layers -> xlayers

lib/xlayers/super_mlp.py

@@ -0,0 +1,98 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import torch
+import torch.nn as nn
+
+import math
+from typing import Optional, Union
+
+import spaces
+from .super_module import SuperModule
+from .super_module import SuperRunMode
+
+IntSpaceType = Union[int, spaces.Integer, spaces.Categorical]
+BoolSpaceType = Union[bool, spaces.Categorical]
+
+
+class SuperLinear(SuperModule):
+    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
+
+    def __init__(
+        self,
+        in_features: IntSpaceType,
+        out_features: IntSpaceType,
+        bias: BoolSpaceType = True,
+    ) -> None:
+        super(SuperLinear, self).__init__()
+
+        # the raw input args
+        self._in_features = in_features
+        self._out_features = out_features
+        self._bias = bias
+
+        self._super_weight = torch.nn.Parameter(
+            torch.Tensor(self.out_features, self.in_features)
+        )
+        if self.bias:
+            self._super_bias = torch.nn.Parameter(torch.Tensor(self.out_features))
+        else:
+            self.register_parameter("_super_bias", None)
+        self.reset_parameters()
+
+    @property
+    def in_features(self):
+        return spaces.get_max(self._in_features)
+
+    @property
+    def out_features(self):
+        return spaces.get_max(self._out_features)
+
+    @property
+    def bias(self):
+        return spaces.has_categorical(self._bias, True)
+
+    def abstract_search_space(self):
+        print('-')
+
+    def reset_parameters(self) -> None:
+        nn.init.kaiming_uniform_(self._super_weight, a=math.sqrt(5))
+        if self.bias:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._super_weight)
+            bound = 1 / math.sqrt(fan_in)
+            nn.init.uniform_(self._super_bias, -bound, bound)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.linear(input, self._super_weight, self._super_bias)
+
+    def extra_repr(self) -> str:
+        return "in_features={:}, out_features={:}, bias={:}".format(
+            self.in_features, self.out_features, self.bias
+        )
+
+
+class SuperMLP(nn.Module):
+    # MLP: FC -> Activation -> Drop -> FC -> Drop
+    def __init__(
+        self,
+        in_features,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer=nn.GELU,
+        drop: Optional[float] = None,
+    ):
+        super(MLP, self).__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop or 0)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x