Update misc
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@ -26,3 +26,5 @@ class TestSuperReArrange(unittest.TestCase):
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tensor = torch.rand((8, 4, 32, 32))
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tensor = torch.rand((8, 4, 32, 32))
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print("The tensor shape: {:}".format(tensor.shape))
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print("The tensor shape: {:}".format(tensor.shape))
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print(layer)
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print(layer)
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outs = layer(tensor)
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print("The output tensor shape: {:}".format(outs.shape))
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145
xautodl/xlayers/misc_utils.py
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145
xautodl/xlayers/misc_utils.py
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@ -0,0 +1,145 @@
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# borrowed from https://github.com/arogozhnikov/einops/blob/master/einops/parsing.py
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class ParsedExpression:
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"""
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non-mutable structure that contains information about one side of expression (e.g. 'b c (h w)')
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and keeps some information important for downstream
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"""
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def __init__(self, expression):
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self.has_ellipsis = False
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self.has_ellipsis_parenthesized = None
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self.identifiers = set()
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# that's axes like 2, 3 or 5. Axes with size 1 are exceptional and replaced with empty composition
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self.has_non_unitary_anonymous_axes = False
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# composition keeps structure of composite axes, see how different corner cases are handled in tests
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self.composition = []
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if "." in expression:
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if "..." not in expression:
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raise ValueError(
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"Expression may contain dots only inside ellipsis (...)"
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)
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if str.count(expression, "...") != 1 or str.count(expression, ".") != 3:
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raise ValueError(
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"Expression may contain dots only inside ellipsis (...); only one ellipsis for tensor "
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)
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expression = expression.replace("...", _ellipsis)
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self.has_ellipsis = True
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bracket_group = None
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def add_axis_name(x):
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if x is not None:
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if x in self.identifiers:
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raise ValueError(
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'Indexing expression contains duplicate dimension "{}"'.format(
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x
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)
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)
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if x == _ellipsis:
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self.identifiers.add(_ellipsis)
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if bracket_group is None:
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self.composition.append(_ellipsis)
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self.has_ellipsis_parenthesized = False
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else:
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bracket_group.append(_ellipsis)
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self.has_ellipsis_parenthesized = True
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else:
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is_number = str.isdecimal(x)
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if is_number and int(x) == 1:
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# handling the case of anonymous axis of length 1
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if bracket_group is None:
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self.composition.append([])
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else:
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pass # no need to think about 1s inside parenthesis
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return
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is_axis_name, reason = self.check_axis_name(x, return_reason=True)
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if not (is_number or is_axis_name):
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raise ValueError(
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"Invalid axis identifier: {}\n{}".format(x, reason)
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)
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if is_number:
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x = AnonymousAxis(x)
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self.identifiers.add(x)
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if is_number:
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self.has_non_unitary_anonymous_axes = True
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if bracket_group is None:
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self.composition.append([x])
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else:
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bracket_group.append(x)
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current_identifier = None
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for char in expression:
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if char in "() ":
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add_axis_name(current_identifier)
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current_identifier = None
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if char == "(":
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if bracket_group is not None:
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raise ValueError(
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"Axis composition is one-level (brackets inside brackets not allowed)"
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)
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bracket_group = []
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elif char == ")":
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if bracket_group is None:
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raise ValueError("Brackets are not balanced")
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self.composition.append(bracket_group)
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bracket_group = None
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elif str.isalnum(char) or char in ["_", _ellipsis]:
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if current_identifier is None:
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current_identifier = char
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else:
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current_identifier += char
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else:
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raise ValueError("Unknown character '{}'".format(char))
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if bracket_group is not None:
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raise ValueError(
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'Imbalanced parentheses in expression: "{}"'.format(expression)
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)
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add_axis_name(current_identifier)
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def flat_axes_order(self) -> List:
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result = []
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for composed_axis in self.composition:
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assert isinstance(composed_axis, list), "does not work with ellipsis"
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for axis in composed_axis:
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result.append(axis)
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return result
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def has_composed_axes(self) -> bool:
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# this will ignore 1 inside brackets
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for axes in self.composition:
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if isinstance(axes, list) and len(axes) > 1:
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return True
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return False
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@staticmethod
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def check_axis_name(name: str, return_reason=False):
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"""
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Valid axes names are python identifiers except keywords,
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and additionally should not start or end with underscore
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"""
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if not str.isidentifier(name):
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result = False, "not a valid python identifier"
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elif name[0] == "_" or name[-1] == "_":
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result = False, "axis name should should not start or end with underscore"
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else:
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if keyword.iskeyword(name):
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warnings.warn(
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"It is discouraged to use axes names that are keywords: {}".format(
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name
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),
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RuntimeWarning,
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)
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if name in ["axis"]:
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warnings.warn(
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"It is discouraged to use 'axis' as an axis name "
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"and will raise an error in future",
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FutureWarning,
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)
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result = True, None
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if return_reason:
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return result
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else:
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return result[0]
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@ -11,6 +11,7 @@ import math
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from typing import Optional, Callable
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from typing import Optional, Callable
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from xautodl import spaces
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from xautodl import spaces
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from .misc_utils import ParsedExpression
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from .super_module import SuperModule
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from .super_module import SuperModule
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from .super_module import IntSpaceType
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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from .super_module import BoolSpaceType
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@ -24,6 +25,17 @@ class SuperReArrange(SuperModule):
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self._pattern = pattern
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self._pattern = pattern
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self._axes_lengths = axes_lengths
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self._axes_lengths = axes_lengths
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axes_lengths = tuple(sorted(self._axes_lengths.items()))
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# Perform initial parsing of pattern and provided supplementary info
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# axes_lengths is a tuple of tuples (axis_name, axis_length)
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left, right = pattern.split("->")
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left = ParsedExpression(left)
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right = ParsedExpression(right)
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import pdb
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pdb.set_trace()
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print("-")
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@property
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@property
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def abstract_search_space(self):
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def abstract_search_space(self):
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@ -31,13 +43,16 @@ class SuperReArrange(SuperModule):
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return root_node
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return root_node
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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import pdb
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pdb.set_trace()
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raise NotImplementedError
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raise NotImplementedError
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def extra_repr(self) -> str:
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def extra_repr(self) -> str:
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params = repr(self._pattern)
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params = repr(self._pattern)
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for axis, length in self._axes_lengths.items():
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for axis, length in self._axes_lengths.items():
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params += ", {}={}".format(axis, length)
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params += ", {}={}".format(axis, length)
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return "{}({})".format(self.__class__.__name__, params)
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return "{:}".format(params)
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