# Copyright 2022 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Original code from https://github.com/Atze00/MoViNet-pytorch
#
# Modified by Zilliz.
import torch
from torch import Tensor
from towhee.models.utils.causal_module import CausalModule
[docs]class TemporalCGAvgPool3D(CausalModule):
"""
TemporalCGAvgPool3D
"""
[docs] def __init__(self,) -> None:
super().__init__()
self.n_cumulated_values = 0
self.register_forward_hook(self._detach_activation)
[docs] def forward(self, x: Tensor) -> Tensor:
input_shape = x.shape
device = x.device
cumulative_sum = torch.cumsum(x, dim=2)
if self.activation is None:
self.activation = cumulative_sum[:, :, -1:].clone()
else:
cumulative_sum += self.activation
self.activation = cumulative_sum[:, :, -1:].clone()
divisor = (torch.arange(1, input_shape[2]+1,
device=device)[None, None, :, None, None]
.expand(x.shape))
x = cumulative_sum / (self.n_cumulated_values + divisor)
self.n_cumulated_values += input_shape[2]
return x
@staticmethod
# pylint: disable=W0613
def _detach_activation(module: CausalModule, input_tensor: Tensor, output: Tensor) -> None:
module.activation.detach_()
def reset_activation(self) -> None:
super().reset_activation()
self.n_cumulated_values = 0