v2

.idea/deployment.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="PublishConfigData" autoUpload="Always" serverName="21d" remoteFilesAllowedToDisappearOnAutoupload="false">
+  <component name="PublishConfigData" autoUpload="Always" serverName="14d" remoteFilesAllowedToDisappearOnAutoupload="false">
     <serverData>
       <paths name="14d">
         <serverdata>

configs/synthesizers/CyCleGAN.yml

@@ -1,4 +1,4 @@
-name: selfie2anime-cycleGAN
+name: huawei-cycylegan-7
 engine: CycleGAN
 result_dir: ./result
 max_pairs: 1000000
@@ -27,18 +27,33 @@ model:
     out_channels: 3
     base_channels: 64
     num_blocks: 9
+    use_transpose_conv: False
+    pre_activation: True
+#  discriminator:
+#    _type: MultiScaleDiscriminator
+#    _add_spectral_norm: True
+#    num_scale: 2
+#    down_sample_method: "bilinear"
+#    discriminator_cfg:
+#      _type: PatchDiscriminator
+#      in_channels: 3
+#      base_channels: 64
+#      num_conv: 4
+#      need_intermediate_feature: True
   discriminator:
     _type: PatchDiscriminator
     _add_spectral_norm: True
     in_channels: 3
     base_channels: 64
     num_conv: 4
+    need_intermediate_feature: False
+
 
 loss:
   gan:
-    loss_type: lsgan
+    loss_type: hinge
     weight: 1.0
-    real_label_val: 1.0
+    real_label_val: 1
     fake_label_val: 0.0
   cycle:
     level: 1
@@ -47,17 +62,22 @@ loss:
     level: 1
     weight: 10.0
   mgc:
-    weight: 5
+    weight: 1
+  fm:
+    weight: 0
+  edge:
+    weight: 0
+    hed_pretrained_model_path: ./network-bsds500.pytorch
 
 optimizers:
   generator:
     _type: Adam
-    lr: 0.0001
+    lr: 1e-4
     betas: [ 0.5, 0.999 ]
     weight_decay: 0.0001
   discriminator:
     _type: Adam
-    lr: 1e-4
+    lr: 4e-4
     betas: [ 0.5, 0.999 ]
     weight_decay: 0.0001
 
@@ -75,10 +95,21 @@ data:
       drop_last: True
     dataset:
       _type: GenerationUnpairedDataset
-      root_a: "/data/i2i/selfie2anime/trainA"
-      root_b: "/data/i2i/selfie2anime/trainB"
+      root_a: "/data/face2cartoon/all_face"
+      root_b: "/data/selfie2anime/trainB/"
       random_pair: True
-      pipeline:
+      pipeline_a:
+        - Load
+        - RandomCrop:
+            size: [ 178, 178 ]
+        - Resize:
+            size: [ 256, 256 ]
+        - RandomHorizontalFlip
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+      pipeline_b:
         - Load
         - Resize:
             size: [ 286, 286 ]
@@ -99,10 +130,18 @@ data:
       drop_last: False
     dataset:
       _type: GenerationUnpairedDataset
-      root_a: "/data/i2i/selfie2anime/testA"
-      root_b: "/data/i2i/selfie2anime/testB"
-      random_pair: False
-      pipeline:
+      root_a: "/data/face2cartoon/test/human"
+      root_b: "/data/face2cartoon/test/anime"
+      random_pair: True
+      pipeline_a:
+        - Load
+        - Resize:
+            size: [ 256, 256 ]
+        - ToTensor
+        - Normalize:
+            mean: [ 0.5, 0.5, 0.5 ]
+            std: [ 0.5, 0.5, 0.5 ]
+      pipeline_b:
         - Load
         - Resize:
             size: [ 256, 256 ]

data/dataset/image_translation.py

@@ -38,9 +38,9 @@ class SingleFolderDataset(Dataset):
 
