TAFG good result

configs/synthesizers/TAFG.yml

@@ -23,7 +23,8 @@ model:
     _bn_to_sync_bn: False
     style_in_channels: 3
     content_in_channels: 24
-    num_blocks: 8
+    num_adain_blocks: 8
+    num_res_blocks: 0
   discriminator:
     _type: MultiScaleDiscriminator
     num_scale: 2
@@ -47,21 +48,17 @@ loss:
       "11": 0.125
       "20": 0.25
       "29": 1
-    criterion: 'L2'
+    criterion: 'L1'
     style_loss: False
     perceptual_loss: True
-    weight: 0.5
+    weight: 10
   style:
     layer_weights:
-      "1": 0.03125
+      "3": 1
-      "6": 0.0625
+    criterion: 'L1'
-      "11": 0.125
-      "20": 0.25
-      "29": 1
-    criterion: 'L2'
     style_loss: True
     perceptual_loss: False
-    weight: 0
+    weight: 10
   fm:
     level: 1
     weight: 10
@@ -71,6 +68,9 @@ loss:
   style_recon:
     level: 1
     weight: 0
+  edge:
+    weight: 10
+    hed_pretrained_model_path: ./network-bsds500.pytorch
 
 optimizers:
   generator:
@@ -91,7 +91,7 @@ data:
       target_lr: 0
     buffer_size: 50
     dataloader:
-      batch_size: 24
+      batch_size: 8
       shuffle: True
       num_workers: 2
       pin_memory: True

configs/synthesizers/TSIT.yml

@@ -0,0 +1,146 @@
+name: self2anime-TSIT
+engine: TSIT
+result_dir: ./result
+max_pairs: 1500000
+
+handler:
+  clear_cuda_cache: True
+  set_epoch_for_dist_sampler: True
+  checkpoint:
+    epoch_interval: 1 # checkpoint once per `epoch_interval` epoch
+    n_saved: 2
+  tensorboard:
+    scalar: 100 # log scalar `scalar` times per epoch
+    image: 2 # log image `image` times per epoch
+
+
+misc:
+  random_seed: 324
+
+model:
+  generator:
+    _type: TSIT-Generator
+    _bn_to_sync_bn: True
+    style_in_channels: 3
+    content_in_channels: 3
+    num_blocks: 5
+    input_layer_type: "conv7x7"
+  discriminator:
+    _type: MultiScaleDiscriminator
+    num_scale: 2
+    discriminator_cfg:
+      _type: PatchDiscriminator
+      in_channels: 3
+      base_channels: 64
+      use_spectral: True
+      need_intermediate_feature: True
+
+loss:
+  gan:
+    loss_type: hinge
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    weight: 1.0
+  perceptual:
+    layer_weights:
+      "1": 0.03125
+      "6": 0.0625
+      "11": 0.125
+      "20": 0.25
+      "29": 1
+    criterion: 'L1'
+    style_loss: False
+    perceptual_loss: True
+    weight: 1
+  style:
+    layer_weights:
+      "1": 0.03125
+      "6": 0.0625
+      "11": 0.125
+      "20": 0.25
+      "29": 1
+    criterion: 'L2'
+    style_loss: True
+    perceptual_loss: False
+    weight: 0
+  fm:
+    level: 1
+    weight: 1
+
+optimizers:
+  generator:
+    _type: Adam
+    lr: 0.0001
+    betas: [ 0, 0.9 ]
+    weight_decay: 0.0001
+  discriminator:
+    _type: Adam
+    lr: 4e-4
+    betas: [ 0, 0.9 ]
+    weight_decay: 0.0001
+
+data:
+  train:
+    scheduler:
+      start_proportion: 0.5
+      target_lr: 0
+    buffer_size: 50
+    dataloader:
+      batch_size: 1
+      shuffle: True
+      num_workers: 2
+      pin_memory: True
+      drop_last: True
+    dataset:
+      _type: GenerationUnpairedDatasetWithEdge
+      root_a: "/data/i2i/VoxCeleb2Anime/trainA"
+      root_b: "/data/i2i/VoxCeleb2Anime/trainB"
+      edges_path: "/data/i2i/VoxCeleb2Anime/edges"
+      landmarks_path: "/data/i2i/VoxCeleb2Anime/landmarks"
+      edge_type: "landmark_hed"
+      size: [ 128, 128 ]
+      random_pair: True
+      pipeline:
+        - Load
+        - Resize:
+            size: [ 128, 128 ]
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+  test:
+    dataloader:
+      batch_size: 8
+      shuffle: False
+      num_workers: 1
+      pin_memory: False
+      drop_last: False
+    dataset:
+      _type: GenerationUnpairedDatasetWithEdge
+      root_a: "/data/i2i/VoxCeleb2Anime/testA"
+      root_b: "/data/i2i/VoxCeleb2Anime/testB"
+      edges_path: "/data/i2i/VoxCeleb2Anime/edges"
+      landmarks_path: "/data/i2i/VoxCeleb2Anime/landmarks"
+      edge_type: "landmark_hed"
+      random_pair: False
+      size: [ 128, 128 ]
+      pipeline:
+        - Load
+        - Resize:
+            size: [ 128, 128 ]
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+    video_dataset:
+      _type: SingleFolderDataset
+      root: "/data/i2i/VoxCeleb2Anime/test_video_frames/"
+      with_path: True
+      pipeline:
+        - Load
+        - Resize:
+            size: [ 256, 256 ]
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]

