils import misc from torch_utils import persistence from networks.basic_module import FullyConnectedLayer, Conv2dLayer, MappingNet, MinibatchStdLayer, DisFromRGB, DisBlock, StyleConv, ToRGB, get_style_code @misc.profiled_function 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] @persistence.persistent_class 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 @misc.profiled_function 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 @misc.profiled_function def window_reverse(windows, window_size, H, W): """ 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)) 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 #---------------------------------------------------------------------------- @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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 @persistence.persistent_class 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)) 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 @persistence.persistent_class 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 = True, 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 @persistence.persistent_class 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='random', return_stg1=False): ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, skip_w_avg_update=skip_w_avg_update) if not return_stg1: img = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode) return img else: img, out_stg1 = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode, return_stg1=True) return img, out_stg1 @persistence.persistent_class 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 if __name__ == '__main__': device = torch.device('cuda:0') batch = 1 res = 512 G = Generator(z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3).to(device) D = Discriminator(c_dim=0, img_resolution=res, img_channels=3).to(device) img = torch.randn(batch, 3, res, res).to(device) mask = torch.randn(batch, 1, res, res).to(device) z = torch.randn(batch, 512).to(device) G.eval() # def count(block): # return sum(p.numel() for p in block.parameters()) / 10 ** 6 # print('Generator', count(G)) # print('discriminator', count(D)) with torch.no_grad(): img, img_stg1 = G(img, mask, z, None, return_stg1=True) print('output of G:', img.shape, img_stg1.shape) score, score_stg1 = D(img, mask, img_stg1, None) print('output of D:', score.shape, score_stg1.shape)