base model, Norm&Conv&ResNet

model/base/module.py

@@ -0,0 +1,109 @@
+import torch.nn as nn
+
+from model.registry import NORMALIZATION
+
+_DO_NO_THING_FUNC = lambda x: x
+
+
+def _use_bias_checker(norm_type):
+    return norm_type not in ["IN", "BN", "AdaIN", "FADE", "SPADE"]
+
+
+def _normalization(norm, num_features, additional_kwargs=None):
+    if norm == "NONE":
+        return _DO_NO_THING_FUNC
+
+    if additional_kwargs is None:
+        additional_kwargs = {}
+    kwargs = dict(_type=norm, num_features=num_features)
+    kwargs.update(additional_kwargs)
+    return NORMALIZATION.build_with(kwargs)
+
+
+def _activation(activation):
+    if activation == "NONE":
+        return _DO_NO_THING_FUNC
+    elif activation == "ReLU":
+        return nn.ReLU(inplace=True)
+    elif activation == "LeakyReLU":
+        return nn.LeakyReLU(negative_slope=0.2, inplace=True)
+    elif activation == "Tanh":
+        return nn.Tanh()
+    else:
+        raise NotImplemented(activation)
+
+
+class Conv2dBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, bias=None,
+                 activation_type="ReLU", norm_type="NONE", **conv_kwargs):
+        super().__init__()
+        self.norm_type = norm_type
+        self.activation_type = activation_type
+
+        # if caller not set bias, set bias automatically.
+        conv_kwargs["bias"] = _use_bias_checker(norm_type) if bias is None else bias
+
+        self.convolution = nn.Conv2d(in_channels, out_channels, **conv_kwargs)
+        self.normalization = _normalization(norm_type, out_channels)
+        self.activation = _activation(activation_type)
+
+    def forward(self, x):
+        return self.activation(self.normalization(self.convolution(x)))
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, num_channels, out_channels=None, padding_mode='reflect',
+                 activation_type="ReLU", out_activation_type=None, norm_type="IN"):
+        super().__init__()
+        self.norm_type = norm_type
+
+        if out_channels is None:
+            out_channels = num_channels
+        if out_activation_type is None:
+            out_activation_type = "NONE"
+
+        self.learn_skip_connection = num_channels != out_channels
+
+        self.conv1 = Conv2dBlock(num_channels, num_channels, kernel_size=3, padding=1, padding_mode=padding_mode,
+                                 norm_type=norm_type, activation_type=activation_type)
+        self.conv2 = Conv2dBlock(num_channels, out_channels, kernel_size=3, padding=1, padding_mode=padding_mode,
+                                 norm_type=norm_type, activation_type=out_activation_type)
+
+        if self.learn_skip_connection:
+            self.res_conv = Conv2dBlock(num_channels, out_channels, kernel_size=3, padding=1, padding_mode=padding_mode,
+                                        norm_type=norm_type, activation_type=out_activation_type)
+
+    def forward(self, x):
+        res = x if not self.learn_skip_connection else self.res_conv(x)
+        return self.conv2(self.conv1(x)) + res
+
+
+class ReverseConv2dBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int,
+                 activation_type="ReLU", norm_type="NONE", additional_norm_kwargs=None, **conv_kwargs):
+        super().__init__()
+        self.normalization = _normalization(norm_type, in_channels, additional_norm_kwargs)
+        self.activation = _activation(activation_type)
+        self.convolution = nn.Conv2d(in_channels, out_channels, **conv_kwargs)
+
+    def forward(self, x):
+        return self.convolution(self.activation(self.normalization(x)))
+
+
+class ReverseResidualBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, padding_mode="reflect",
+                 norm_type="IN", additional_norm_kwargs=None, activation_type="ReLU"):
+        super().__init__()
+        self.learn_skip_connection = in_channels != out_channels
+        self.conv1 = ReverseConv2dBlock(in_channels, in_channels, activation_type, norm_type, additional_norm_kwargs,
+                                        kernel_size=3, padding=1, padding_mode=padding_mode)
+        self.conv2 = ReverseConv2dBlock(in_channels, out_channels, activation_type, norm_type, additional_norm_kwargs,
+                                        kernel_size=3, padding=1, padding_mode=padding_mode)
+        if self.learn_skip_connection:
+            self.res_conv = ReverseConv2dBlock(
+                in_channels, out_channels, activation_type, norm_type, additional_norm_kwargs,
+                kernel_size=3, padding=1, padding_mode=padding_mode)
+
+    def forward(self, x):
+        res = x if not self.learn_skip_connection else self.res_conv(x)
+        return self.conv2(self.conv1(x)) + res