engine/TAFG.py

@@ -1,20 +1,17 @@
 from itertools import chain
 
-from omegaconf import OmegaConf
+import ignite.distributed as idist
-
 import torch
 import torch.nn as nn
-import ignite.distributed as idist
 from ignite.engine import Events
-
 from omegaconf import read_write, OmegaConf
 
-from model.weight_init import generation_init_weights
-from loss.I2I.perceptual_loss import PerceptualLoss
-from loss.gan import GANLoss
-
 from engine.base.i2i import EngineKernel, run_kernel
 from engine.util.build import build_model
+from loss.I2I.edge_loss import EdgeLoss
+from loss.I2I.perceptual_loss import PerceptualLoss
+from loss.gan import GANLoss
+from model.weight_init import generation_init_weights
 
 
 class TAFGEngineKernel(EngineKernel):
@@ -24,6 +21,10 @@ class TAFGEngineKernel(EngineKernel):
         perceptual_loss_cfg.pop("weight")
         self.perceptual_loss = PerceptualLoss(**perceptual_loss_cfg).to(idist.device())
 
+        style_loss_cfg = OmegaConf.to_container(config.loss.style)
+        style_loss_cfg.pop("weight")
+        self.style_loss = PerceptualLoss(**style_loss_cfg).to(idist.device())
+
         gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
         gan_loss_cfg.pop("weight")
         self.gan_loss = GANLoss(**gan_loss_cfg).to(idist.device())
@@ -32,6 +33,9 @@ class TAFGEngineKernel(EngineKernel):
         self.recon_loss = nn.L1Loss() if config.loss.recon.level == 1 else nn.MSELoss()
         self.style_recon_loss = nn.L1Loss() if config.loss.style_recon.level == 1 else nn.MSELoss()
 
