Update stylegan_model.py

stylegan_model.py

@@ -1,126 +1,719 @@
 import math
-import pickle
+import random
+import functools
+import operator
 
 import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
+from torch import nn
+from torch.nn import functional as F
+from torch.autograd import Function
 
+from model.stylegan.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix
 
-def get_rank():
-    if not dist.is_available():
-        return 0
+class PixelNorm(nn.Module):
+    def __init__(self):
+        super().__init__()
 
-    if not dist.is_initialized():
-        return 0
+    def forward(self, input):
+        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
 
-    return dist.get_rank()
 
+def make_kernel(k):
+    k = torch.tensor(k, dtype=torch.float32)
 
-def synchronize():
-    if not dist.is_available():
-        return
+    if k.ndim == 1:
+        k = k[None, :] * k[:, None]
 
-    if not dist.is_initialized():
-        return
+    k /= k.sum()
 
-    world_size = dist.get_world_size()
+    return k
 
-    if world_size == 1:
-        return
 
-    dist.barrier()
+class Upsample(nn.Module):
+    def __init__(self, kernel, factor=2):
+        super().__init__()
 
+        self.factor = factor
+        kernel = make_kernel(kernel) * (factor ** 2)
+        self.register_buffer("kernel", kernel)
 
-def get_world_size():
-    if not dist.is_available():
-        return 1
+        p = kernel.shape[0] - factor
 
-    if not dist.is_initialized():
-        return 1
+        pad0 = (p + 1) // 2 + factor - 1
+        pad1 = p // 2
 
-    return dist.get_world_size()
+        self.pad = (pad0, pad1)
 
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
 
-def reduce_sum(tensor):
-    if not dist.is_available():
-        return tensor
+        return out
 
-    if not dist.is_initialized():
-        return tensor
 
-    tensor = tensor.clone()
-    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
+class Downsample(nn.Module):
+    def __init__(self, kernel, factor=2):
+        super().__init__()
 
-    return tensor
+        self.factor = factor
+        kernel = make_kernel(kernel)
+        self.register_buffer("kernel", kernel)
 
+        p = kernel.shape[0] - factor
 
-def gather_grad(params):
-    world_size = get_world_size()
-    
-    if world_size == 1:
-        return
+        pad0 = (p + 1) // 2
+        pad1 = p // 2
 
-    for param in params:
-        if param.grad is not None:
-            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
-            param.grad.data.div_(world_size)
+        self.pad = (pad0, pad1)
 
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
 
-def all_gather(data):
-    world_size = get_world_size()
+        return out
 
-    if world_size == 1:
-        return [data]
 
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to('cuda')
+class Blur(nn.Module):
+    def __init__(self, kernel, pad, upsample_factor=1):
+        super().__init__()
 
-    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
-    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
+        kernel = make_kernel(kernel)
 
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
+        if upsample_factor > 1:
+            kernel = kernel * (upsample_factor ** 2)
 
-    if local_size != max_size:
-        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
-        tensor = torch.cat((tensor, padding), 0)
+        self.register_buffer("kernel", kernel)
 
-    dist.all_gather(tensor_list, tensor)
+        self.pad = pad
 
-    data_list = []
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, pad=self.pad)
 
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
+        return out
 
-    return data_list
 
+class EqualConv2d(nn.Module):
+    def __init__(
+        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dilation=1 ## modified
+    ):
+        super().__init__()
 
-def reduce_loss_dict(loss_dict):
-    world_size = get_world_size()
+        self.weight = nn.Parameter(
+            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+        )
+        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
 
-    if world_size < 2:
-        return loss_dict
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation ## modified
 
-    with torch.no_grad():
-        keys = []
-        losses = []
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_channel))
 
-        for k in sorted(loss_dict.keys()):
-            keys.append(k)
-            losses.append(loss_dict[k])
+        else:
+            self.bias = None
 
-        losses = torch.stack(losses, 0)
-        dist.reduce(losses, dst=0)
+    def forward(self, input):
+        out = conv2d_gradfix.conv2d(
+            input,
+            self.weight * self.scale,
+            bias=self.bias,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation,  ## modified
+        )
 