 @DATASET.register_module()
 class GenerationUnpairedDataset(Dataset):
-    def __init__(self, root_a, root_b, random_pair, pipeline, with_path=False):
-        self.A = SingleFolderDataset(root_a, pipeline, with_path)
-        self.B = SingleFolderDataset(root_b, pipeline, with_path)
+    def __init__(self, root_a, root_b, random_pair, pipeline_a, pipeline_b, with_path=False):
+        self.A = SingleFolderDataset(root_a, pipeline_a, with_path)
+        self.B = SingleFolderDataset(root_b, pipeline_b, with_path)
         self.with_path = with_path
         self.random_pair = random_pair
 

engine/CycleGAN.py

@@ -1,11 +1,13 @@
 from itertools import chain
 
+import ignite.distributed as idist
 import torch
 
 from engine.base.i2i import EngineKernel, run_kernel
 from engine.util.build import build_model
 from engine.util.container import GANImageBuffer, LossContainer
-from engine.util.loss import pixel_loss, gan_loss
+from engine.util.loss import pixel_loss, gan_loss, feature_match_loss
+from loss.I2I.edge_loss import EdgeLoss
 from loss.I2I.minimal_geometry_distortion_constraint_loss import MGCLoss
 from model.weight_init import generation_init_weights
 
@@ -17,7 +19,10 @@ class CycleGANEngineKernel(EngineKernel):
         self.gan_loss = gan_loss(config.loss.gan)
         self.cycle_loss = LossContainer(config.loss.cycle.weight, pixel_loss(config.loss.cycle.level))
         self.id_loss = LossContainer(config.loss.id.weight, pixel_loss(config.loss.id.level))
-        self.mgc_loss = LossContainer(config.loss.mgc.weight, MGCLoss())
+        self.mgc_loss = LossContainer(config.loss.mgc.weight, MGCLoss("opposite"))
+        self.fm_loss = LossContainer(config.loss.fm.weight, feature_match_loss(1, "same"))
+        self.edge_loss = LossContainer(config.loss.edge.weight, EdgeLoss(
+            "HED", hed_pretrained_model_path=config.loss.edge.hed_pretrained_model_path).to(idist.device()))
         self.image_buffers = {k: GANImageBuffer(config.data.train.buffer_size or 50) for k in
                               self.discriminators.keys()}
 
@@ -64,8 +69,12 @@ class CycleGANEngineKernel(EngineKernel):
             loss[f"cycle_{ph}"] = self.cycle_loss(generated["a2b2a" if ph == "a" else "b2a2b"], batch[ph])
             loss[f"id_{ph}"] = self.id_loss(generated[f"{ph}2{ph}"], batch[ph])
             loss[f"mgc_{ph}"] = self.mgc_loss(generated["a2b" if ph == "a" else "b2a"], batch[ph])
-            loss[f"gan_{ph}"] = self.config.loss.gan.weight * self.gan_loss(
-                self.discriminators[ph](generated["a2b" if ph == "b" else "b2a"]), True)
+            prediction_fake = self.discriminators[ph](generated["a2b" if ph == "b" else "b2a"])
+            loss[f"gan_{ph}"] = self.config.loss.gan.weight * self.gan_loss(prediction_fake, True)
+            if self.fm_loss.weight > 0:
+                prediction_real = self.discriminators[ph](batch[ph])
+                loss[f"feature_match_{ph}"] = self.fm_loss(prediction_fake, prediction_real)
+            loss[f"edge_{ph}"] = self.edge_loss(generated["a2b" if ph == "a" else "b2a"], batch[ph], gt_is_edge=False)
         return loss
 
     def criterion_discriminators(self, batch, generated) -> dict:

engine/base/i2i.py

@@ -101,9 +101,12 @@ class EngineKernel(object):
 
 
 def _remove_no_grad_loss(loss_dict):
+    need_to_pop = []
     for k in loss_dict:
         if not isinstance(loss_dict[k], torch.Tensor):
-            loss_dict.pop(k)
+            need_to_pop.append(k)
+    for k in need_to_pop:
+        loss_dict.pop(k)
     return loss_dict
 
 

engine/util/loss.py

@@ -23,3 +23,19 @@ def mse_loss(x, target_flag):
 
 def bce_loss(x, target_flag):
     return F.binary_cross_entropy_with_logits(x, torch.ones_like(x) if target_flag else torch.zeros_like(x))
+
+
+def feature_match_loss(level, weight_policy):
+    compare_loss = pixel_loss(level)
+    assert weight_policy in ["same", "exponential_decline"]
+
+    def fm_loss(generated_features, target_features):
+        num_scale = len(generated_features)
+        loss = 0
+        for s_i in range(num_scale):
+            for i in range(len(generated_features[s_i]) - 1):
+                weight = 1 if weight_policy == "same" else 2 ** i
+                loss += weight * compare_loss(generated_features[s_i][i], target_features[s_i][i].detach()) / num_scale
+        return loss
+
+    return fm_loss

loss/I2I/minimal_geometry_distortion_constraint_loss.py

@@ -105,10 +105,12 @@ class MGCLoss(nn.Module):
     Minimal Geometry-Distortion Constraint Loss from https://openreview.net/forum?id=R5M7Mxl1xZ
     """
 