+        self.edge_loss = EdgeLoss("HED", hed_pretrained_model_path=config.loss.edge.hed_pretrained_model_path).to(
+            idist.device())
+
     def _process_batch(self, batch, inference=False):
         # batch["b"] = batch["b"] if inference else batch["b"][0].expand(batch["a"].size())
         return batch
@@ -74,7 +78,9 @@ class TAFGEngineKernel(EngineKernel):
         batch = self._process_batch(batch)
         loss = dict()
         loss_perceptual, _ = self.perceptual_loss(generated["b"], batch["a"])
-        loss["perceptual"] = loss_perceptual * self.config.loss.perceptual.weight
+        _, loss_style = self.style_loss(generated["a"], batch["a"])
+        loss["style"] = self.config.loss.style.weight * loss_style
+        loss["perceptual"] = self.config.loss.perceptual.weight * loss_perceptual
         for phase in "ab":
             pred_fake = self.discriminators[phase](generated[phase])
             loss[f"gan_{phase}"] = 0
@@ -93,10 +99,7 @@ class TAFGEngineKernel(EngineKernel):
                         loss_fm += self.fm_loss(pred_fake[i][j], pred_real[i][j].detach()) / num_scale_discriminator
                 loss[f"fm_{phase}"] = self.config.loss.fm.weight * loss_fm
         loss["recon"] = self.config.loss.recon.weight * self.recon_loss(generated["a"], batch["a"])
-        # loss["style_recon"] = self.config.loss.style_recon.weight * self.style_recon_loss(
+        loss["edge"] = self.config.loss.edge.weight * self.edge_loss(generated["b"], batch["edge_a"][:, 0:1, :, :])
-        #     self.generators["main"].module.style_encoders["b"](batch["b"]),
-        #     self.generators["main"].module.style_encoders["b"](generated["b"])
-        # )
         return loss
 
     def criterion_discriminators(self, batch, generated) -> dict:

engine/TSIT.py

@@ -0,0 +1,106 @@
+from itertools import chain
+
+import ignite.distributed as idist
+import torch
+import torch.nn as nn
+from omegaconf import OmegaConf
+
+from engine.base.i2i import EngineKernel, run_kernel
+from engine.util.build import build_model
+from loss.I2I.perceptual_loss import PerceptualLoss
+from loss.gan import GANLoss
+from model.weight_init import generation_init_weights
+
+
+class TSITEngineKernel(EngineKernel):
+    def __init__(self, config):
+        super().__init__(config)
+        perceptual_loss_cfg = OmegaConf.to_container(config.loss.perceptual)
+        perceptual_loss_cfg.pop("weight")
+        self.perceptual_loss = PerceptualLoss(**perceptual_loss_cfg).to(idist.device())
+
+        gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
+        gan_loss_cfg.pop("weight")
+        self.gan_loss = GANLoss(**gan_loss_cfg).to(idist.device())
+
+        self.fm_loss = nn.L1Loss() if config.loss.fm.level == 1 else nn.MSELoss()
+
+    def build_models(self) -> (dict, dict):
+        generators = dict(
+            main=build_model(self.config.model.generator)
+        )
+        discriminators = dict(
+            b=build_model(self.config.model.discriminator)
+        )
+        self.logger.debug(discriminators["b"])
+        self.logger.debug(generators["main"])
+
+        for m in chain(generators.values(), discriminators.values()):
+            generation_init_weights(m)
+
+        return generators, discriminators
+
+    def setup_after_g(self):
+        for discriminator in self.discriminators.values():
+            discriminator.requires_grad_(True)
+
+    def setup_before_g(self):
+        for discriminator in self.discriminators.values():
+            discriminator.requires_grad_(False)
+
+    def forward(self, batch, inference=False) -> dict:
+        with torch.set_grad_enabled(not inference):
+            fake = dict(
+                b=self.generators["main"](content_img=batch["a"], style_img=batch["b"])
+            )
+        return fake
+
+    def criterion_generators(self, batch, generated) -> dict:
+        loss = dict()
+        loss_perceptual, _ = self.perceptual_loss(generated["b"], batch["a"])
+        loss["perceptual"] = loss_perceptual * self.config.loss.perceptual.weight
+        for phase in "b":
+            pred_fake = self.discriminators[phase](generated[phase])
+            loss[f"gan_{phase}"] = 0
+            for sub_pred_fake in pred_fake:
+                # last output is actual prediction
+                loss[f"gan_{phase}"] += self.config.loss.gan.weight * self.gan_loss(sub_pred_fake[-1], True)
+
+            if self.config.loss.fm.weight > 0 and phase == "b":
+                pred_real = self.discriminators[phase](batch[phase])
+                loss_fm = 0
+                num_scale_discriminator = len(pred_fake)
+                for i in range(num_scale_discriminator):
+                    # last output is the final prediction, so we exclude it
+                    num_intermediate_outputs = len(pred_fake[i]) - 1
+                    for j in range(num_intermediate_outputs):
+                        loss_fm += self.fm_loss(pred_fake[i][j], pred_real[i][j].detach()) / num_scale_discriminator
+                loss[f"fm_{phase}"] = self.config.loss.fm.weight * loss_fm
+        return loss
+
+    def criterion_discriminators(self, batch, generated) -> dict:
+        loss = dict()
+        for phase in self.discriminators.keys():
+            pred_real = self.discriminators[phase](batch[phase])
+            pred_fake = self.discriminators[phase](generated[phase].detach())
+            loss[f"gan_{phase}"] = 0
+            for i in range(len(pred_fake)):
+                loss[f"gan_{phase}"] += (self.gan_loss(pred_fake[i][-1], False, is_discriminator=True)
+                                         + self.gan_loss(pred_real[i][-1], True, is_discriminator=True)) / 2
+        return loss
+
+    def intermediate_images(self, batch, generated) -> dict:
+        """
+        returned dict must be like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
+        :param batch:
+        :param generated: dict of images
+        :return: dict like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
+        """
+        return dict(
+            b=[batch["a"].detach(), batch["b"].detach(), generated["b"].detach()]
+        )
+
+
+def run(task, config, _):
+    kernel = TSITEngineKernel(config)
+    run_kernel(task, config, kernel)