-        if dist.get_rank() == 0:
-            losses /= world_size
+        return out
 
-        reduced_losses = {k: v for k, v in zip(keys, losses)}
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
+            f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding}, dilation={self.dilation})" ## modified
+        )
 
-    return reduced_losses
+
+class EqualLinear(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
+    ):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+
+        else:
+            self.bias = None
+
+        self.activation = activation
+
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+        if self.activation:
+            out = F.linear(input, self.weight * self.scale)
+            out = fused_leaky_relu(out, self.bias * self.lr_mul)
+
+        else:
+            out = F.linear(
+                input, self.weight * self.scale, bias=self.bias * self.lr_mul
+            )
+
+        return out
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
+        )
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        demodulate=True,
+        upsample=False,
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        fused=True,
+    ):
+        super().__init__()
+
+        self.eps = 1e-8
+        self.kernel_size = kernel_size
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+        self.upsample = upsample
+        self.downsample = downsample
+
+        if upsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) - (kernel_size - 1)
+            pad0 = (p + 1) // 2 + factor - 1
+            pad1 = p // 2 + 1
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
+
+        fan_in = in_channel * kernel_size ** 2
+        self.scale = 1 / math.sqrt(fan_in)
+        self.padding = kernel_size // 2
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+        )
+
+        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+
+        self.demodulate = demodulate
+        self.fused = fused
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
+            f"upsample={self.upsample}, downsample={self.downsample})"
+        )
+
+    def forward(self, input, style, externalweight=None):
+        batch, in_channel, height, width = input.shape
+
+        if not self.fused:
+            weight = self.scale * self.weight.squeeze(0)
+            style = self.modulation(style)
+
+            if self.demodulate:
+                w = weight.unsqueeze(0) * style.view(batch, 1, in_channel, 1, 1)
+                dcoefs = (w.square().sum((2, 3, 4)) + 1e-8).rsqrt()
+
+            input = input * style.reshape(batch, in_channel, 1, 1)
+
+            if self.upsample:
+                weight = weight.transpose(0, 1)
+                out = conv2d_gradfix.conv_transpose2d(
+                    input, weight, padding=0, stride=2
+                )
+                out = self.blur(out)
+
+            elif self.downsample:
+                input = self.blur(input)
+                out = conv2d_gradfix.conv2d(input, weight, padding=0, stride=2)
+
+            else:
+                out = conv2d_gradfix.conv2d(input, weight, padding=self.padding)
+
+            if self.demodulate:
+                out = out * dcoefs.view(batch, -1, 1, 1)
+
+            return out
+
+        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+        if externalweight is None:
+            weight = self.scale * self.weight * style
+        else:
+            weight = self.scale * (self.weight + externalweight) * style
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+        weight = weight.view(
+            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+        )
+
+        if self.upsample:
+            input = input.view(1, batch * in_channel, height, width)
+            weight = weight.view(
+                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+            )
+            weight = weight.transpose(1, 2).reshape(
+                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+            )
+            out = conv2d_gradfix.conv_transpose2d(
+                input, weight, padding=0, stride=2, groups=batch
+            )
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+            out = self.blur(out)
+
+        elif self.downsample:
+            input = self.blur(input)
+            _, _, height, width = input.shape
+            input = input.view(1, batch * in_channel, height, width)
+            out = conv2d_gradfix.conv2d(
+                input, weight, padding=0, stride=2, groups=batch
+            )
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        else:
+            input = input.view(1, batch * in_channel, height, width)
+            out = conv2d_gradfix.conv2d(
+                input, weight, padding=self.padding, groups=batch
+            )
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        return out
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.zeros(1))
+
+    def forward(self, image, noise=None):
+        if noise is None:
+            batch, _, height, width = image.shape
+            noise = image.new_empty(batch, 1, height, width).normal_()
+
