from e2cnn.gspaces import *
from e2cnn.nn import FieldType
from e2cnn.nn import GeometricTensor
from ..equivariant_module import EquivariantModule
import torch
import torch.nn.functional as F
from typing import List, Tuple, Any
import numpy as np
__all__ = ["ReLU"]
[docs]class ReLU(EquivariantModule):
def __init__(self, in_type: FieldType, inplace: bool = False):
r"""
Module that implements a pointwise ReLU to every channel independently.
The input representation is preserved by this operation and, therefore, it equals the output
representation.
Only representations supporting pointwise non-linearities are accepted as input field type.
Args:
in_type (FieldType): the input field type
inplace (bool, optional): can optionally do the operation in-place. Default: ``False``
"""
assert isinstance(in_type.gspace, GeneralOnR2)
super(ReLU, self).__init__()
for r in in_type.representations:
assert 'pointwise' in r.supported_nonlinearities, \
'Error! Representation "{}" does not support "pointwise" non-linearity'.format(r.name)
self.space = in_type.gspace
self.in_type = in_type
# the representation in input is preserved
self.out_type = in_type
self._inplace = inplace
[docs] def forward(self, input: GeometricTensor) -> GeometricTensor:
r"""
Applies ReLU function on the input fields
Args:
input (GeometricTensor): the input feature map
Returns:
the resulting feature map after relu has been applied
"""
assert input.type == self.in_type, "Error! the type of the input does not match the input type of this module"
return GeometricTensor(F.relu(input.tensor, inplace=self._inplace), self.out_type)
def evaluate_output_shape(self, input_shape: Tuple[int, int, int, int]) -> Tuple[int, int, int, int]:
assert len(input_shape) == 4
assert input_shape[1] == self.in_type.size
b, c, hi, wi = input_shape
return b, self.out_type.size, hi, wi
def check_equivariance(self, atol: float = 1e-6, rtol: float = 1e-5) -> List[Tuple[Any, float]]:
c = self.in_type.size
x = torch.randn(3, c, 10, 10)
x = GeometricTensor(x, self.in_type)
errors = []
for el in self.space.testing_elements:
out1 = self(x).transform_fibers(el)
out2 = self(x.transform_fibers(el))
errs = (out1.tensor - out2.tensor).detach().numpy()
errs = np.abs(errs).reshape(-1)
print(el, errs.max(), errs.mean(), errs.var())
assert torch.allclose(out1.tensor, out2.tensor, atol=atol, rtol=rtol), \
'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}' \
.format(el, errs.max(), errs.mean(), errs.var())
errors.append((el, errs.mean()))
return errors
def extra_repr(self):
return 'inplace={}, type={}'.format(
self._inplace, self.in_type
)
[docs] def export(self):
r"""
Export this module to a normal PyTorch :class:`torch.nn.ReLU` module and set to "eval" mode.
"""
self.eval()
return torch.nn.ReLU(inplace=self._inplace)