model/GAN/TAFG.py

@@ -1,9 +1,10 @@
 import torch
 import torch.nn as nn
-from .base import ResidualBlock
-from model.registry import MODEL
 from torchvision.models import vgg19
+
 from model.normalization import select_norm_layer
+from model.registry import MODEL
+from .base import ResidualBlock
 
 
 class VGG19StyleEncoder(nn.Module):
@@ -169,25 +170,37 @@ class StyleGenerator(nn.Module):
 
 @MODEL.register_module("TAFG-Generator")
 class Generator(nn.Module):
-    def __init__(self, style_in_channels, content_in_channels=3, out_channels=3, style_dim=512, num_blocks=8,
+    def __init__(self, style_in_channels, content_in_channels=3, out_channels=3, style_dim=512,
+                 num_adain_blocks=8, num_res_blocks=4,
                  base_channels=64, padding_mode="reflect"):
         super(Generator, self).__init__()
-        self.num_blocks = num_blocks
+        self.num_adain_blocks=num_adain_blocks
         self.style_encoders = nn.ModuleDict({
-            "a": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_blocks,
+            "a": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_adain_blocks,
                                 base_channels=base_channels, padding_mode=padding_mode),
-            "b": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_blocks,
+            "b": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_adain_blocks,
                                 base_channels=base_channels, padding_mode=padding_mode),
         })
-        self.content_encoder = ContentEncoder(content_in_channels, base_channels, num_blocks=num_blocks,
+        self.content_encoder = ContentEncoder(content_in_channels, base_channels, num_blocks=8,
                                               padding_mode=padding_mode, norm_type="IN")
         res_block_channels = 2 ** 2 * base_channels
-        self.adain_resnet_a = nn.ModuleList([
+
-            ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_blocks)
+        self.resnet = nn.ModuleDict({
-        ])
+            "a": nn.Sequential(*[
-        self.adain_resnet_b = nn.ModuleList([
+                ResidualBlock(res_block_channels, padding_mode, "IN", use_bias=True) for _ in range(num_res_blocks)
-            ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_blocks)
+            ]),
-        ])
+            "b": nn.Sequential(*[
+                ResidualBlock(res_block_channels, padding_mode, "IN", use_bias=True) for _ in range(num_res_blocks)
+            ])
+        })
+        self.adain_resnet = nn.ModuleDict({
+            "a": nn.ModuleList([
+                ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_adain_blocks)
+            ]),
+            "b": nn.ModuleList([
+                ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_adain_blocks)
+            ])
+        })
 
