Move to xautodl

xautodl/xlayers/super_dropout.py

@@ -0,0 +1,83 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import math
+from typing import Optional, Callable, Tuple
+
+import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+
+
+class SuperDropout(SuperModule):
+    """Applies a the dropout function element-wise."""
+
+    def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
+        super(SuperDropout, self).__init__()
+        self._p = p
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.dropout(input, self._p, self.training, self._inplace)
+
+    def forward_with_container(self, input, container, prefix=[]):
+        return self.forward_raw(input)
+
+    def extra_repr(self) -> str:
+        xstr = "inplace=True" if self._inplace else ""
+        return "p={:}".format(self._p) + ", " + xstr
+
+
+class SuperDrop(SuperModule):
+    """Applies a the drop-path function element-wise."""
+
+    def __init__(self, p: float, dims: Tuple[int], recover: bool = True) -> None:
+        super(SuperDrop, self).__init__()
+        self._p = p
+        self._dims = dims
+        self._recover = recover
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        if not self.training or self._p <= 0:
+            return input
+        keep_prob = 1 - self._p
+        shape = [input.shape[0]] + [
+            x if y == -1 else y for x, y in zip(input.shape[1:], self._dims)
+        ]
+        random_tensor = keep_prob + torch.rand(
+            shape, dtype=input.dtype, device=input.device
+        )
+        random_tensor.floor_()  # binarize
+        if self._recover:
+            return input.div(keep_prob) * random_tensor
+        else:
+            return input * random_tensor  # as masks
+
+    def forward_with_container(self, input, container, prefix=[]):
+        return self.forward_raw(input)
+
+    def extra_repr(self) -> str:
+        return (
+            "p={:}".format(self._p)
+            + ", dims={:}".format(self._dims)
+            + ", recover={:}".format(self._recover)
+        )