-    def __init__(self, beta=0.5, lambda_=0.05, device=idist.device()):
+    def __init__(self, mi_to_loss_way="opposite", beta=0.5, lambda_=0.05, device=idist.device()):
         super().__init__()
         self.beta = beta
         self.lambda_ = lambda_
+        assert mi_to_loss_way in ["opposite", "reciprocal"]
+        self.mi_to_loss_way = mi_to_loss_way
         mu_y, mu_x = torch.meshgrid([torch.arange(-1, 1.25, 0.25), torch.arange(-1, 1.25, 0.25)])
         self.mu_x = mu_x.flatten().to(device)
         self.mu_y = mu_y.flatten().to(device)
@@ -134,6 +136,8 @@ class MGCLoss(nn.Module):
 
     def forward(self, fake, real):
         rSMI = self.batch_rSMI(fake, real, self.mu_x, self.mu_y, self.beta, self.lambda_, self.R)
+        if self.mi_to_loss_way == "reciprocal":
+            return 1/rSMI.mean()
         return -rSMI.mean()
 
 

loss/gan.py

@@ -1,5 +1,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
+import torch
 
 
 class GANLoss(nn.Module):
@@ -10,7 +11,7 @@ class GANLoss(nn.Module):
         self.fake_label_val = fake_label_val
         self.loss_type = loss_type
 
-    def forward(self, prediction, target_is_real: bool, is_discriminator=False):
+    def single_forward(self, prediction, target_is_real: bool, is_discriminator=False):
         """
         gan loss forward
         :param prediction: network prediction
@@ -37,3 +38,14 @@ class GANLoss(nn.Module):
             return loss
         else:
             raise NotImplementedError(f'GAN type {self.loss_type} is not implemented.')
+
+    def forward(self, prediction, target_is_real: bool, is_discriminator=False):
+        if isinstance(prediction, torch.Tensor):
+            # origin
+            return self.single_forward(prediction, target_is_real, is_discriminator)
+        elif isinstance(prediction, list):
+            # for multi scale discriminator, e.g. MultiScaleDiscriminator
+            loss = 0
+            for p in prediction:
+                loss += self.single_forward(p[-1], target_is_real, is_discriminator)
+            return loss

model/__init__.py

@@ -2,3 +2,4 @@ from model.registry import MODEL, NORMALIZATION
 import model.base.normalization
 import model.image_translation.UGATIT
 import model.image_translation.CycleGAN
+import model.image_translation.pix2pixHD

model/image_translation/pix2pixHD.py

@@ -0,0 +1,29 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from model import MODEL
+
+
+@MODEL.register_module()
+class MultiScaleDiscriminator(nn.Module):
+    def __init__(self, num_scale, discriminator_cfg, down_sample_method="avg"):
+        super().__init__()
+        assert down_sample_method in ["avg", "bilinear"]
+        self.down_sample_method = down_sample_method
+
+        self.discriminator_list = nn.ModuleList([
+            MODEL.build_with(discriminator_cfg) for _ in range(num_scale)
+        ])
+
+    def down_sample(self, x):
+        if self.down_sample_method == "avg":
+            return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)
+        if self.down_sample_method == "bilinear":
+            return F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=True)
+
+    def forward(self, x):
+        results = []
+        for discriminator in self.discriminator_list:
+            results.append(discriminator(x))
+            x = self.down_sample(x)
+        return results

util/registry.py

@@ -53,11 +53,9 @@ class _Registry:
         else:
             raise TypeError(f'cfg must be a dict or a str, but got {type(cfg)}')
 
-        for k in args:
-            assert isinstance(k, str)
-            if k.startswith("_"):
-                warnings.warn(f"got param start with `_`: {k}, will remove it")
-                args.pop(k)
+        for invalid_key in [k for k in args.keys() if k.startswith("_")]:
+            warnings.warn(f"got param start with `_`: {invalid_key}, will remove it")
+            args.pop(invalid_key)
 
         if not (isinstance(default_args, dict) or default_args is None):
             raise TypeError('default_args must be a dict or None, '