         self.decoders = nn.ModuleDict({
             "a": Decoder(out_channels, base_channels, norm_type="LN", num_blocks=0, padding_mode=padding_mode),
@@ -196,10 +209,10 @@ class Generator(nn.Module):
 
     def forward(self, content_img, style_img, which_decoder: str = "a"):
         x = self.content_encoder(content_img)
+        x = self.resnet[which_decoder](x)
         styles = self.style_encoders[which_decoder](style_img)
-        styles = torch.chunk(styles, self.num_blocks * 2, dim=1)
+        styles = torch.chunk(styles, self.num_adain_blocks * 2, dim=1)
-        resnet = self.adain_resnet_a if which_decoder == "a" else self.adain_resnet_b
+        for i, ar in enumerate(self.adain_resnet[which_decoder]):
-        for i, ar in enumerate(resnet):
             ar.norm1.set_style(styles[2 * i])
             ar.norm2.set_style(styles[2 * i + 1])
             x = ar(x)

model/GAN/TSIT.py

@@ -0,0 +1,192 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from model import MODEL
+from model.normalization import AdaptiveInstanceNorm2d
+from model.normalization import select_norm_layer
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, padding_mode='zeros', norm_type="IN", use_bias=None,
+                 use_spectral=True):
+        super().__init__()
+        self.padding_mode = padding_mode
+        self.use_bias = use_bias
+        self.use_spectral = use_spectral
+        if use_bias is None:
+            # Only for IN, use bias since it does not have affine parameters.
+            self.use_bias = norm_type == "IN"
+        norm_layer = select_norm_layer(norm_type)
+        self.main = nn.Sequential(
+            self.conv_block(in_channels, in_channels),
+            norm_layer(in_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+            self.conv_block(in_channels, out_channels),
+            norm_layer(out_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+        self.skip = nn.Sequential(
+            self.conv_block(in_channels, out_channels, padding=0, kernel_size=1),
+            norm_layer(out_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+        )
+
+    def conv_block(self, in_channels, out_channels, kernel_size=3, padding=1):
+        conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding,
+                         padding_mode=self.padding_mode, bias=self.use_bias)
+        if self.use_spectral:
+            return nn.utils.spectral_norm(conv)
+        else:
+            return conv
+
+    def forward(self, x):
+        return self.main(x) + self.skip(x)
+
+
+class Interpolation(nn.Module):
+    def __init__(self, scale_factor=None, mode='nearest', align_corners=None):
+        super(Interpolation, self).__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners,
+                             recompute_scale_factor=False)
+
+    def __repr__(self):
+        return f"DownSampling(scale_factor={self.scale_factor}, mode={self.mode}, align_corners={self.align_corners})"
+
+
+class FADE(nn.Module):
+    def __init__(self, use_spectral, features_channels, in_channels, affine=False, track_running_stats=True):
+        super().__init__()
+        self.bn = nn.BatchNorm2d(num_features=in_channels, affine=affine, track_running_stats=track_running_stats)
+        self.alpha_conv = conv_block(use_spectral, features_channels, in_channels, kernel_size=3, padding=1,
+                                     padding_mode="zeros")
+        self.beta_conv = conv_block(use_spectral, features_channels, in_channels, kernel_size=3, padding=1,
+                                    padding_mode="zeros")
+
+    def forward(self, x, feature):
+        alpha = self.alpha_conv(feature)
+        beta = self.beta_conv(feature)
+        x = self.bn(x)
+        return alpha * x + beta
+
+
+class FADEResBlock(nn.Module):
+    def __init__(self, use_spectral, features_channels, in_channels, out_channels):
+        super().__init__()
+        self.main = nn.Sequential(
+            FADE(use_spectral, features_channels, in_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+            conv_block(use_spectral, in_channels, in_channels, kernel_size=3, padding=1),
+            FADE(use_spectral, features_channels, in_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+            conv_block(use_spectral, in_channels, out_channels, kernel_size=3, padding=1),
+        )
+        self.skip = nn.Sequential(
+            FADE(use_spectral, features_channels, in_channels),
+            nn.LeakyReLU(0.2, inplace=True),