+        return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+    def __init__(self, channel, size=4):
+        super().__init__()
+
+        self.input = nn.Parameter(torch.randn(1, channel, size, size))
+
+    def forward(self, input):
+        batch = input.shape[0]
+        out = self.input.repeat(batch, 1, 1, 1)
+
+        return out
+
+
+class StyledConv(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        upsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        demodulate=True,
+    ):
+        super().__init__()
+
+        self.conv = ModulatedConv2d(
+            in_channel,
+            out_channel,
+            kernel_size,
+            style_dim,
+            upsample=upsample,
+            blur_kernel=blur_kernel,
+            demodulate=demodulate,
+        )
+
+        self.noise = NoiseInjection()
+        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
+        # self.activate = ScaledLeakyReLU(0.2)
+        self.activate = FusedLeakyReLU(out_channel)
+
+    def forward(self, input, style, noise=None, externalweight=None):
+        out = self.conv(input, style, externalweight)
+        out = self.noise(out, noise=noise)
+        # out = out + self.bias
+        out = self.activate(out)
+
+        return out
+
+
+class ToRGB(nn.Module):
+    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        if upsample:
+            self.upsample = Upsample(blur_kernel)
+
+        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, input, style, skip=None, externalweight=None):
+        out = self.conv(input, style, externalweight)
+        out = out + self.bias
+
+        if skip is not None:
+            skip = self.upsample(skip)
+
+            out = out + skip
+
+        return out
+
+
+class Generator(nn.Module):
+    def __init__(
+        self,
+        size,
+        style_dim,
+        n_mlp,
+        channel_multiplier=2,
+        blur_kernel=[1, 3, 3, 1],
+        lr_mlp=0.01,
+    ):
+        super().__init__()
+
+        self.size = size
+
+        self.style_dim = style_dim
+
+        layers = [PixelNorm()]
+
+        for i in range(n_mlp):
+            layers.append(
+                EqualLinear(
+                    style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
+                )
+            )
+
+        self.style = nn.Sequential(*layers)
+
+        self.channels = {
+            4: 512,
+            8: 512,
+            16: 512,
+            32: 512,
+            64: 256 * channel_multiplier,
+            128: 128 * channel_multiplier,
+            256: 64 * channel_multiplier,
+            512: 32 * channel_multiplier,
+            1024: 16 * channel_multiplier,
+        }
+
+        self.input = ConstantInput(self.channels[4])
+        self.conv1 = StyledConv(
+            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
+        )
+        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
+
+        self.log_size = int(math.log(size, 2))
+        self.num_layers = (self.log_size - 2) * 2 + 1
+
+        self.convs = nn.ModuleList()
+        self.upsamples = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+
+        in_channel = self.channels[4]
+
+        for layer_idx in range(self.num_layers):
+            res = (layer_idx + 5) // 2
+            shape = [1, 1, 2 ** res, 2 ** res]
+            self.noises.register_buffer(f"noise_{layer_idx}", torch.randn(*shape))
+
+        for i in range(3, self.log_size + 1):
+            out_channel = self.channels[2 ** i]
+
+            self.convs.append(
+                StyledConv(
+                    in_channel,
+                    out_channel,
+                    3,
+                    style_dim,
+                    upsample=True,
+                    blur_kernel=blur_kernel,
+                )
+            )
+
+            self.convs.append(
+                StyledConv(
+                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
+                )
+            )
+
+            self.to_rgbs.append(ToRGB(out_channel, style_dim))
+
+            in_channel = out_channel
+
+        self.n_latent = self.log_size * 2 - 2
+
+    def make_noise(self):
+        device = self.input.input.device
+
+        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
+
+        for i in range(3, self.log_size + 1):
+            for _ in range(2):
+                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
+
+        return noises
+
+    def mean_latent(self, n_latent):
+        latent_in = torch.randn(
+            n_latent, self.style_dim, device=self.input.input.device
+        )
+        latent = self.style(latent_in).mean(0, keepdim=True)
+
+        return latent
+
+    def get_latent(self, input):
+        return self.style(input)
+
+    def forward(
+        self,
+        styles,
+        return_latents=False,
+        inject_index=None,
+        truncation=1,
+        truncation_latent=None,
+        input_is_latent=False,
+        noise=None,
+        randomize_noise=True,
+        z_plus_latent=False,
+        return_feature_ind=999,
+    ):
+        if not input_is_latent:
+            if not z_plus_latent:
+                styles = [self.style(s) for s in styles]
+            else:
+                styles_ = []
+                for s in styles:
+                    style_ = []
+                    for i in range(s.shape[1]):
+                        style_.append(self.style(s[:,i]).unsqueeze(1))
+                    styles_.append(torch.cat(style_,dim=1))
+                styles = styles_
+
+        if noise is None:
+            if randomize_noise:
+                noise = [None] * self.num_layers
+            else:
+                noise = [
+                    getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
+                ]
+
+        if truncation < 1:
+            style_t = []
+
+            for style in styles:
+                style_t.append(
+                    truncation_latent + truncation * (style - truncation_latent)
+                )
+
+            styles = style_t
+
+        if len(styles) < 2:
+            inject_index = self.n_latent
+
+            if styles[0].ndim < 3:
+                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+
+            else:
+                latent = styles[0]
+
+        else:
+            if inject_index is None:
+                inject_index = random.randint(1, self.n_latent - 1)
+
+            if styles[0].ndim < 3:
+                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+                latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+
+                latent = torch.cat([latent, latent2], 1)
+            else:
+                latent = torch.cat([styles[0][:,0:inject_index], styles[1][:,inject_index:]], 1)
+
+        out = self.input(latent)
+        out = self.conv1(out, latent[:, 0], noise=noise[0])
+
+        skip = self.to_rgb1(out, latent[:, 1])
+
+        i = 1
+        for conv1, conv2, noise1, noise2, to_rgb in zip(
+            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+        ):
+            out = conv1(out, latent[:, i], noise=noise1)
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+
+            i += 2
+            if i > return_feature_ind:
+                return out, skip
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+
+        else:
+            return image, None
+
+
+class ConvLayer(nn.Sequential):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        bias=True,
+        activate=True,
+        dilation=1, ## modified
+    ):
+        layers = []
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
+
+            stride = 2
+            self.padding = 0
+
+        else:
+            stride = 1
+            self.padding = kernel_size // 2 + dilation-1 ## modified
+
+        layers.append(
+            EqualConv2d(
+                in_channel,
+                out_channel,
+                kernel_size,
+                padding=self.padding,
+                stride=stride,
+                bias=bias and not activate,
+                dilation=dilation, ## modified
+            )
+        )
+
+        if activate:
+            layers.append(FusedLeakyReLU(out_channel, bias=bias))
+
+        super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        self.conv1 = ConvLayer(in_channel, in_channel, 3)
+        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
+
+        self.skip = ConvLayer(
+            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+        )
+
+    def forward(self, input):
+        out = self.conv1(input)
+        out = self.conv2(out)
+
+        skip = self.skip(input)
+        out = (out + skip) / math.sqrt(2)
+
+        return out
+
+
+class Discriminator(nn.Module):
+    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
+        super().__init__()
+
+        channels = {
+            4: 512,
+            8: 512,
+            16: 512,
+            32: 512,
+            64: 256 * channel_multiplier,
+            128: 128 * channel_multiplier,
+            256: 64 * channel_multiplier,
+            512: 32 * channel_multiplier,
+            1024: 16 * channel_multiplier,
+        }
+
+        convs = [ConvLayer(3, channels[size], 1)]
+
+        log_size = int(math.log(size, 2))
+
+        in_channel = channels[size]
+
+        for i in range(log_size, 2, -1):
+            out_channel = channels[2 ** (i - 1)]
+
+            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
+
+            in_channel = out_channel
+
+        self.convs = nn.Sequential(*convs)
+
+        self.stddev_group = 4
+        self.stddev_feat = 1
+
+        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
+        self.final_linear = nn.Sequential(
+            EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
+            EqualLinear(channels[4], 1),
+        )
+
+    def forward(self, input):
+        out = self.convs(input)
+
+        batch, channel, height, width = out.shape
+        group = min(batch, self.stddev_group)
+        stddev = out.view(
+            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+        )
+        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+        stddev = stddev.repeat(group, 1, height, width)
+        out = torch.cat([out, stddev], 1)
+
+        out = self.final_conv(out)
+
+        out = out.view(batch, -1)
+        out = self.final_linear(out)
+
+        return out