base model, Norm&Conv&ResNet

model/base/normalization.py

@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from model import NORMALIZATION
+from model.base.module import Conv2dBlock
+
+_VALID_NORM_AND_ABBREVIATION = dict(
+    IN="InstanceNorm2d",
+    BN="BatchNorm2d",
+)
+
+for abbr, name in _VALID_NORM_AND_ABBREVIATION.items():
+    NORMALIZATION.register_module(module=getattr(nn, name), name=abbr)
+
+
+@NORMALIZATION.register_module("ADE")
+class AdaptiveDenormalization(nn.Module):
+    def __init__(self, num_features, base_norm_type="BN"):
+        super().__init__()
+        self.num_features = num_features
+        self.base_norm_type = base_norm_type
+        self.norm = self.base_norm(num_features)
+        self.gamma = None
+        self.beta = None
+        self.have_set_condition = False
+
+    def base_norm(self, num_features):
+        if self.base_norm_type == "IN":
+            return nn.InstanceNorm2d(num_features)
+        elif self.base_norm_type == "BN":
+            return nn.BatchNorm2d(num_features, affine=False, track_running_stats=True)
+
+    def set_condition(self, gamma, beta):
+        self.gamma, self.beta = gamma, beta
+        self.have_set_condition = True
+
+    def forward(self, x):
+        assert self.have_set_condition
+        x = self.norm(x)
+        x = self.gamma * x + self.beta
+        self.have_set_condition = False
+        return x
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(num_features={self.num_features}, " \
+               f"base_norm_type={self.base_norm_type})"
+
+
+@NORMALIZATION.register_module("AdaIN")
+class AdaptiveInstanceNorm2d(AdaptiveDenormalization):
+    def __init__(self, num_features: int):
+        super().__init__(num_features, "IN")
+        self.num_features = num_features
+
+    def set_style(self, style):
+        style = style.view(*style.size(), 1, 1)
+        gamma, beta = style.chunk(2, 1)
+        super().set_condition(gamma, beta)
+
+
+@NORMALIZATION.register_module("FADE")
+class FeatureAdaptiveDenormalization(AdaptiveDenormalization):
+    def __init__(self, num_features: int, condition_in_channels, base_norm_type="BN", padding_mode="zeros"):
+        super().__init__(num_features, base_norm_type)
+        self.beta_conv = nn.Conv2d(condition_in_channels, self.num_features, kernel_size=3, padding=1,
+                                   padding_mode=padding_mode)
+        self.gamma_conv = nn.Conv2d(condition_in_channels, self.num_features, kernel_size=3, padding=1,
+                                    padding_mode=padding_mode)
+
+    def set_feature(self, feature):
+        gamma = self.gamma_conv(feature)
+        beta = self.beta_conv(feature)
+        super().set_condition(gamma, beta)
+
+
+@NORMALIZATION.register_module("SPADE")
+class SpatiallyAdaptiveDenormalization(AdaptiveDenormalization):
+    def __init__(self, num_features: int, condition_in_channels, base_channels=128, base_norm_type="BN",
+                 activation_type="ReLU", padding_mode="zeros"):
+        super().__init__(num_features, base_norm_type)
+        self.base_conv_block = Conv2dBlock(condition_in_channels, num_features, activation_type=activation_type,
+                                           kernel_size=3, padding=1, padding_mode=padding_mode, norm_type="NONE")
+        self.beta_conv = nn.Conv2d(base_channels, num_features, kernel_size=3, padding=1, padding_mode=padding_mode)
+        self.gamma_conv = nn.Conv2d(base_channels, num_features, kernel_size=3, padding=1, padding_mode=padding_mode)
+
+    def set_condition_image(self, condition_image):
+        feature = self.base_conv_block(condition_image)
+        gamma = self.gamma_conv(feature)
+        beta = self.beta_conv(feature)
+        super().set_condition(gamma, beta)
+
+
+def _instance_layer_normalization(x, gamma, beta, rho, eps=1e-5):
+    out = rho * F.instance_norm(x, eps=eps) + (1 - rho) * F.layer_norm(x, x.size()[1:], eps=eps)
+    out = out * gamma + beta
+    return out
+
+
+@NORMALIZATION.register_module("ILN")
+class ILN(nn.Module):
+    def __init__(self, num_features, eps=1e-5):
+        super(ILN, self).__init__()
+        self.eps = eps
+        self.rho = nn.Parameter(torch.Tensor(num_features))
+        self.gamma = nn.Parameter(torch.Tensor(num_features))
+        self.beta = nn.Parameter(torch.Tensor(num_features))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.zeros_(self.rho)
+        nn.init.ones_(self.gamma)
+        nn.init.zeros_(self.beta)
+
+    def forward(self, x):
+        return _instance_layer_normalization(
+            x, self.gamma.expand_as(x), self.beta.expand_as(x), self.rho.expand_as(x), self.eps)
+
+
+@NORMALIZATION.register_module("AdaILN")
+class AdaILN(nn.Module):
+    def __init__(self, num_features, eps=1e-5, default_rho=0.9):
+        super(AdaILN, self).__init__()
+        self.eps = eps
+        self.rho = nn.Parameter(torch.Tensor(num_features))
+        self.rho.data.fill_(default_rho)
+
+        self.gamma = None
+        self.beta = None
+        self.have_set_condition = False
+
+    def set_condition(self, gamma, beta):
+        self.gamma, self.beta = gamma, beta
+        self.have_set_condition = True
+
+    def forward(self, x):
+        assert self.have_set_condition
+        out = _instance_layer_normalization(
+            x, self.gamma.expand_as(x), self.beta.expand_as(x), self.rho.expand_as(x), self.eps)
+        self.have_set_condition = False
+        return out