+            conv_block(use_spectral, in_channels, out_channels, kernel_size=1, padding=0),
+        )
+        self.up_sample = Interpolation(2, mode="nearest")
+
+    @staticmethod
+    def forward_with_fade(module, x, feature):
+        for layer in module:
+            if layer.__class__.__name__ == "FADE":
+                x = layer(x, feature)
+            else:
+                x = layer(x)
+        return x
+
+    def forward(self, x, feature):
+        out = self.forward_with_fade(self.main, x, feature) + self.forward_with_fade(self.main, x, feature)
+        return self.up_sample(out)
+
+
+def conv_block(use_spectral, in_channels, out_channels, **kwargs):
+    conv = nn.Conv2d(in_channels, out_channels, **kwargs)
+    return nn.utils.spectral_norm(conv) if use_spectral else conv
+
+
+@MODEL.register_module("TSIT-Generator")
+class TSITGenerator(nn.Module):
+    def __init__(self, num_blocks=7, base_channels=64, content_in_channels=3, style_in_channels=3,
+                 out_channels=3, use_spectral=True, input_layer_type="conv1x1"):
+        super().__init__()
+        self.num_blocks = num_blocks
+        self.base_channels = base_channels
+        self.use_spectral = use_spectral
+
+        self.content_input_layer = self.build_input_layer(content_in_channels, base_channels, input_layer_type)
+        self.style_input_layer = self.build_input_layer(style_in_channels, base_channels, input_layer_type)
+        self.content_stream = self.build_stream()
+        self.style_stream = self.build_stream()
+        self.generator = self.build_generator()
+        self.end_conv = nn.Sequential(
+            conv_block(use_spectral, base_channels, out_channels, kernel_size=7, padding=3, padding_mode="zeros"),
+            nn.Tanh()
+        )
+
+    def build_generator(self):
+        stream_sequence = []
+        multiple_now = min(2 ** self.num_blocks, 2 ** 4)
+        for i in range(1, self.num_blocks + 1):
+            m = self.num_blocks - i
+            multiple_prev = multiple_now
+            multiple_now = min(2 ** m, 2 ** 4)
+            stream_sequence.append(nn.Sequential(
+                AdaptiveInstanceNorm2d(multiple_prev * self.base_channels),
+                FADEResBlock(self.use_spectral, multiple_prev * self.base_channels, multiple_prev * self.base_channels,
+                             multiple_now * self.base_channels)
+            ))
+        return nn.ModuleList(stream_sequence)
+
+    def build_input_layer(self, in_channels, out_channels, input_layer_type="conv7x7"):
+        if input_layer_type == "conv7x7":
+            return nn.Sequential(
+                conv_block(self.use_spectral, in_channels, out_channels, kernel_size=7, stride=1,
+                           padding_mode="zeros", padding=3, bias=True),
+                select_norm_layer("IN")(out_channels),
+                nn.ReLU(inplace=True)
+            )
+        elif input_layer_type == "conv1x1":
+            return conv_block(self.use_spectral, in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        else:
+            raise NotImplemented
+
+    def build_stream(self):
+        multiple_now = 1
+        stream_sequence = []
+        for i in range(1, self.num_blocks + 1):
+            multiple_prev = multiple_now
+            multiple_now = min(2 ** i, 2 ** 4)
+            stream_sequence.append(nn.Sequential(
+                Interpolation(scale_factor=0.5, mode="nearest"),
+                ResBlock(multiple_prev * self.base_channels, multiple_now * self.base_channels,
+                         use_spectral=self.use_spectral)
+            ))
+        return nn.ModuleList(stream_sequence)
+
+    def forward(self, content_img, style_img):
+        c = self.content_input_layer(content_img)
+        s = self.style_input_layer(style_img)
+        content_features = []
+        style_features = []
+        for i in range(self.num_blocks):
+            s = self.style_stream[i](s)
+            c = self.content_stream[i](c)
+            content_features.append(c)
+            style_features.append(s)
+        z = torch.randn(size=content_features[-1].size(), device=content_features[-1].device)
+
+        for i in range(self.num_blocks):
+            m = - i - 1
+            layer = self.generator[i]
+            layer[0].set_style(torch.cat(torch.std_mean(style_features[m], dim=[2, 3]), dim=1))
+            z = layer[0](z)
+            z = layer[1](z, content_features[m])
+        return self.end_conv(z)

model/__init__.py

@@ -4,3 +4,4 @@ import model.GAN.TAFG
 import model.GAN.UGATIT
 import model.GAN.wrapper
 import model.GAN.base
+import model.GAN.TSIT

model/normalization.py

@@ -1,6 +1,7 @@
-import torch.nn as nn
 import functools
+
 import torch
+import torch.nn as nn
 
 
 def select_norm_layer(norm_type):

run.sh

@@ -5,16 +5,18 @@ TASK=$2
 GPUS=$3
 MORE_ARG=${*:4}
 
+RANDOM_MASTER=$(shuf -i 2000-65000 -n 1)
+
 _command="print(len('${GPUS}'.split(',')))"
 GPU_COUNT=$(python3 -c "${_command}")
 
 echo "GPU_COUNT:${GPU_COUNT}"
 
 echo CUDA_VISIBLE_DEVICES=$GPUS \
-PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPU_COUNT" \
+  PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPU_COUNT" \
   main.py "$TASK" "$CONFIG" --backup_config --setup_output_dir --setup_random_seed "$MORE_ARG"
 
 CUDA_VISIBLE_DEVICES=$GPUS \
-PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPU_COUNT" \
+  PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPU_COUNT" \
+  --master_port=${RANDOM_MASTER} \
   main.py "$TASK" "$CONFIG" $MORE_ARG --backup_config --setup_output_dir --setup_random_seed
-

tool/dump_tensorboard.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+
+# edit from https://gist.github.com/hysts/81a0d30ac4f33dfa0c8859383aec42c2
+
+import argparse
+import pathlib
+
+import cv2
+import numpy as np
+from tensorboard.backend.event_processing import event_accumulator
+
+
+def save(outdir: pathlib.Path, tag, event_acc):
+    events = event_acc.Images(tag)
+
+    for index, event in enumerate(events):
+        s = np.frombuffer(event.encoded_image_string, dtype=np.uint8)
+        image = cv2.imdecode(s, cv2.IMREAD_COLOR)
+        outpath = outdir / f"{tag.replace('/', '_')}@{index}.png"
+        cv2.imwrite(outpath.as_posix(), image)
+
+
+# ffmpeg -framerate 1 -i ./tmp/test_b/%04d.jpg -vcodec mpeg4 test_b.mp4
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--path', type=str, required=True)
+    parser.add_argument('--outdir', type=str, required=True)
+    parser.add_argument("--tag", type=str, required=False)
+    args = parser.parse_args()
+
+    event_acc = event_accumulator.EventAccumulator(args.path, size_guidance={'images': 0})
+    event_acc.Reload()
+
+    outdir = pathlib.Path(args.outdir)
+    outdir.mkdir(exist_ok=True, parents=True)
+
+    if args.tag is None:
+        for tag in event_acc.Tags()['images']:
+            save(outdir, tag, event_acc)
+    else:
+        assert args.tag in event_acc.Tags()['images'], f"{args.tag} not in {event_acc.Tags()['images']}"
+        save(outdir, args.tag, event_acc)
+
+
+if __name__ == '__main__':
+    main()